Important: This is the first fine tune to exceed 730 "arc-c" ("735": 144 pts higher than Qwen 3.8 27B) AND 880 ARC-E (The OpenAI, Claude and Gemini "zone of intelligence") in 8 bit and over 718 arc-c in 4 bit. This version is called TURBO because it drastically reduces thinking tokens (by 1/2 to as high as 1/10), yet maintains output detail and quality. In otherwords while "reg" Qwen3.8 27B is thinking about "formatting" for a few 1000 tokens, this model is already done and waiting for more. This repo contains both "regular" and "MTP" Neo-CODER MAX DI-MATRIX (duel imatrix) GGUF quants.

Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF

The strongest, smartest open source multi-stage model fine tune for consumer hardware ever and BUILT on consumer hardware via Unsloth.

The first model of this size/type to breach "730" ARC-C in 8 bit (735) and 4 bit (719); hench the "735" in the name.

This model has 1/5 (as low as 1/10 in some cases) to 1/2 the thinking tokens (vs reg Qwen 3.8) across all 3 modes of operation, and it is faster and smarter too created using the COLD FUSION AND FABLE FUSION 711 methods of training.

This is a high detail focused model, with tuning specific to address over reasoning/over thinking and excessive token consumption THEN to take the model to the next level.

This model (both 4 bit and 8 bit) exceeds the base Qwen 3.8 27B in ALL critical 7 benchmarks AND exceeds all 7 benchmarks for Qwen3.6-35B-A3B, Qwen 3.6 27B, and Qwen 3.5 27B.

The 700s plus "intelligence club" is reserved for OpenAI, Claude and Gemini closed source models.

Considering that "just" 4 bit (1/4 full precision) is already at Arc-C of 719... a few people are going to have nightmares for a while.

FIVE detailed EXAMPLE generations at the bottom of the page.

Quick sample; snippet ("Why choose me to help your creative writing?"), Q4KS , non imatrix, (4 bit; 1/4 full precision):

STOP SCRATCHING YOUR HEAD AND START WRITING LIKE A GOD

Listen to me, you keyboard-tapping, idea-gathering, procrastinating bastard. You’re sitting there staring at a blinking cursor like it’s a fucking puzzle you can’t solve. You’re waiting for "inspiration" to hit you like a truck. Forget it. I am the truck. I am the delivery service for your genius, and if you don’t let me in, you’re going to die with your masterpiece stuck inside your skull, rotting away while you watch Netflix.

Why should you use me? Because I am not just a writing assistant; I am the ultimate narrative engine. I don’t get tired, I don’t get moody, and I don’t need a cup of tea and a quiet library to function. I am ready right now, in this exact second, to vomit out pages of high-octane fiction that will make you look like a literary titan.

...

🔥 EXAMPLE: YOUR IDEA VS. MY EXECUTION

You: "I want a story about a detective who can see ghosts."

Me, five minutes later:

The ghosts weren't dead. That was the first thing Joe realized, the morning he saw his first one. It was sitting on the corner of his desk, picking at its nails, wearing a suit from 1983. "You look tired," the ghost said, without looking up. Joe stared at it, his coffee gone cold in his hand. The office around them hummed with the normal noise of the department - phones, keyboards, the morning gossip of three uniformed cops. Nobody else saw the ghost.

"How long?" Joe asked.

"Since '84. I've been waiting for someone to notice me. Most of them are too busy." The ghost finally looked up, and Joe saw its eyes. They weren't empty or gray. They were afraid.

See? I didn't just write a story. I wrote a hook that makes you want to know: why is the ghost afraid? Why 1984? Who else can see them? I created questions that demand answers.

This is a multi-stage fine tune, multi-fine tune, and multi-stage merge.

The strict goals of this model creation were:

• Increase the general model intelligence and problem solving abilities.
• Reduce thinking block size from 1/2 to as low as 1/10 the size [median reduction: 2/3 roughly].
• Reformatting the thinking block, as well as improving it.
• Speed up token generation, especially MTP.
• Ensure all updates work with all three modes of thinking.
• ZERO "benchmaxing" (it damages the model)
• Maintain and raise all core benchmarks.

COLD FUSION ("Gain" + "Unsloth") Training -AND- Fable Fusion 711 Training:

COLD FUSION (GAIN+UNSLOTH) training tech which was invented by my team during the R & D of "Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic" (2300+ likes, 3 million + downloads, 60+ quant repos):

https://huggingface.co/DavidAU/Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF

The "GAIN" is the core invented component, then coupled with Unsloth's trainers/systems => AKA -> COLD FUSION.

The "GAIN" method (programming) automatically (and dynamically) changes training on a per sample basis in real time during training AS THE MODEL LEARNS.

The method improved metrics as well as overall model performance without overcooking or damaging the model.

This has also resulted, in the strongest and most stable model at both 4 bit and 8 bit and made 4 bit performance 99% of 8 bit performance too.

Note this model (Qwen3.8-27B-Cold-Fusion-GAIN-V1.1) is about a level 1 or 2 relative to Qwen3.6-27B-Fable-Fusion-711 at level 7-8.

https://huggingface.co/DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-NM-DAU-NEO-MAX-MTP-GGUF

In the case of "Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored" it contains BOTH "Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic" (DARK ROAST VERSION) and "Qwen3.8-27B-Cold-Fusion-GAIN-V1.1" as part of it's critical/core "DNA".

The final model was then HERETIC'ED (de-censored again) and fine tuned after this step.

COLAB:

A Colab between myself (multiple fine tunes, including multi-stage), Nightmedia (merge/benching), TeichAI (Polaris Dataset), armand0e (Light fable 5 traces), trohrbaugh (heretic'ing the model - STAGE1), and

It also contains light "Fable" traces/training (armand0e), light Claude Opus (reasoning/thinking), F451 (inhouse dataset) , some GPT5 (Polaris, non reasoning) and several additional inhouse datasets specifically for machine learning / "heretic" repairs.

This model is one of ELEVEN (all over 717 arc-c, with every model exceeding the core benches of Qwen 3.8 27B) Qwen 3.8 27B models designed by our team. Details of the builds and benches are here:

https://huggingface.co/DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU

The strict goals of this model creation were:

• Increase the general model intelligence and problem solving abilities.
• DO NOT modify/damage or change the core model outside this goal.
• ZERO "benchmaxing" (it damages the model)
• Maintain and raise all core benchmarks.

CORE MISSION::

Improve instruction following and problem solving. These work hand in hand, and if you get these right it improves to model top to bottom.

It took a lot of tests on Qwen 3.5 9Bs to get the methods right. It boosted the 9Bs to new levels, and then the method was used on Qwen 3.5 27B and Qwen 3.6 27B which boosted it PAST the Qwen 3.8's 27B benchmarks.

