Qwen3.8-27B on 16-32 GB Nvidia GPUs one-click install for Windows / Linux

_{by Mia'a AI Lab}

A serving kit for Qwen/Qwen3.8-27B in turboderp's EXL3 quants, on one consumer NVIDIA card. It picks a quant that fits the card it finds, installs its own Python environment, downloads the weights, serves an OpenAI-compatible endpoint, and opens a chat UI. Windows and Linux, same behaviour.

It started as a 16 GB recipe — the 2.0 bpw quant is still that floor, and still the one thing here that is not turboderp's own upload (Mia-AiLab/Qwen3.8-27B-EXL3-2.0bpw, SC_2.00bpw_H3_V3). Everything from 2.5 bpw up is pulled from turboderp/Qwen3.8-27B-exl3 by revision. Which one you get is decided by your VRAM, at setup, and you can change it any time.

Jump to your guide: Windows · Linux

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Before you start (both systems)

Not needed: CUDA Toolkit, Visual Studio Build Tools, Git. The engine arrives as a prebuilt wheel; compiling is the fallback for platforms no wheel covers.

Everything the kit installs stays inside its own folder — .venv/, models/, logs/, apps/, .dsh/. Nothing goes into the system Python and nothing needs administrator rights.

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Windows

Everything is in the windows\ folder. Run the .bat files from Explorer (double-click) or from a cmd window opened in the kit folder.

1. Install — windows\START-HERE.bat

Double-click it once. It opens a page in your browser and does the whole install there: it shows what it found on the card, offers the model sizes that fit it, then installs and downloads with a progress bar and a live log. Nothing is asked in the console.

When the download finishes it loads that model and hands the page over to the chat, so one double-click takes you from nothing to a working chat window.

windows\START-HERE.bat              install, then start what was installed
windows\START-HERE.bat --no-start   install only — for fetching a second size

Notes:

• The download is resumable. Closing the window, losing the connection or rebooting costs you nothing — it picks up from the byte it stopped at. A model left half-downloaded is shown as such in the menus, and running windows\START-HERE.bat again finishes it. Nothing offers to start a model until every weight file is on disk.
• Prefer the old console questions to the web page? Set SETUP=console in .env.

2. Every day after that — windows\start.bat

windows\start.bat          start a model that is already here
windows\start.bat setup    go to setup instead (same as START-HERE.bat)

It never downloads anything. What it does:

1. Asks which model, if more than one size is on disk. Enter takes the one that ran last, and it starts on its own after 45 seconds so an unattended machine still comes up.
2. Checks free VRAM right before the load — it wants the GPU_MEM_GB budget from .env plus a little margin. If that much is not free it lists the programs holding VRAM (browsers, games, Discord, other AI tools) and waits: Enter re-checks, c continues anyway, q quits, and it continues on its own after 120 seconds. Take this seriously on Windows — with too little free VRAM the driver pages the model into system RAM instead of failing, and it then runs many times slower.
3. Loads the model and opens the chat UI at http://127.0.0.1:3080/.

If nothing is installed yet, or nothing finished downloading, it says so and offers to run setup for you — double-clicking the wrong one is never a dead end.

Two files rather than one because they answer two different questions: windows\start.bat never downloads, and windows\START-HERE.bat --no-start never loads.

3. While it runs

• The console window it opened is the server. Closing it stops the model.
• Simplex puts an icon in the notification area — right-click for Open Simplex, Restart the model, Show the Simplex folder, View the log and Quit Simplex. (TRAY=no in .env turns it off.)
• Every launch writes a full transcript to logs\, so a crash that scrolls past is still readable afterwards.
• The first successful launch adds Start-menu and desktop shortcuts (SHORTCUTS=no in .env to skip that).

4. Stopping — windows\stop.bat

windows\stop.bat                 stop both the model and the chat UI
windows\stop.bat --harness-only  leave the model loaded, close the UI
windows\stop.bat --server-only   leave the UI running, unload the model

5. Windows troubleshooting

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Linux

Everything is in the linux/ folder. Run the scripts from the kit root; they find their own way regardless of where you call them from.

If the files arrived without their execute bit (a zip, a copy off Windows), run them as bash linux/setup.sh instead of ./linux/setup.sh, or chmod +x linux/*.sh linux/simplex once.

1. Install — ./linux/setup.sh

./linux/setup.sh

It creates .env from .env.example on the first run, asks the profile questions in the terminal (tools/profiles.py) — there is no setup page on Linux — builds .venv, installs PyTorch and the engine, downloads the weights, and stops. It does not load a model.

