Qwen3.8-Flash-Next-Uncensored — EXL3 4.05 bpw

EXL3 quantization of orcarouter/Qwen3.8-Flash-Next-Uncensored, an abliterated (refusal-removed) build of Qwen/Qwen3.8-Flash-Next. Built with ExLlamaV3 1.4.6.


Size on disk	102 GB
Language model	4.05 bpw
`lm_head`	6 bpw
MTP layers	8 bpw
n-gram (PLE) tables	6 bpw
Vision tower	unquantized (16-bit)
VRAM when loaded	~66 GB (FP16 cache) / ~71 GB (8-bit cache, 1.4.8)

Config

model:
  model_name: Qwen3.8-Flash-Next-Uncensored-exl3-4bpw
  cache_size: 16384
  ngram_ram: true
  tensor_parallel: false  # true needs ExLlamaV3 1.5.1+

draft_model:
  draft_mode: mtp

tensor_parallel needs ExLlamaV3 1.5.1 or later for Qwen4ExpForConditionalGeneration (QSA layers are not split). On earlier versions it fails with NotImplementedError: Tensor-parallel is not currently implemented for Qwen4ExpForConditionalGeneration and you must use autosplit. Not tested on my side.

Cache quantization needs ExLlamaV3 ≥ 1.4.8. Below that, cache_mode other than FP16 is rejected with QSA attention currently supports only the fp16 cache layer. On 1.4.8 "8,8" loads fine — but it buys nothing here: the KV cache is ~2 kB per token per attention layer, so a few hundred MB at 16k context. VRAM went up (66 → 71 GB) with the new expandable-segment allocator, and throughput moved <5%. Use FP16 unless you have a reason not to.

ngram_ram: true loads the n-gram table into system RAM instead of streaming it from disk per forward.

MTP is preserved; TabbyAPI logs Using main model MTP component for drafting on load.

Conversion

python convert.py \
  -i Qwen3.8-Flash-Next-Uncensored \
  -o Qwen3.8-Flash-Next-Uncensored-exl3-4bpw \
  -w /tmp/exl3-work \
  -b 4.05 -hb 6 -ngb 6 -mb 8 -vb 16 \
  -d 0,1,2,3 -v

Default calibration corpus, 250 rows x 2048 columns.

Measurements

4x RTX 4000 Ada (20 GiB, sm89), PCIe, no NVLink, 2x Xeon Gold 5318Y. ExLlamaV3 1.4.6 + TabbyAPI, autosplit, MTP on, FP16 cache. AIPerf, streaming, ignore_eos, seed 42, with warmup.

ISL 1000 / OSL 500 / concurrency 4 / 20 requests:

Metric	value
Request latency	25,508 ms
Inter-token latency	46.6 ms
Time to first token	2,484 ms
Output throughput	76 tok/s

Same run on 1.4.8 with an 8-bit cache: 26,129 ms / 47.1 ms / 2,620 ms / 73 tok/s.

ISL 8000 / OSL 200 / concurrency 2 / 12 requests:

Metric	value
Time to first token	12,724 ms
Active prefill	760 tok/s
Inter-token latency	37.3 ms
Request latency	19,957 ms

Single stream via chat.py: 34.5 tok/s with MTP (70% acceptance on code), 20.8 tok/s without.

Same box and tooling, my EXL3 6.0bpw of the dense Qwen3.8-27B-Uncensored gives 20,816 ms / 35.0 ms ITL / 91 tok/s on the first run.

20 requests per run, so gaps under ~5% are noise.

Quality

Not benchmarked at 4.05 bpw. Abliteration figures are orcarouter's, measured on the BF16 source, not on this quant.

Safety

Safety alignment has been substantially removed via abliteration. It will comply with requests the original Qwen3.8-Flash-Next refuses, and has no meaningful built-in guardrails. Add your own moderation layer before any deployment.

License

Qwen Community License 1.0, inherited from Qwen/Qwen3.8-Flash-Next.

Lygodactylus/Qwen3.8-Flash-Next-Uncensored-exl3-4bpw