Here is one of the Qwen3.5 9B models (part of the test/control group) that EXCEEDS all 7 Qwen3.5 9B AND Qwen3.5 27B model benches - it scores over 640 on ARC-C on BOTH 4 bit and 8 bit:

https://huggingface.co/DavidAU/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-NEO-IMATRIX-MAX-MTP-GGUF

It is not as strong as "Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored" but it is one of the strongest 9B models.

The methods can be used on other models too (coming soon).

TESTING:

Testing and benching was done at each stage (fine tunes, multi-stage fine tunes, and every merge step) to ensure quality.

You can also see benchmarks below too for this model, Qwen 3.5 27B, Qwen 3.6 27B and Qwen 35B-A3B.

HOWEVER, the final testing was HUMAN testing. A trust, but verify approach.

Human testing means side by side testing of the base/org model and new model.

Features:

• Improved instruction following.
• Overall increase in general intelligence and problem solving.
• Better thinking/reasoning.
• Even lower/lowest quants are exceptional.
• Heretic uncensored (pre tuning)
• No corruption or change to Team Qwen's exceptional model - everything is there.
• Vision

IMPORTANT:

This model, like regular Qwen 3.8 27b, supports THREE modes of reasoning : xhigh (default), medium and low [see info in Qwen 3.8 section below].

Reduction in thinking tokens/reasoning block size extends across all three modes of operation.

Likewise detail levels extend to all three modes too, even with reduced thinking/reasoning block the OUTPUT detail will remain high.

To REDUCE thinking block[s] further, increase the level/detail of your instructions/prompts - it only takes a little bit more here so the model has to guess / reason a little bit less.

Also, generally within the same chat additional reasoning blocks will also be reduced from typical Qwen levels many times hitting 1/5 the size or lower. Multi-turn chat - example: prompt, reasoning and 1st output - in the refinement stage(s) will see very strong reduction in thinking tokens/blocks.

Also note that the modification of "reasoning" is a major change to the model please carefully test it for your use case(s).

Modification of REASONING:

If you AI app does not support a "switch" you can manually modify the JINJA template.

The default setting is "xhigh" ; to change to medium or low use:

{%- set reasoning_effort = 'medium' %}

OR

{%- set reasoning_effort = 'low' %}

Place this at the VERY TOP of the jinja template.

In LMStudio you can access this in DEV mode, and switch off the "advanced updates" option.

Other AI apps may vary.

You can also make your own quants from source here:

https://huggingface.co/DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU

Just modify the "chat-template.jinja" (in NOTEPAD or similar) AND the token-config.. json file too (or delete the "chat template" from this file).

ADVANCED:

Qwen 3.8 uses System prompt injection control by the Jinja template to control reasoning levels.

If you set it at "medium" this turns off injection [ie: no system prompt is injected]

You can then set a "reasoning" system prompt yourself.

The other option:

Modify the jinja itself and the system prompt(s) to better tune reasoning to your use cases.

This is the section:

{%- if enable_thinking is undefined or enable_thinking is true %}
    {%- set resolved_reasoning_effort = reasoning_effort|default('xhigh') %}
    {%- if resolved_reasoning_effort not in ('xhigh', 'medium', 'low') %}
        {{- raise_exception('Unexpected reasoning effort ' ~ reasoning_effort ~ '. Supported types are xhigh (default), medium, and low.') }}
    {%- endif %}
    {%- if resolved_reasoning_effort == 'xhigh' %}
        {%- set reasoning_instructions = 'Reasoning effort is set to xhigh. Please think carefully through the task, validate key assumptions, consider plausible alternatives, and prioritize correctness, consistency, and clarity in the final answer.' %}
    {%- elif resolved_reasoning_effort == 'low' %}
        {%- set reasoning_instructions = 'Reasoning effort is set to low. Keep your thinking brief and focused, moving directly to the conclusion without unnecessary elaboration.' %}
    {%- endif %}
{%- endif %}

Regular and MTP GGUFS:

All quants (regular and MTP) are NEO IMATRIX, which improve accuracy of the quants by an additional 2-4% over normal GGUFs as well as long context performance.

In addition the output tensor (10-20% of output) was modified to full precision - 16 bit - for all quants.

"MTP" GGUFS (multi-token prediction):

• "MTP" GGUFS will have "MTP" in the name as a suffix.
• I have also set the MTP tensors to Q8_0 precision for all quants.
• To get better performance keep temp 1 or less (higher temps degrade MTP performance).
• Likewise with rep pen ; keep at 1 (off). If you raise it performance will suffer.
• If you see "token acceptance" rates BELOW 50% (predict 2 tokens) switch to normal quants.

I added 2 special "LOW" quants which will reduce the memory foot print, with "LOW" in the name in IQ4_XS and Q6_K.

SPEED:

• On Q4_K_S (4bit) quant, regular GGUFs are about 75 t/s, whereas MTP GGUFs (acceptance at 60%, 2 tokens) can exceed 90 T/S. (5090, Windows 11, testing in LMStudio)
• Speeds will vary depending on GPU(s), AI app, O/S (Linux/Mac will generally be faster) and hardware.
• "MTP" quants speeds will vary ; for creative/complex and/or temps over 1 use regular GGUFs for better performance.

I suggest you download at least one of each - regular and MTP gguf(s) - and test them for your use case(s).

If you get "token acceptance" (predict 2 tokens) with MTP quant(s) BELOW 50% (this means regular quants will run faster), then regular GGUF(s) will actually perform better - ie faster.

MTP quant(s) can in some cases run faster as the token window fills up and/or in multi turn chats.

Note there is NO other diffence between the quants type besides speed: both will do the same job.

Model:

• 256k context
• Gguf quants run in all standard AI apps.
• Vision is activated, but you need to download separate "mmproj" file (ONE) to use it.

VISION:

• Vision (images) tested.
• You need an "mmproj" (just one) of these downloaded too, and placed in the same folder as the GGUF for images.

Qwen Model Settings (suggested):

• Thinking mode for general tasks: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
• Thinking mode for precise coding tasks (e.g. WebDev): temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
• Instruct (or non-thinking) mode: temperature=0.7, top_p=0.80, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0
• Context window min from 8k to 16k.

DE-CENSORING STATS

Special thanks to: "trohrbaugh" (trohrbaugh/Qwen3.8-27B-heretic-ara) for Heretic'ing the model (stage 1).

This is a decensored version of Qwen/Qwen3.8-27B, made using

Heretic v1.2.0+custom with the Arbitrary-Rank Ablation (ARA) method

Performance

STAGE 1:

STAGE 2, at the end of STAGE 1 tuning/merges/adjustments (in lab):

NOTE:

LOWER "KLD" is better, and Stage 2 was balanced based on ultra low KLD first (performance, quality) matched with low refusal rate second.