The download is resumable: interrupt it and run ./linux/setup.sh again to carry on.

2. Every day after that — ./linux/start.sh

./linux/start.sh                 pick a downloaded model and serve it
./linux/start.sh --no-harness    serve /v1 only, no chat UI
./linux/start.sh -b              run in the background, output in logs/
./linux/start.sh --status        is a backgrounded one running?

It lists the models that finished downloading and asks which one (Enter is the one used last; it auto-picks after 45 seconds), then serves:

http://localhost:8888/v1     the OpenAI-compatible API
http://127.0.0.1:3080/       the chat UI

-b is the honest equivalent of the Windows tray: it detaches, writes to logs/simplex-*.log, and tells you where that log is and how to stop it. First-run setup and the model menu still happen — written to the log instead of the screen.

On a box with no desktop session webbrowser has nothing to open, so the chat address is printed for you to copy. Take the whole thing, token and all — see the note on the token.

There is no free-VRAM preflight on Linux (that check is Windows-specific, because Windows silently spills to system RAM instead of failing). If a load fails with Insufficient VRAM in split for model and cache, lower CONTEXT_SIZE or GPU_MEM_GB in .env, or close what is holding the card.

No tray icon and no desktop shortcuts either — those are Windows.

3. Stopping — ./linux/stop.sh

./linux/stop.sh                 stop both
./linux/stop.sh --harness-only  leave the model loaded
./linux/stop.sh --server-only   leave the chat UI running

4. Linux troubleshooting

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One command, both systems

The files above are the double-click doors. Every verb, on either system, is simplex — the same program (tools/cli.py), so the two cannot drift apart:

simplex setup                   install the environment and fetch a model
simplex start                   load a model and serve it
simplex start --no-harness      ...serving /v1 only
simplex start -b                ...in the background, log in logs/
simplex start -p 9000           ...on another port, just this once
simplex stop                    stop both
simplex stop --harness-only     ...and detach the UI, model still loaded
simplex restart                 stop, then start
simplex status                  what is running, which model, which ports
simplex status --json           the same, for scripts
simplex logs -f                 follow the launcher log
simplex models                  what is on disk, and what is half-downloaded
simplex harness start           attach the UI to a server already running
simplex harness stop|status|open|settings
simplex doctor                  check this machine before blaming the model

simplex with no verb prints the help and then the status. Every verb takes --help. It is not on your PATH — run it from the kit folder.

simplex doctor

The first thing to run when something is wrong. It checks the Python version, the venv and the engine version inside it, the driver and the card, Node, both ports and who holds them, the .env values that have to be valid, whether the weights are all there, and the free disk.

With or without the chat UI

UI= in .env is the standing answer (browser, server or no); --harness / --no-harness overrides it for one run, on simplex start, linux/start.sh and windows\start.bat alike. The UI is also a verb of its own, so it can be attached to a model that is already loaded, or taken away without unloading one.

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What the launcher picks for your GPU

Setup (windows\START-HERE.bat / ./linux/setup.sh, or simplex setup, or PROFILE=ask in .env) runs tools/profiles.py. It reads the card's VRAM with nvidia-smi, computes what fits under a budget of VRAM − max(1.3 GB, 8 %), and offers the sizes that fit. Enter takes the recommendation. If a model is already downloaded, "keep current" is the default, so an unattended start never triggers a surprise download.

The choice is written into .env (MODEL_DIR, HF_TARGET_REPO, HF_REVISION, MODEL_ID, CONTEXT_SIZE, CACHE_QUANT, GPU_MEM_GB, VISION) and everything downstream follows it.

KV cache is int4 on every profile the planner writes — measured within 0.001 KL of fp16, with no hardware requirement, so it runs on every supported GPU. (The .env.example template still ships CACHE_QUANT=8,4, which is the old hand-tuned 2.0 bpw baseline below; picking a profile overwrites it.)

Each row's download size and the exact context it plans are printed by the planner itself, and it will do that for any card without you owning one:

Windows:  .venv\Scripts\python.exe tools\profiles.py --list --vram 16
Linux:    .venv/bin/python tools/profiles.py --list --vram 16

Where those numbers come from

Where a real prefill has been run at a stated budget, the menu offers what was measured rather than what the formula computes. The context was grown on a 14.7 GB budget until a prefill failed (2026-09-06), and the formula had been leaving a lot on the table:

The formula's flat 2.6 GB overhead is a bound over every quant, so on any one of them it is slack — 3.5 bpw is the extreme case, priced out of a 16 GB card entirely by a formula that the card then ran at 78k tokens, and 6.0 bpw is not far behind at 9k against 84k. Above the budget a row was measured under, the formula takes over again; below it, the measurement only ever lowers the answer.

