Qwen3.8-Flash-Next — Q4_0-Q8out-v3

Run a 95.5 GiB model on a 64 GB Mac, at ~27 tokens/sec.

This checkpoint is tuned for one specific job: running Qwen3.8-Flash-Next on Apple Silicon with 64 GB of unified memory, by keeping the rarely-used expert weights on SSD and reading them only when a token actually needs them.

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⚠️ Read this first

This model does not run on stock llama.cpp. Not "runs slower" — it will try to load all 95.5 GiB into memory and fail.

You need this fork, which adds --moe-stream:

👉 https://github.com/npanj/llama.cpp

Upstream llama.cpp does support the qwen4exp architecture. What it does not have is expert streaming, which is the entire reason this checkpoint fits on a 64 GB machine.

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What you need

The disk matters more than you'd expect — expert weights are read from it continuously while generating. An external USB drive will be much slower.

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Quick start

Full instructions, troubleshooting and flag-by-flag explanation: docs/qwen38-flash-next-v3.md

1. Build the fork

git clone https://github.com/npanj/llama.cpp
cd llama.cpp
cmake -B build -DGGML_METAL=ON -DCMAKE_BUILD_TYPE=Release
cmake --build build -j8 --config Release

2. Download the checkpoint (95.5 GiB, 3 shards)

D=~/models/qwen38-flash-next-v3 && mkdir -p $D
for i in 1 2 3; do
  curl -fL --retry 5 -C - -o $D/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-0000$i-of-00003.gguf \
    https://huggingface.co/nitinpanj/qwen38-flash-next-v3/resolve/main/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-0000$i-of-00003.gguf
done

3. Download the draft head (1.9 GiB — worth about +50% generation speed)

D=~/models/qwen38-flash-next-mtp && mkdir -p $D
curl -fL --retry 5 -C - -o $D/mtp-shared-Q4_K_M.gguf \
  https://huggingface.co/nitinpanj/qwen38-flash-next-v3/resolve/main/MTP/mtp-shared-Q4_K_M.gguf

4. Let the GPU wire enough memory — don't skip this, and it resets on reboot

sudo sysctl iogpu.wired_limit_mb=59392

5. Run

export LLAMA_MOE_STREAM_LOOKAHEAD=1 LLAMA_MOE_STREAM_WAVE_CAP=200 \
       LLAMA_MOE_STREAM_PARTITION=1 LLAMA_QWEN4EXP_SPARSE_FA=1

./build/bin/llama-server \
  -m ~/models/qwen38-flash-next-v3/Qwen3.8-Flash-Next-Q4_0-Q8out-v3-00001-of-00003.gguf \
  -md ~/models/qwen38-flash-next-mtp/mtp-shared-Q4_K_M.gguf \
  -ngl 99 \
  --moe-stream --moe-stream-cache 36 --moe-stream-io-threads 8 --moe-stream-direct \
  -c 98304 -b 4096 -ub 4096 -cms 512 -np 1 -fa on \
  --cache-reuse 0 --cache-ram 512 \
  --jinja --reasoning-format deepseek \
  --spec-type draft-mtp --spec-draft-n-max 3 --spec-draft-p-min 0.3 \
  --spec-draft-ngl 99 --spec-max-prompt 0 \
  --host 127.0.0.1 --port 8080

Then open http://127.0.0.1:8080, or point any OpenAI-compatible client at http://127.0.0.1:8080/v1.

First load takes a few minutes — it is reading 95.5 GiB off disk.

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Files

About the draft head. It has no token embeddings of its own — it borrows the main model's, which is how it stays at 1.9 GiB instead of 2.6. So it only works when passed with -md alongside this checkpoint, on this fork. It cannot be loaded on its own.

Its tensor names are fork-specific too. Upstream PR #28243 adds shared-MTP support for this architecture using unsloth's original naming, not the names this fork renames to. If that lands, unsloth's sidecar will load on upstream llama.cpp directly and this file still will not.

Prefer to build your own from unsloth's published sidecar? The scripts and an explanation of the conversion are at scripts/mtp/.

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Measured performance

Apple M5 Pro, 64 GB, with the configuration above:

Generation slows as context fills. That is expected — at depth the cost is dominated by reading the attention cache, not by the streamed expert weights.

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What "v3" is

bartowski's Q4_0 with a Q8_0 output.weight spliced in, plus unsloth's UD-IQ4_XS spliced into five tensor groups that stay resident in memory (attn, hc, token_embd, ssm_out, shexp).

Measured against the unspliced checkpoint, paired 40-chunk perplexity:

The five resident groups are not arbitrary. Trimming to attn,hc,token_embd measures the same perplexity but drops decode 14% — ssm_out and shexp do nothing for perplexity and are worth about 11 points of decode.

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If your Mac isn't 64 GB

• Less memory: lower --moe-stream-cache and the matching wired cap. It still loads; expect it to be slower. Untested below 64 GB.
• More memory: raise the cache — but in small steps. On a 64 GB machine, going from 36 to 38 GiB of cache collapsed generation from ~24 to ~3.7 tokens/sec as macOS began swapping. More is not monotonically better.
• Not a Mac: untested. Expert streaming is not Metal-specific in principle, but nothing here has been tuned or measured on CUDA or CPU.

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Credit

• Base model: Qwen3.8-Flash-Next by Qwen.
• Quantization sources: bartowski (Q4_0) and unsloth (UD-IQ4_XS, and the MTP sidecar this draft head is converted from).
• Expert streaming (--moe-stream), the feature that makes this run at all, comes from mihailescu2m/llama.cpp.

This repository is the spliced checkpoint and the converted draft head. Model weights remain under the original Qwen license.

Problems? Open an issue at github.com/npanj/llama.cpp/issues. Please say which Mac and how much memory you have, and include the first ~30 lines the server prints at startup.