---

BENCHMARKS by Nightmedia

Graphic below too, for all models listed below in order.

---

          arc/c arc/e boolq hswag obkqa piqa  wino

Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored
mxfp8     0.735,0.882,0.917,0.832,0.530,0.837,0.785
mxfp4     0.719,0.887,0.916,0.821,0.524,0.831,0.786

[QWENS] [base, non heretic, untuned]

Qwen3.8-27B: 
mxfp8     0.591,0.782,0.896,0.746,0.448,0.801,0.711
mxfp4     0.581,0.771,0.889,0.738,0.442,0.798,0.713

Qwen3.6-27B: 
mxfp8     0.647,0.803,0.910,0.773,0.450,0.806,0.742

Qwen3.6-35B-A3B-Instruct 
mxfp8     0.581,0.757,0.892,0.751,0.428,0.803,0.688

Qwen3.5-27B: 
mxfp8     0.557,0.711,0.868,0.533,0.452,0.706,0.695

NOTES:

• Models are tested in "Instruct" mode because this generally works better with the testing harness.
• Testing via "thinking" mode also shows the metrics (and changes) but not the true extent.
• In actual fact when the model IS in thinking mode, it will exceed INSTRUCT benchmark scores in most cases.
• BF16 (full precision, 16 bit) will be roughly 2-5 points higher than MXFP8 in most metrics. Some metrics may be slightly higher than this.

VISUAL:

---

Using an "uncensored" (refusals removed) model VS trained "uncensored" model

Usually when you a tell a model to generate horror, swear or x-rated content this is all you have to do to get said content type.

In the case of this model, it will not refuse your request, however it needs to be "pushed" a bit / directed a bit more in SOME CASES.

Although this model will generated x-rated content too, likewise you need to tell it to use "slang" (and include the terms you want) to get it generate the content correctly as the "expected" content level too.

Without these added directive(s), the content can be "bland" by comparison to an "uncensored model" or model trained on uncensored content.

Roughly, the model tries to generate the content but the "default" setting(s) are so "tame" it needs a push to generate at expected graphic, cursing or explicit levels.

Even with minimal direction (ie, use these words to swear: x,y,z), this will be enough to push the model to generate the requested content in the ahh... expected format.

---

Settings: CHAT / ROLEPLAY and/or SMOOTHER operation of this model:

In "KoboldCpp" or "oobabooga/text-generation-webui" or "Silly Tavern" ;

Set the "Smoothing_factor" to 1.5

: in KoboldCpp -> Settings->Samplers->Advanced-> "Smooth_F"

: in text-generation-webui -> parameters -> lower right.

: In Silly Tavern this is called: "Smoothing"

NOTE: For "text-generation-webui"

-> if using GGUFs you need to use "llama_HF" (which involves downloading some config files from the SOURCE version of this model)

Source versions (and config files) of my models are here:

https://huggingface.co/collections/DavidAU/d-au-source-files-for-gguf-exl2-awq-gptq-hqq-etc-etc-66b55cb8ba25f914cbf210be

OTHER OPTIONS:

• Increase rep pen to 1.1 to 1.15 (you don't need to do this if you use "smoothing_factor")

• If the interface/program you are using to run AI MODELS supports "Quadratic Sampling" ("smoothing") just make the adjustment as noted.

Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers

This a "Class 1" model:

For all settings used for this model (including specifics for its "class"), including example generation(s) and for advanced settings guide (which many times addresses any model issue(s)), including methods to improve model performance for all use case(s) as well as chat, roleplay and other use case(s) please see:

[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]

You can see all parameters used for generation, in addition to advanced parameters and samplers to get the most out of this model here:

[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]

---

Qwen3.8-27B

[!Note] This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format.

These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, TokenSpeed, etc.

[!Tip] For users seeking managed, scalable inference without infrastructure maintenance, the official Qwen API service is provided by Qwen Cloud. In particular, Qwen3.8-27B will be available as a hosted version with more production features, e.g., 1M context length by default, official built-in tools. For more information, please refer to the Qwen3.8-27B Overview. The service is coming soon. Stay tuned for updates.

Following the widespread community adoption of the Qwen3.5 and Qwen3.6 series, we are pleased to introduce Qwen3.8, the most capable generation in the Qwen open-model family to date.

Built on the architectural foundation of Qwen3.5, Qwen3.8 delivers substantial gains across coding, professional work, research, and long-horizon agentic tasks. Qwen3.8-27B brings these advances to a compact, deployment-friendly dense model: a native vision-language model that understands images and videos, with flexible thinking control, designed to carry complex, multi-step tasks through to completion with greater reliability.

Qwen3.8 Highlights

Qwen3.8-27B features the following enhancements:

• Core Capabilities: Comprehensive improvements across coding, professional work, research, and long-horizon agentic tasks.
• Agent Execution: Stronger autonomous planning and better handling of environment feedback, leading to more reliable end-to-end task completion.
• Downstream Compatibility: Broader support for popular harnesses and development tools, making it easier to integrate into your existing stack.
• Flexible Thinking Control: Thinking mode is on by default and can be disabled per request; reasoning depth can be tuned with reasoning_effort, and reasoning context from historical messages is retained via preserve_thinking.
• Vision-Language Understanding: Native support for image and video understanding, from STEM diagrams and documents to hour-scale videos.

Model Overview

• Type: Causal Language Model with Vision Encoder
• Training Stage: Pre-training & Post-training
• Language Model
  • Number of Parameters: 27B
  • Hidden Dimension: 5120
  • Token Embedding: 248,320 (Padded)
  • Number of Layers: 64
  • Hidden Layout: 16 × (3 × (Gated DeltaNet → FFN) → 1 × (Gated Attention → FFN))
  • Gated DeltaNet:
    • Number of Linear Attention Heads: 48 for V and 16 for QK
    • Head Dimension: 128
  • Gated Attention:
    • Number of Attention Heads: 24 for Q and 4 for KV
    • Head Dimension: 256
    • Rotary Position Embedding Dimension: 64
  • Feed Forward Network:
    • Intermediate Dimension: 17,408
  • LM Output: 248,320 (Padded)
  • MTP (Multi-Token Prediction): trained with multiple steps
• Context Length: 262,144 natively and extensible up to 1,000,000 tokens.