Two rows are also capped at their measurement: nothing has ever prefilled past 204800 tokens on 5.0 bpw or 83712 on 6.0 bpw, at any budget, so neither plans past it. (Their older ceilings — 183296 for 5.0, "nothing survived" for 6.0 — came from a run with ~40 other processes on the card, and a clean run at a tighter budget beat both, which is how you tell contention from a ceiling.)

How the default is chosen

Bold above is the best quality that still has real context (≥ 128k), not the longest context. On a 16 GB card that is the 2.5 bpw row — 3.0 bpw is the better model, but it fits 118k there and only text-only, against a measured 176k with images one rung down. The pick also keeps images where it can: on a 24 GB card 5.0 bpw clears 128k only by dropping the vision tower, so the default steps one rung down to 4.0 bpw, which holds native context with images. One rung, never more — and answering "no images" puts 5.0 bpw back.

Quants other than the 2.0 bpw baseline are pulled from turboderp's branches (HF_REVISION); their vision towers are unquantised (0.87 GB measured), which is why images are off on the tight profiles. The quality words come from turboderp's mean-KL-vs-bf16 figures: 2.0 → 0.35 fair, 2.5 → 0.30 good, 3.0 → 0.11 better, 3.5 → 0.08 very good, 4.0 → 0.05 very good, 5.0 → 0.014 excellent, 6.0 → 0.007 near-lossless.

The original 16 GB baseline

A 16 GB board typically has about 14.7 GB free after the driver. These are the hand-tuned settings the 2.0 bpw quant was validated on, and what .env.example still ships:

Measured at load under that cap: CUDA allocated 12.35 GiB, reserved 13.37 GiB; native CONTEXT_SIZE=262144 fails to boot (Insufficient VRAM in split for model and cache). Weights on disk are ~9.7 GB; the rest is KV (16 full-attention layers), the MTP draft cache, GDN recurrent state and CUDA workspace.

If you have more than 16 GB: let the profile planner do it — it already knows. By hand, a 24 GB card takes CONTEXT_SIZE=262144 and GPU_MEM_GB=22. Do not do that on 16 GB.

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Chat with the model

The DeepSeek Harness

The kit serves the model. What you talk to is DeepSeek Harness (dsh, MIT), started as a second process once the model is loaded and answering, at http://127.0.0.1:3080/. windows\start.bat and ./linux/start.sh open it for you; http://127.0.0.1:8888/ is a small page saying where everything is, which forwards there as soon as the harness answers.

None of it is vendored here. It is a Node application, so the launcher runs it with npx and npm caches it after the first run — which is why Node is on the requirements list. No Node, no harness: the launcher says so, and /v1 keeps serving every other client. UI=no in .env skips it entirely.

It configures itself against whatever loaded. Before starting the harness, tools/dsh.py asks the running server on /v1/models what it actually is — the model id, the context window, whether the vision tower fit, and which reasoning levels this chat template accepts and acts on — and writes that as a provider route into .dsh/settings.yaml, naming it as the default the picker opens on. So the model, its context meter, its image support and its effort menu are right on the first launch with nothing typed into a form. After a fallback (no room for the vision tower, say) the harness is told what happened rather than what .env hoped for. Switching quants rewrites the same file; the harness re-reads it per request, so it never needs restarting.

Those keys are yours the moment you edit them. The launcher keeps a copy of what it last wrote beside the file and stops generating as soon as they differ, so a hand-tuned route survives every restart. Delete the file to get a fresh one. Everything else in it is read past and written back untouched. Every field it accepts is in dsh's configuration catalog.

The first address is not the plain one

dsh authenticates the browser with a token it mints fresh on every launch and prints once, as dsh web: http://127.0.0.1:3080/?token=.... Opening that sets a cookie good for thirty days and redirects to a clean /; arriving at the bare address without it answers "dsh web authentication required; reopen the URL printed by dsh web". So the launcher reads the address off dsh's own output rather than composing it from the port, and http://127.0.0.1:8888/ forwards through /harness, which knows the current one. If you need it by hand, it is in the launcher window and in logs/.

