Qwen3.8-Flash-Next-exl3-CMP170HX-64GB

Qwen3.8-Flash-Next EXL3 — fitted to a single unlocked CMP 170HX 8GB

A single unlocked NVIDIA CMP 170HX 8GB (64 GB of VRAM) runs the full 262,144-token context with MTP speculative decoding, filling the card's VRAM to the last few hundred MiB.

Mixed-bitrate EXL3 built from turboderp's EXL3 quants of Qwen3.8-Flash-Next: 4-bit routed experts on 20 of the 48 layers, 3-bit on the rest, 6-bit attention, shared experts and lm_head (details below). Requires ExLlamaV3 v1.5.1 or newer.

Requirements

• GPU: an unlocked CMP 170HX 8GB (64 GB of VRAM)
• SSD: 39 GB free for the n-gram table, which is read from it during inference. With enough free RAM, the whole 39 GB can be kept in RAM instead.
• RAM: free RAM is used as a cache for the n-gram table, which makes it slightly faster.

Numbers

All measured on the machine described below, with the TabbyAPI config in this repo (one stream, MTP draft 3, 8-bit K/V cache, 262,144-token cache).

Full context — prompt = the first N tokens of wikitext-2 (test) + a summarization request, fresh prompt each time (no prefix cache), greedy, thinking off, up to 500 new tokens:

Short context — 500 new tokens, greedy, thinking off (median of 3 after 1 warm-up):

Accuracy

Greedy, thinking off. HumanEval code is run against its tests; GSM8K is scored on the final number.

How it was made

No new quantization. ExLlamaV3 stores every linear layer independently and reads its bitrate from the tensor shape, so tensors from two conversions of the same source model can be combined module by module. This repo takes:

The 4-bit layers follow ExLlamaV3's own promotion order (closest to either end of the stack first). Experts average 3.43 bpw. Only the tensors needed were fetched from turboderp's repo with HTTP range requests and written straight into per-layer shards (experts-LNN.safetensors, dense.safetensors, …), so a different layer choice is a one-file swap. The script and the exact recipe are in recipe/ — pick your own number of 4-bit layers for a different VRAM budget with build_mix.py.

TabbyAPI

recipe/tabbyapi-config.yml is the config every number above was measured with. It uses the sampling defaults in recipe/sampler-override-flash-next.yml (temperature 1.0, top_p 0.95, top_k 20, min_p 0, for thinking mode); copy that file to TabbyAPI's sampler_overrides/flash-next.yml. The parts that matter:

model:
  max_seq_len: 262144
  cache_size: 262144
  cache_mode: "8,8"
  chunk_size: 2048
  max_batch_size: 1
  ngram_ram: false
  vision: true
  vision_offload: true
draft_model:
  draft_mode: mtp
  draft_cache_mode: "8,8"
  draft_num_tokens: 3

MTP draft length, measured in a separate run (20 × 4-bit, one stream, 500 tokens, greedy):

In ExLlamaV3's own example scripts the same settings are -cs 262144 -cq 8 -mtp -ndt 3.

The n-gram table

By default the 39 GB n-gram table stays on disk, and only the rows each token needs are read, in parallel. The OS keeps what has been read in free RAM (page cache), so frequently used rows come from RAM and you do not need enough RAM to hold the whole table. To keep all 39 GB in RAM, use ngram_ram: true (TabbyAPI) or -ngr / --ngram_ram (ExLlamaV3 scripts), which is slightly faster.

Test machine

• GPU: NVIDIA CMP 170HX 8GB, unlocked to 64 GB; reports compute capability 8.0; PCIe 2.0 x4 link; power limit 180 W, stock clocks; driver 615.71.09
• Software: Linux 7.2, CUDA 13.4, PyTorch 2.9.1+cu130, ExLlamaV3 1.5.1 (master 6b84a21), TabbyAPI main 7208273

Building ExLlamaV3 for this card

The card reports compute capability 8.0, so build for sm_80. With GCC 16, PyTorch 2.9's headers fail to compile through nvcc (need 'typename' before ... in List_inl.h); GCC 15 plus -fpermissive works:

CC=gcc-15 CXX=g++-15 CUDAHOSTCXX=g++-15 \
NVCC_APPEND_FLAGS="-Xcompiler=-fpermissive" \
TORCH_CUDA_ARCH_LIST="8.0" \
python setup.py build_ext --inplace

Notes

• Sized for this card's exact budget. On other GPUs, rebuild with a different layer count from recipe/.
• The vision tower lives in pinned system RAM (vision_offload: true) to leave its VRAM to the experts.

License and credits

• Model: Qwen3.8-Flash-Next by Qwen, under the Qwen Community License 1.0 (see LICENSE).
• Quantization: every tensor here comes from turboderp/Qwen3.8-Flash-Next-exl3.
• Inference: ExLlamaV3 and TabbyAPI.