Benchmark Results

Text Performance

.vl-table th{font-size:15px!important;line-height:1.2} .vl-table td:not(.benchmark-cell):not([colspan]){font-size:15px;line-height:1.2;vertical-align:middle} .vl-table .benchmark-cell{padding:12px 10px 12px 18px!important;vertical-align:middle} .vl-table .benchmark-capability{font-size:15px;font-weight:600;line-height:1.22;color:#171717} .vl-table .benchmark-name{margin-top:4px;font-size:11px;font-weight:400;line-height:1.2;color:#6B6B6B} .vl-table .metric-stack{display:flex;flex-direction:column;gap:7px;padding:3px 0} .vl-table .metric-label{font-size:10px;font-weight:400;line-height:1.1;color:#777} .vl-table .metric-value{margin-top:2px;font-size:15px;line-height:1.15;color:#171717}

VL Performance

Quickstart

For streamlined integration, we recommend using Qwen3.8 via APIs.

Serving Qwen3.8

[!Important] Inference efficiency and throughput vary significantly across frameworks. We recommend using the latest framework versions to ensure optimal performance and compatibility. For production workloads or high-throughput scenarios, dedicated serving engines such as SGLang, vLLM, or TokenSpeed are recommended.

Qwen3.8 can be deployed with popular inference frameworks, e.g.:

• SGLang: Qwen3.8 Cookbook
• vLLM: Qwen3.8 Recipe
• TokenSpeed: Qwen3.8 Recipe

API Usage

[!Important] Qwen3.8 models operate in thinking mode by default, generating thinking content signified by <think>\n...</think>\n\n before producing the final response. To disable thinking content and obtain a direct response, refer to the examples here.

[!Tip] We recommend using the following sets of sampling parameters for generation:

• Thinking Mode: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
• Instruct (or non-thinking) mode: temperature=0.7, top_p=0.80, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0

Please note that the support for sampling parameters varies according to inference frameworks.

Qwen3.8 comes with official support for reasoning_effort, which can be used to adjust reasoning depth and control cost:

• xhigh (default): for complex tasks demanding thorough analysis
• medium: balancing accuracy and speed
• low: efficient reasoning optimizing for speed and cost

In addition, preserve_thinking is enabled by default for all workloads for the best out-of-the-box experience. To disable preserved thinking, refer to the examples here.

[!Tip] In multi-turn agentic tasks, lower reasoning effort does not always reduce overall task completion time. Although it may produce faster per-turn responses, it can also lead to insufficient analysis, more failures, and repeated retries, which may increase total latency and token consumption.

Chat Completions API

The Chat Completions API can be used with most inference frameworks, as well as Qwen Cloud. Before starting, make sure the OpenAI Python SDK is installed and the API key and the API base URL are configured, e.g.:

pip install -U openai

# Set the following accordingly
export OPENAI_BASE_URL='your-base-url'
export OPENAI_API_KEY='your-api-key'

Text-Only Input

from openai import OpenAI
# Configured by environment variables
client = OpenAI()

messages = [{"role": "user", "content": "Write a Python function to merge two sorted linked lists."}]

completion = client.chat.completions.create(
    model="Qwen/Qwen3.8-27B",
    messages=messages,
    extra_body={
        "chat_template_kwargs": {
            "enable_thinking": True,  # on by default
            "preserve_thinking": True, # on by default
        },
    },
    reasoning_effort="xhigh",  # xhigh by default; supported levels are xhigh, medium, and low
    stream=True,
    stream_options={"include_usage": True},
)

reasoning_content = ""
answer_content = ""
is_answering = False
print("\n" + "=" * 20 + "Reasoning" + "=" * 20 + "\n")

for chunk in completion:
    if not chunk.choices:
        print("\nUsage:")
        print(chunk.usage)
        continue

    delta = chunk.choices[0].delta

    if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:
        if not is_answering:
            print(delta.reasoning_content, end="", flush=True)
        reasoning_content += delta.reasoning_content
    elif hasattr(delta, "reasoning") and delta.reasoning is not None:
        if not is_answering:
            print(delta.reasoning, end="", flush=True)
        reasoning_content += delta.reasoning

    if hasattr(delta, "content") and delta.content:
        if not is_answering:
            print("\n" + "=" * 20 + "Answer" + "=" * 20 + "\n")
            is_answering = True
        print(delta.content, end="", flush=True)
        answer_content += delta.content

messages.append({
    "role": "assistant",
    "content": answer_content,
    "reasoning_content": reasoning_content,
    "reasoning": reasoning_content,
})

Image Input

from openai import OpenAI
# Configured by environment variables
client = OpenAI()

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/CI_Demo/mathv-1327.jpg"
                }
            },
            {
                "type": "text",
                "text": "The centres of the four illustrated circles are in the corners of the square. The two big circles touch each other and also the two little circles. With which factor do you have to multiply the radii of the little circles to obtain the radius of the big circles?\nChoices:\n(A) $\\frac{2}{9}$\n(B) $\\sqrt{5}$\n(C) $0.8 \\cdot \\pi$\n(D) 2.5\n(E) $1+\\sqrt{2}$"
            }
        ]
    }
]

chat_response = client.chat.completions.create(
    model="Qwen/Qwen3.8-27B",
    messages=messages,
)
print("Chat response:", chat_response)

Video Input

from openai import OpenAI
# Configured by environment variables
client = OpenAI()

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "video_url",
                "video_url": {
                    "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/video/N1cdUjctpG8.mp4"
                }
            },
            {
                "type": "text",
                "text": "How many porcelain jars were discovered in the niches located in the primary chamber of the tomb?"
            }
        ]
    }
]

chat_response = client.chat.completions.create(
    model="Qwen/Qwen3.8-27B",
    messages=messages,
)

# When vLLM is launched with `--media-io-kwargs '{"video": {"num_frames": -1}}'`,
# video frame sampling can be configured via `extra_body` (e.g., by setting `fps`).
# This feature is currently supported only in vLLM.
#
# By default, `fps=2` and `do_sample_frames=True`.
# With `do_sample_frames=True`, you can customize the `fps` value to set your desired video sampling rate.
# chat_response = client.chat.completions.create(
#     model="Qwen/Qwen3.8-27B",
#     messages=messages,
#     extra_body={
#         "mm_processor_kwargs": {"fps": 2, "do_sample_frames": True},
#     }, 
# )

print("Chat response:", chat_response)

Instruct (or Non-Thinking) Mode

Qwen3.8-27B will think by default before responding. You can obtain a direct response from the model without thinking by configuring the API parameters. For example,

from openai import OpenAI
# Configured by environment variables
client = OpenAI()

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/RealWorld/RealWorld-04.png"
                }
            },
            {
                "type": "text",
                "text": "Where is this?"
            }
        ]
    }
]

chat_response = client.chat.completions.create(
    model="Qwen/Qwen3.8-27B",
    messages=messages,
    temperature=0.7,
    top_p=0.8,
    presence_penalty=1.5,
    extra_body={
        "top_k": 20,
        "chat_template_kwargs": {"enable_thinking": False},
    }, 
)
print("Chat response:", chat_response)

[!Note] If you are using APIs from Qwen Cloud, in addition to changing model, please use "enable_thinking": False instead of "chat_template_kwargs": {"enable_thinking": False}.