That route only answers a browser on this computer, even with HOST=0.0.0.0: the token is a session on an agent that runs commands here.

The harness binds loopback only and refuses to bind 0.0.0.0 at all: its agent runs commands on this PC and there is no login. To reach it from a phone, put a proxy in front of it rather than opening the port — tailscale serve --bg 3080. Note that the shipped HOST=0.0.0.0 already exposes /v1 (the API, not the harness) to whatever network you are on; set HOST=127.0.0.1 if that network is not yours.

Its workspace, approval policy, tools, MCP servers and plugins are all its own — see its documentation. Pick a workspace folder in it before the first message.

Any other OpenAI client

/v1 is a plain OpenAI endpoint, so nothing about the harness is compulsory. Leave windows\start.bat / ./linux/start.sh running and point a client at it. There is no API key; many apps still require a dummy value such as local.

Chatbox, Open WebUI, Continue, Cursor's custom endpoint, Cherry Studio (below), a curl, an SDK, another machine on your network — all of them work against that base URL, at the same time as the harness does. Open WebUI is stronger if you want a big tools/RAG UI and are fine running Docker.

Tool calling. Send OpenAI tools (function name + JSON schema) on POST /v1/chat/completions. The model emits Qwen XML; the server parses it into tool_calls. Your app must run the function and POST a follow-up with role: "tool" (and the previous assistant tool_calls). tool_choice of auto, required, or a named function is supported.

curl http://127.0.0.1:8888/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3.8-27b-exl3-2.5bpw",
    "messages": [{"role": "user", "content": "What is the weather in Tel Aviv?"}],
    "tools": [{
      "type": "function",
      "function": {
        "name": "get_weather",
        "description": "Current weather for a city",
        "parameters": {
          "type": "object",
          "properties": {"city": {"type": "string"}},
          "required": ["city"]
        }
      }
    }]
  }'

Plain chat (no tools):

curl http://127.0.0.1:8888/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen3.8-27b-exl3-2.5bpw","messages":[{"role":"user","content":"Hi"}]}'

Images. The quants keep Qwen3.8's vision tower and the server loads it by default (VISION=auto), so you can send OpenAI image_url content parts — data: URLs or http(s) links. Pictures are downscaled to IMAGE_MAX_PIXELS (1 MP ≈ 1024 prompt tokens) before encoding. If the tower does not fit next to your context under the VRAM cap, the Ready box says Images: off and the server keeps running text-only — lower CONTEXT_SIZE and restart, or pick a quant whose profile says images are on. Video is not supported.

Defaults: temperature 0.6, top-p 0.95, top-k 20, thinking on. One request at a time; extras queue.

Cherry Studio (optional, off by default)

The kit used to ship Cherry Studio as its chat app. It is still wired up for anyone who wants Cherry's assistants, knowledge bases and MCP servers — set CHERRY_AUTOSTART=ask (or yes) in .env:

• The launcher downloads the pinned portable build (v2.0.10, ~285 MB, once) into apps/cherry-studio/. Nothing is installed system-wide; Cherry keeps its data in apps/cherry-studio/data/.
• The first time, Cherry opens and closes once by itself to create that data folder. The kit then writes its configuration straight into Cherry's store: provider Simplex (local) → http://127.0.0.1:8888/v1, key local, the model id from .env (tool calling + image input on), set as the default chat model and on the default assistant, onboarding skipped. Usage analytics is switched off (Cherry → Settings → Privacy to change).
• After the Ready box: "Open Cherry Studio and start chatting now? [Y/n]". Enter/y opens it (the portable build unpacks for ~10–20 s), n or no answer within 90 s leaves it closed. Keep the server window open while chatting.
• Changing PORT in .env re-points the provider on the next start. Open Cherry later without the prompt: .venv\Scripts\python.exe tools\cherry.py open on Windows, .venv/bin/python tools/cherry.py open on Linux (status instead of open shows what the kit thinks).
• .env knobs: CHERRY_AUTOSTART=ask|yes|no (no also skips the download), CHERRY_VERSION (pinned; the store layout is checked against v2.0.x), CHERRY_EXE=<path> to use a Cherry Studio you already installed — the kit then only sends Cherry's official import link (cherrystudio://providers/api-keys), you confirm the popup and add the model id under the new provider.
• Web search and tools are on by default. The default assistant gets web_search + web_fetch as function tools (Cherry's stock keyless search provider, Exa MCP at mcp.exa.ai; change it under Settings → Web Search), runs MCP in auto mode, and the kit installs Cherry's keyless builtin MCP servers @cherry/fetch and @cherry/sequentialthinking. Tune with CHERRY_WEB_SEARCH=1|0 and CHERRY_MCP_SERVERS= (also @cherry/python, @cherry/browser; empty = none) in .env — re-applied on the next start when you change them. New assistants you create in Cherry start with Cherry's own defaults (web search off) unless you copy the default one.
• Cherry Studio is AGPL-3.0 (see its README for the commercial-use terms); the kit downloads the official release binary and does not redistribute it.