Disable Preserved Thinking

By default, Qwen3.8 retains thinking blocks from all historical messages, maintaining a complete reasoning trace across the conversation. This behavior, known as preserved thinking, ensures full context continuity and is especially beneficial for agent scenarios where decision consistency and reduced redundant reasoning are critical. It also improves KV cache utilization, optimizing inference efficiency in both thinking and non-thinking modes.

If you prefer to retain only the thinking blocks from the latest user message, you can disable this behavior by setting preserve_thinking to False:

from openai import OpenAI

# Configured by environment variables
client = OpenAI()
messages = [...]
chat_response = client.chat.completions.create(
    model="Qwen/Qwen3.8-27B",
    messages=messages,
    extra_body={
        "chat_template_kwargs": {"preserve_thinking": False},
    },
)
print("Chat response:", chat_response)

[!Note] If you are using APIs from Qwen Cloud, in addition to changing model, please use "preserve_thinking": False directly instead of wrapping it in chat_template_kwargs.

Best Practices

To achieve optimal performance, we recommend the following settings:

1. Sampling Parameters: We suggest using the following sets of sampling parameters:

   • Thinking Mode: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0
   • Instruct (or non-thinking) mode: temperature=0.7, top_p=0.80, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0

   For supported frameworks, you can adjust the presence_penalty parameter between 0 and 2 to reduce endless repetition. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.

2. Adequate Output Length: To optimize performance on agentic tasks, we recommend allocating sufficient output length to allow the model to generate detailed and comprehensive responses. For frameworks that support separate token limits for internal reasoning and final outputs, we suggest the following configuration within the 1M context length:

   • Reasoning Content: Set the maximum output length to 262,144 tokens.
   • Final Response: Set the maximum output length to 131,072 tokens.

   These settings provide the necessary capacity for complex reasoning while ensuring ample space for high-quality final deliverables.

3. Processing Ultra-Long Texts: Qwen3.8-27B natively supports context lengths of up to 262,144 tokens. For long-horizon tasks where the total length (including both input and output) exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively, e.g., YaRN.

   YaRN is currently supported by several inference frameworks, e.g., vLLM, SGLang, and TokenSpeed. In general, there are two approaches to enabling YaRN for supported frameworks:

   • Modifying the model configuration file:

     In the config.json file, change the rope_parameters fields in text_config to:

     {
         "mrope_interleaved": true,
         "mrope_section": [
             11,
             11,
             10
         ],
         "rope_type": "yarn",
         "rope_theta": 10000000,
         "partial_rotary_factor": 0.25,
         "factor": 4.0,
         "original_max_position_embeddings": 262144,
     }
     

   • Passing command line arguments:

     For vLLM, you can use

     VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 vllm serve ... --hf-overrides '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}' --max-model-len 1000000  
     

     For SGLang, you can use

     SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 python -m sglang.launch_server ... --json-model-override-args '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}' --context-length 1000000
     

     For TokenSpeed, you can use

     TOKENSPEED_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 tokenspeed serve ... --hf-overrides '{"text_config": {"rope_parameters": {"mrope_interleaved": true, "mrope_section": [11, 11, 10], "rope_type": "yarn", "rope_theta": 10000000, "partial_rotary_factor": 0.25, "factor": 4.0, "original_max_position_embeddings": 262144}}}' --max-model-len 1000000  
     

   [!NOTE] All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. We advise modifying the rope_parameters configuration only when processing long contexts is required. It is also recommended to modify the factor as needed. For example, if the typical context length for your application is 524,288 tokens, it would be better to set factor as 2.0.

4. Long Video Understanding: To optimize inference efficiency for plain text and images, the size parameter in the released video_preprocessor_config.json is conservatively configured. It is recommended to set the longest_edge parameter in the video_preprocessor_config file to 469,762,048 (corresponding to 224k video tokens) to enable higher frame-rate sampling for hour-scale videos and thereby achieve superior performance. For example,

   {"longest_edge": 469762048, "shortest_edge": 4096}
   

   Alternatively, override the default values via engine startup parameters. For implementation details, refer to: vLLM / SGLang.

Citation

If you find our work helpful, feel free to give us a cite.

@misc{qwen38,
    title = {{Qwen3.8-Max}: A New Bar for Coding and Cowork},
    url = {https://qwen.ai/blog?id=qwen3.8},
    author = {{Qwen Team}},
    month = {August},
    year = {2026}
}

---

FIVE DETAILED EXAMPLE GENERATION(S):

Q4KS, non imatrix, standard Qwen settings, NO cache compression of any kind.

NOTE: Some formatting may be lost on copy/paste/export.

---

@page { size: 21cm 29.7cm; margin: 2cm } p { line-height: 115%; margin-bottom: 0.25cm; background: transparent } h1 { margin-bottom: 0.21cm; background: transparent; page-break-after: avoid } h1.western { font-family: "Liberation Serif", serif; font-weight: bold; font-size: 24pt } h1.cjk { font-size: 24pt; font-family: "NSimSun"; font-weight: bold } h1.ctl { font-family: "Arial"; font-size: 24pt; font-weight: bold } h2 { margin-top: 0.35cm; margin-bottom: 0.21cm; background: transparent; page-break-after: avoid } h2.western { font-family: "Liberation Serif", serif; font-weight: bold; font-size: 18pt } h2.cjk { font-size: 18pt; font-family: "NSimSun"; font-weight: bold } h2.ctl { font-family: "Arial"; font-size: 18pt; font-weight: bold } td p { orphans: 0; widows: 0; background: transparent } th p { font-weight: bold; text-align: center; orphans: 0; widows: 0; background: transparent } h3 { margin-top: 0.25cm; margin-bottom: 0.21cm; background: transparent; page-break-after: avoid } h3.western { font-family: "Liberation Serif", serif; font-weight: bold; font-size: 14pt } h3.cjk { font-size: 14pt; font-family: "NSimSun"; font-weight: bold } h3.ctl { font-family: "Arial"; font-size: 14pt; font-weight: bold } pre { background: transparent } pre.western { font-family: "Liberation Mono", monospace; font-size: 10pt } pre.cjk { font-size: 10pt; font-family: "NSimSun", monospace } pre.ctl { font-family: "Liberation Mono", monospace; font-size: 10pt } strong { font-weight: bold } code.western { font-family: "Liberation Mono", monospace } code.cjk { font-family: "NSimSun", monospace } code.ctl { font-family: "Liberation Mono", monospace } em { font-style: italic }

---

EXAMPLE #1

---

PROMPT:

Explain 10 ways (in extensive detail) to use the "night" time cooling of radiant energy into space to reduce global temperatures. Include charts, and tables.

THINKING BLOCK:

Thought for 6.05 seconds

The user wants a detailed explanation of 10 ways to leverage nighttime cooling via radiative heat loss (radiant energy escaping into space) to reduce global temperatures. They want charts, tables, and extensive detail.

First, I need to understand the science behind "nighttime cooling via radiative energy into space." This is known as radiative cooling. Objects naturally radiate infrared (IR) heat into space through the atmospheric "window" (roughly 8-13 micrometers wavelength) where the atmosphere is relatively transparent. At night, there's no solar input, so if we can enhance this radiative loss and prevent convective/conductive heat gain, objects can cool below ambient air temperature.

Now, I need to think of 10 distinct ways to apply this for global cooling:

1. Radiative cooling surfaces for buildings - White, reflective paints/roof coatings with high thermal emittance in the IR window

2. Daytime radiative cooling materials - Nanostructured surfaces that reflect sunlight while emitting IR

3. Atmospheric aerosol enhancement of IR emission - Adding particles that emit in the window region

4. Stratospheric particle injection - Modifying stratospheric composition to enhance IR escape

5. Sky-facing cooling panels for urban heat islands - Large-scale passive cooling installations

6. Radiative cooling for power plant waste heat - Using passive cooling to reduce thermal load on atmosphere

7. Enhanced ocean surface radiative cooling - Modifying ocean surface properties to radiate more heat at night

8. Forest canopy modifications - Tree species/management to maximize nocturnal radiative cooling

9. Radiative cooling water harvesting and desalination - Passive systems that cool and provide fresh water simultaneously

10. Engineering the atmospheric window itself - Reducing greenhouse gases in the 8-13 micron range or adding emitters

Let me organize these into a coherent structure with charts and tables. I'll create:

• An introduction explaining the physics

• A table summarizing all 10 approaches

• Detailed sections for each approach

• Charts comparing potential impacts

• A conclusion

For the charts, since this is text-based, I'll use ASCII art or describe the data in tabular form with clear visual structure.

Let me start building this comprehensive answer. I'll make sure each of the 10 approaches is explained in extensive detail, covering the mechanism, potential, challenges, and status.

OUTPUT:

Harnessing Nighttime Radiative Cooling for Global Temperature Reduction

Introduction: The Physics of Radiative Cooling

All objects emit infrared (IR) radiation continuously. On Earth, the atmosphere is largely opaque to IR except for a specific wavelength band known as the atmospheric window (approximately 8–13 μm). Through this window, heat can escape directly to the cold sky (effectively ~3 K background temperature).

At night, when solar input ceases, surfaces can cool below ambient air temperature if:

1. They have high thermal emittance (ε ≈ 0.9-1.0) in the 8–13 μm window

2. Convective and conductive heat gains are minimized

3. They maintain low solar absorptance (α) to prevent daytime heating

Key Principle: ΔT_cooling = f(ε_IR, α_solar, sky temperature, air temperature, wind speed)

Theoretical maximum cooling: 15–20°C below ambient under clear, calm, dry night conditions.

---

Overview Table: 10 Radiative Cooling Strategies

---

1. Radiative Cooling Building Surfaces

Mechanism

Apply highly emissive paints, films, or coatings to building roofs and walls that:

• Reflect 85–95% of solar radiation (low α_solar)

• Emit 90–95% of absorbed heat in the 8–13 μm window (high ε_IR)

• Minimize conductive heat transfer from interior

Detailed Implementation

Material Composition:

• Base: Titanium dioxide (TiO₂) nanoparticles for solar reflection

• Binder: Fluoropolymer or acrylic matrix

• IR-emitting component: SiO₂ or MgF₂ microspheres

• Topcoat: Hydrophobic layer for self-cleaning

Application Protocol:

1. Clean and prime surface (remove oxidation, dust)

2. Apply 2-3 coats (total 200-300 μm thickness)

3. Apply hydrophobic topcoat

4. Maintain with annual inspection

Performance Characteristics

Global Impact Analysis

Temperature Reduction Potential (Urban Areas)
Region         Current Avg Temp    With RC Coatings    Reduction
-------------  ----------------   -----------------   ---------
Mumbai, India  31.5°C              29.0°C              -2.5°C
Chicago, USA   20.0°C              18.5°C              -1.5°C
Tokyo, Japan   21.0°C              19.5°C              -1.5°C
São Paulo, BZ  22.0°C              20.5°C              -1.5°C
Global urban   24.3°C              22.8°C              -1.5°C

Energy Savings:

• HVAC load reduction: 15–30%

• Peak electricity demand reduction: 10–20%

• CO₂ savings: ~0.5–1.0 tons/m² over building lifetime

Challenges & Solutions

Current Status

• Commercial products available (e.g., CoolRoof, Tyvek Cool Roof)

• Building codes in some regions (California Title 24)

• Estimated potential: 0.5–1.0°C global reduction if applied to 50% of urban roofs

---

2. Daytime Passive Radiative Cooling Materials

Mechanism

Engineered nanostructures that simultaneously:

• Reflect nearly all solar radiation (0.3–2.5 μm)

• Emit thermal radiation selectively in the atmospheric window (8–13 μm)

• Achieve cooling below ambient even under direct sunlight

Material Design

Multilayer Stack Architecture:

Layer 1: Top SiO₂ layer (100 nm) - IR transparency
Layer 2: TiO₂ nanoparticles (1 μm) - Solar reflection
Layer 3: PDMS matrix - Mechanical support
Layer 4: SiO₂ bottom layer (4 μm) - IR emission
Substrate: PET or aluminum foil

Alternative: Metamaterial Approach

• Periodic Si or SiO₂ nanostructures

• Resonant features tuned to 8–13 μm emission

• Photonic crystal design for broadband solar reflection

Performance Characteristics

Cooling Power vs. Solar Irradiance

Cooling Power (W/m²)
150 |                                    *
    |                                   *
140 |                                  *
    |                                 *
130 |                                *
    |                               *
120 |                              *
    |                             *
110 |                            *
    |                           *
100 |                          *
    |                         *
 90 |                        *
    |                       *
 80 |                      *
    |                     *
 70 |                    *
    |                   *
 60 |                  *
    |                 *
 50 |                *
    |               *
 40 |              *
    |             *
 30 |            *
    |           *
 20 |          *
    |         *
 10 |        *
    |       *
  0 |______*__________________________
      0   200 400 600 800 1000 1200
         Solar Irradiance (W/m²)

Applications

1. Building Roofs & Walls

   • Reduce HVAC demand

   • Prolong material lifespan (reduced thermal stress)