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Prebuilt wheels: no compiler needed

Compiling the ExLlamaV3 CUDA kernels is the slowest and most fragile part of setup: it wants the CUDA Toolkit and Visual Studio Build Tools, several GB of downloads that have nothing to do with chatting to a model. Nobody has to do it, because the engine publishes wheels itself.

Simplex looks for one in this order:

1. wheels/ in the kit folder — what you copy off a USB stick.
2. The engine's own release — turboderp-org/exllamav3 attaches a wheel per (CUDA line × torch version × Python). This is the normal path and needs no configuration.
3. WHEEL_INDEX in .env — one or more pip --find-links targets (a GitHub Releases page, a file share, an internal index).
4. PyPI, which has triton-windows but not exllamav3.
5. Compiling from source, for the cases none of the above covers — a CUDA line or platform the engine has no build for (aarch64/GB10), or no route to github.com.

Step 2 resolves to one exact URL rather than pointing pip at the release page, and that distinction matters. The CUDA line and torch version live in the wheel's local version (1.4.4+cu128.torch2.10.0), which pip does not match against anything: given --find-links it filters on the Python and platform tags only, then takes the highest version string. A torch 2.10 environment would be handed the torch 2.11 build, and the failure arrives later as an undefined-symbol ImportError that reads like a corrupt install. So the launcher installs torch first, asks the venv what it actually got, and names the one wheel that fits.

This is also why the default PyTorch index is cu128: the engine builds for cu128 and cu132 only, so torch from any other line means no wheel exists and everyone compiles. cu128 covers Blackwell and needs driver 570+. (TORCH_INDEX_URL in .env overrides it. aarch64 keeps cu130, since no engine wheel exists there on any line.)

A wheel is only used when its Python, ABI and platform tags match the interpreter it is going into, so a cp313 wheel can never land in a cp312 environment. Check what would be picked:

Windows:  .venv\Scripts\python.exe tools\wheels.py
Linux:    .venv/bin/python tools/wheels.py

That prints the venv's tags, the torch version and CUDA line found, and the wheel it would install. wheels/README.md covers the override cases and the recipe for building one yourself.

Engine version. This kit requires ExLlamaV3 v1.4.4 — it is what the quantized vision tower needs, and what the kit is validated against. PyPI skips 1.4.4 (1.4.2 → 1.4.5), so the launchers install the git tag, and both start paths refuse to run against any other version. Engine: ExLlamaV3.

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Installing on a Windows PC as an app

Unzip (or clone) anywhere, double-click windows\START-HERE.bat, then windows\start.bat from then on. There is no system-wide install and no administrator rights are needed; the first successful launch adds the Start-menu and desktop shortcuts for you (SHORTCUTS=no in .env to skip that).

Whether the browser offers an Install button for the chat page depends on the front end you use, not on this kit. Installing only ever works on localhost or over HTTPS (a secure context); over a plain http:// LAN address browsers refuse to register a service worker, so there the page stays an ordinary one.

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Configuration

.env in the kit folder, created from .env.example on the first run of either system. It is read as a key=value list, not executed, so values with spaces are fine. The ones you are most likely to touch:

.venv/, models/, logs/, apps/, .dsh/ and .env stay on your machine and are not part of the git tree.

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Simplex, the UI this kit used to ship

Up to this version the kit served its own chat and agent UI in the server process. That UI is now Simplex, a standalone project: nothing in it was specific to this model or this server, and it talks to any OpenAI-compatible endpoint — including this one, at http://127.0.0.1:8888/v1. Its conversations, projects and providers moved with it, so an existing install picks up where it left off. Run its own launcher beside this one and set UI=no here if you want it back in place of the harness, or run both.

License

Apache-2.0 (inherited from the base model). Kit scripts: MIT.