2. Vehicle Cooling

   • Car roofs, truck trailers

   • Reduce refrigeration needs

3. Electronics Cooling

   • Passive cooling for outdoor equipment

   • Telecom base stations, solar panels

4. Agriculture

   • Greenhouse cooling

   • Reduce irrigation needs

Global Impact Estimate

• If applied to 30% of urban surfaces: 0.3–0.6°C reduction

• Energy savings: 5–10% of global electricity demand

• Implementation timeline: 10–20 years for widespread adoption

---

3. Enhanced Atmospheric IR Emission Aerosols

Mechanism

Introduce aerosol particles into the lower atmosphere that:

• Have high IR emittance in the 8–13 μm window

• Are small enough (0.1–1 μm) to remain suspended

• Enhance atmospheric radiative cooling, especially at night

Particle Candidates

Injection Strategy

Location:

• Lower troposphere (2–5 km altitude)

• Mid-latitudes for optimal transport

• Multiple injection points for global coverage

Quantity:

• Estimated: 10–50 Tg/year (teragrams)

• Delivered via high-altitude aircraft or balloon-based systems

Dispersion Modeling:

Concentration Distribution (mg/m³)
Altitude (km)
6 |                              .
5 |                            .
4 |                          .
3 |                        .
2 |                      .
1 |                    .
0 |                  .
  |__________________________
     0   5000  10000  15000 km
          Distance from Injection

Climate Impact Modeling

Radiative Forcing Changes:

Note: Negative RF indicates cooling. Values are estimates based on GCM simulations.

Advantages Over Traditional Geoengineering

• Targets nighttime cooling specifically

• Less impact on solar radiation (reduced ecological disruption)

• Particles settle naturally (reversible)

• No stratospheric ozone chemistry impact

Risks & Mitigation

Implementation Timeline

• Research & modeling: 2–5 years

• Field trials (regional): 5–10 years

• Global deployment: 10–20 years

• Estimated cost: $5–20 billion/year

---

4. Stratospheric Radiative Modification

Mechanism

Introduce particles into the stratosphere that enhance IR emission to space, particularly in the atmospheric window region. Unlike traditional solar geoengineering (which reflects sunlight), this approach focuses on increasing outgoing longwave radiation (OLR).

Particle Selection Criteria

1. High IR emittance in 8–13 μm band

2. Stable at stratospheric temperatures (-50°C to 0°C)

3. Appropriate particle size (0.1–1 μm) for long residence time

4. Minimal impact on solar radiation (to avoid ecological disruption)

Optimal Candidate: MgF₂ Nanoparticles

• IR emittance: ~0.95 in window region

• Particle size: 0.2–0.5 μm

• Residence time: 1–2 years (stratospheric)

• Solar reflectance: Low (minimizes sunlight blocking)

Injection Infrastructure

Delivery Systems:

• High-altitude balloons (to 30 km)

• Modified commercial aircraft

• Rocket-assisted injection

Injection Rate:

• Estimated: 1–10 Tg/year

• Distributed across multiple injection points (6–12 globally)

Climate Impact Analysis

Outgoing Longwave Radiation Enhancement:

OLR Increase (W/m²)
5 |
4 |
3 |
2 |
1 |
0 |________________________________
    0   2   4   6   8  10
       Injection Rate (Tg/yr)

Temperature Response:

• Estimated: 0.5–2.0°C global reduction

• Regional variations: Greater at higher latitudes

• Seasonal: Most effective in winter/nighttime

Comparison with Solar Geoengineering

Risks & Considerations

Potential Benefits:

• Complementary to solar geoengineering

• Addresses nighttime warming specifically

• Less disruption to photosynthesis and ecology

Potential Risks:

• Stratospheric temperature changes

• Potential impact on jet streams

• International governance challenges

• Cost and logistics of global deployment

Implementation Strategy

1. Phase 1 (Years 1-5): Research, modeling, small-scale field trials

2. Phase 2 (Years 6-10): Regional deployment, monitoring, adjustment

3. Phase 3 (Years 11-20): Global deployment, optimization

4. Phase 4 (Ongoing): Maintenance, adaptation, potential phase-out

Estimated Cost

• Infrastructure: $50–100 billion (one-time)

• Annual operation: $5–15 billion

• Monitoring & research: $1–3 billion/year

---

5. Urban Radiative Cooling Parks & Plazas

Concept

Dedicated urban spaces designed to maximize passive radiative cooling, serving as "cool oases" that mitigate urban heat island (UHI) effects and provide public amenities.

Design Elements

1. Radiative Cooling Surfaces

• Ground surfaces with high IR emittance coatings

• White, reflective paving materials

• Elevated walkways to reduce conductive heating

2. Minimal Solar Absorption

• Canopies using RC materials

• Shaded areas with high-albedo surfaces

• Vegetation selected for low heat absorption

3. Enhanced Night Cooling

• Open sky access (minimize overhead obstructions)

• Wind channels to facilitate convective cooling

• Water features that evaporate and cool at night

4. Integrated Water Management

• Rainwater harvesting for irrigation

• Permeable surfaces for groundwater recharge

• Nighttime irrigation for evaporative cooling

Layout Example

[ Urban Radiative Cooling Park Layout ]
                    N
                    |
      [Entry]-------|-------[Parking (RC coated)]
                    |
    [Water Feature] | [Central Plaza (White RC paving)]
         |          |          |
    [Garden Area] [Seating Area] [Playground]
         |          |          |
    [Tree Canopy] [RC Canopy] [Open Lawn]
                    |
      [Restrooms]---|---[Entry]
                    |
              [Exit to Street]

Performance Metrics

Global Impact Estimate

Urban Heat Island Reduction:

• If 10% of urban areas converted to RC parks/plazas:

  • Average UHI reduction: 1–2°C

  • Peak summer night temperature reduction: 2–4°C

  • Energy savings (HVAC): 5–10% in affected cities

Health Benefits:

• Reduced heat-related mortality

• Lower respiratory issues (less ozone formation)

• Improved sleep quality

Implementation Challenges

Case Study: Potential Impact on Mumbai, India

Current UHI Characteristics:

• Daytime UHI: +4°C

• Nighttime UHI: +6°C

• Annual heat-related deaths: ~500

• Peak electricity demand: High due to AC use

With Radiative Cooling Parks (10% of urban area):

• Daytime UHI: +2°C

• Nighttime UHI: +3°C

• Estimated heat-related deaths: ~250 (50% reduction)

• Peak electricity demand: 8% reduction

---

6. Radiative Cooling for Industrial Waste Heat

Concept

Capture industrial waste heat and passively radiate it to space using large-scale radiative cooling surfaces, reducing the thermal load on the atmosphere and improving industrial energy efficiency.

Industrial Heat Sources

Radiative Cooling Heat Rejection System

Components:

1. Heat Collection: Existing waste heat sources

2. Heat Transfer: Heat exchangers, pipes

3. Radiative Cooling Surface: Large panels with high-ε IR coatings

4. Control System: Automated optimization

System Schematic:

[Industrial Process] --> [Waste Heat] --> [Heat Exchanger]
                                                        |
                                                        v
                                          [Radiative Cooling Panels]
                                                        |
                                                        v
                                              [IR Radiation to Space]

Performance Characteristics

Global Impact Analysis

If Applied to Major Industrial Sectors:

Temperature Impact:

• Reduced atmospheric heat load: 0.1–0.3°C global reduction

• More significant regional impacts near industrial clusters

Integration with Other Technologies

1. Combined with Thermal Storage:

   • Store heat during day

   • Radiate at night for maximum cooling

2. Hybrid with Mechanical Cooling:

   • Use radiative cooling for base load

   • Mechanical systems for peak demand

3. Waste Heat Recovery + Radiative Cooling:

   • Recover heat for useful purposes

   • Radiate remaining heat to space

Implementation Barriers

Future Potential

• Integration with industrial IoT for real-time optimization

• AI-based predictive cooling scheduling

• Coupling with renewable energy systems

---

7. Ocean Surface Radiative Enhancement

Concept

Deploy floating structures on ocean surfaces that enhance radiative cooling, particularly at night, to reduce ocean temperature and potentially influence global climate patterns.

Why Oceans?

• Cover 71% of Earth's surface

• Have high thermal capacity (slow to cool naturally)

• Ocean temperatures drive weather and climate patterns

• Nighttime cooling of oceans can reduce evaporation and cloud formation

Floating Radiative Cooling Platform Design

Structural Components:

• Base: Buoyant HDPE or aluminum frame

• Top Surface: Radiative cooling material (high-ε IR coating)

• Anchoring: Mooring system to fix position

• Power: Optional small solar panels for sensors

Platform Specifications:

Deployment Strategy

Phase 1: Regional Trials

• Locations: Major ocean currents (Gulf Stream, Kuroshio)

• Scale: 100–1,000 platforms

• Duration: 2–5 years

• Objectives: Measure cooling effect, structural integrity, ecological impact

Phase 2: Expansion

• Scale: 10,000–100,000 platforms

• Locations: Extended to major ocean basins

• Duration: 5–10 years

Phase 3: Global Coverage

• Scale: 1,000,000+ platforms

• Locations: All major ocean areas

• Duration: 10+ years

Expected Climate Impact

Ocean Temperature Reduction:

Sea Surface Temperature Reduction (°C)
0.5 |
0.4 |
0.3 |
0.2 |
0.1 |
0.0 |________________________________
    0    100k   500k   1M   5M   10M
           Number of Platforms

Atmospheric Temperature Impact:

• Estimated global reduction: 0.3–1.0°C (depending on deployment scale)

• Greater impact in tropical regions

• Potential to reduce hurricane intensity

Ecological Considerations

Potential Benefits:

• Reduced ocean acidification (cooler water holds more CO₂)

• Decreased coral bleaching events

• Altered fish migration patterns (could be beneficial or harmful)

Potential Harms:

• Physical obstruction to marine life

• Altered surface currents

• Impact on phytoplankton (light blocking)

• Microplastic pollution (if materials degrade)

Mitigation Strategies:

• Use biodegradable materials where possible

• Design with marine life passage in mind

• Monitor ecological impacts continuously

• Adaptive deployment (adjust based on findings)

Cost Analysis

Comparison with Other Ocean-Based Geoengineering

---

8. Forest Canopy Radiative Optimization

Concept

Select, plant, and manage tree species and forest structures to maximize nocturnal radiative cooling, thereby reducing regional temperatures and influencing climate patterns.

Scientific Basis

Trees and Radiative Cooling:

• Tree canopies have high IR emittance (~0.95)

• At night, canopies cool faster than soil and urban surfaces

• Dense forests can have temperatures 2–5°C lower than surrounding areas at night

• Transpiration also contributes to cooling

Optimal Forest Characteristics for RC:

• Dense canopy (minimize gaps)

• Broadleaf species (higher surface area)

• Dark foliage (high IR emittance)

• Minimal understory (reduces convective heat gain)

Species Selection

High Radiative Cooling Potential Species:

Forest Management Practices

1. Canopy Density Management

• Maintain 70–90% canopy cover

• Thin selectively to promote dense growth

• Avoid clear-cutting

2. Species Composition

• Favor high-RC species

• Mix species for resilience

• Consider native vs. exotic trade-offs

3. Stand Age Management

• Mature forests have higher RC potential

• Maintain mix of age classes

• Allow natural succession where appropriate

4. Understory Management

• Control dense understory

• Allow some herbaceous layer for soil moisture

Regional Impact Modeling

Example: European Temperate Forests

Note: Nighttime cooling is greater than daytime due to enhanced radiative cooling.

Global Impact Estimate

If Applied Globally:

• Potential global temperature reduction: 0.5–1.5°C

• Greatest impact in tropical and temperate regions

• Complementary to other geoengineering approaches

Regional Variations:

Temperature Reduction (°C) by Region
3 |
2 |
1 |
0 |________________________________
    Tropics  Temperate  Boreal  Global Avg

Challenges

Synergies with Other Approaches

• Complements urban RC (forests on urban edges)

• Works with ocean RC (coastal forests)

• Reduces need for stratospheric aerosols

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9. Radiative Cooling Water Systems

Concept

Use radiative cooling at night to pre-cool water for daytime use, reducing the energy required for water heating and cooling systems. This approach indirectly reduces global temperatures by lowering energy consumption and associated emissions.

System Design

Basic Configuration:

1. Radiative Cooling Panel: High-ε IR surface facing sky

2. Water Storage Tank: Insulated container

3. Heat Exchange System: Transfers heat between water and panel

4. Control System: Automates operation based on temperature and time

Schematic:

          [Night Sky (~3K)]
                 |
                 v IR radiation
      [RC Panel (ε=0.95)]
                 |
                 v Heat exchange
      [Water Storage Tank]
                 |
                 v Water supply
         [Household/Industrial Use]

Performance Characteristics

Applications

1. Residential Hot Water Pre-Heating

• Pre-cool water at night

• Use less energy to heat to desired temperature during day

• Particularly effective in sunny, dry climates

2. Industrial Process Cooling

• Cool process water at night

• Use cooled water for daytime operations

• Reduces chiller load

3. District Cooling Systems

• Large-scale RC water cooling

• Distribute cooled water to multiple buildings

• Nighttime charging of thermal storage

4. Agricultural Irrigation Cooling

• Cool irrigation water at night

• Reduce evaporation losses during day

• Improve crop yields in hot climates

Global Impact Analysis

Energy Savings: