MagneticLab/Qwen3.8-Flash-Next-NVFP4

Qwen3.8-Flash-Next — NVFP4

An NVFP4 quantization of Qwen/Qwen3.8-Flash-Next that runs on one DGX Spark (GB10, 121 GiB unified memory) at the model's full native 262,144-token context, with tool calling and vision intact.

The upstream recipe asks for two GB300s (TP2 validated, TP4 recommended). This build fits a single 121 GiB node by quantizing the routed experts to NVFP4 and serving the 51.2B-parameter n-gram (PLE) table from a quantized sidecar that is mmapped read-only from disk.

Everything below was measured on real hardware, not estimated.

Get it

Read this before you start the download. 99 GB of the checkpoint is a duplicate of the PLE table that is never read at runtime — it gets stubbed at load time and served from the sidecar instead. Skip it and you download 101 GB instead of 200 GB, with an identical running model.

Lean download — recommended:

hf download MagneticLab/Qwen3.8-Flash-Next-NVFP4 \
  --local-dir /opt/models/Qwen3.8-Flash-Next-NVFP4 \
  --exclude "qwen-nvfp4-modelopt/model-0000[6-9]-of-00131.safetensors" \
  --exclude "qwen-nvfp4-modelopt/model-000[12]?-of-00131.safetensors" \
  --exclude "qwen-nvfp4-modelopt/model-0003[0-6]-of-00131.safetensors"

Those three patterns cover exactly shards 6-36 and nothing else. Everything not matched — the other 100 shards, both index files, the configs, the sidecar, the overlays — is downloaded.

Then point the model at the trimmed index:

cd /opt/models/Qwen3.8-Flash-Next-NVFP4/qwen-nvfp4-modelopt
mv model.safetensors.index.json.trimmed model.safetensors.index.json

If you would rather not trust a glob, download everything and delete the block afterwards — see "Drop 99 GB now" below.

Full download — 200 GB, if you would rather not depend on the overlay staying compatible with future images:

hf download MagneticLab/Qwen3.8-Flash-Next-NVFP4 \
  --local-dir /opt/models/Qwen3.8-Flash-Next-NVFP4

Everything needed to serve the model is in this one repository — checkpoint, PLE sidecar and the vLLM overlays.

What is in this repository

Path	Size	What it is
qwen-nvfp4-modelopt/	186 GB	Main checkpoint, 131 shards, NVFP4 routed experts
ple-sidecar/ples_nvfp4/	28.8 GB	Quantized PLE n-gram table, 128 shards
qwen-nvfp4-modelopt/model.safetensors.index.json.trimmed	30 MB	Optional index listing only the 100 shards actually needed

| ple-ref/ | 230 MB | vLLM overlay files (see credits) + a one-shard format sample | | config.json | 5 KB | A copy of the checkpoint's config, at the root. The Hub counts downloads by watching for config.json in the repository root; without it this repo shows "Downloads are not tracked". Serving uses the copy inside qwen-nvfp4-modelopt/ — the root one is bookkeeping, ignore it. |

ple-ref/ holds worker_image_quant.py and ple_layer_quant.py — the two files that teach vLLM to read a quantized PLE table instead of holding it in BF16 (95 GB of host RAM). They are not my work: they come from primitive-ai/Qwen3.8-Flash-Next-PLE-quant, Apache-2.0, and are bundled here so that one hf download gives you a working set. If you want the newest version, take them from the source repository.

Already downloaded everything? Drop 99 GB now

The checkpoint carries a duplicate of the PLE table that is stubbed at load time and served from the sidecar instead — so 31 of the 131 shards are never read. Swap in the trimmed index and delete those shards to go from 186 GB to 84 GB:

cd qwen-nvfp4-modelopt
python3 - <<'PY'
import json, os, glob
need = set(json.load(open("model.safetensors.index.json.trimmed"))["weight_map"].values())
for p in glob.glob("*.safetensors"):
    if os.path.basename(p) not in need:
        os.remove(p)
PY
mv model.safetensors.index.json.trimmed model.safetensors.index.json

Verified equivalent: weights in memory 74.34 GiB and KV 411,496 tokens come out identical either way, and a needle-in-haystack test at 82k prompt tokens passes on the trimmed build. Keep the full version if you would rather not depend on the overlay staying compatible with future images — a full checkpoint still has the table, a trimmed one fails to load without the stub.

Serving it

Image: vllm/vllm-openai:qwen38-flash-next (has linux/arm64).

docker run -d --name qwenflash --gpus all --ipc=host --network host \
  --ulimit memlock=-1 --ulimit stack=67108864 \
  -v /opt/models/Qwen3.8-Flash-Next-NVFP4/ple-ref/worker_image_quant.py:/usr/local/lib/python3.12/dist-packages/vllm/v1/ple_offload/worker.py:ro \
  -v /opt/models/Qwen3.8-Flash-Next-NVFP4/ple-ref/ple_layer_quant.py:/usr/local/lib/python3.12/dist-packages/vllm/models/qwen3_8_flash_next/nvidia/ple_layer.py:ro \
  -v /opt/models/Qwen3.8-Flash-Next-NVFP4/ple-sidecar/ples_nvfp4:/ples_nvfp4:ro \
  -v /opt/models/Qwen3.8-Flash-Next-NVFP4/qwen-nvfp4-modelopt:/model:ro \
  -e VLLM_PLE_QUANT_DIR=/ples_nvfp4 \
  -e VLLM_PLE_CPU_OFFLOAD=1 \
  -e VLLM_PLE_OFFLOAD_READY_TIMEOUT=3600 \
  vllm/vllm-openai:qwen38-flash-next \
  --model /model \
  --served-model-name Qwen3.8-Flash-Next-NVFP4 \
  --quantization modelopt_fp4 \
  --distributed-executor-backend mp \
  --tensor-parallel-size 1 \
  --kv-cache-memory=10737418240 \
  --max-num-seqs 8 \
  --max-model-len 262144 \
  --max-num-batched-tokens 8192 \
  --enable-prefix-caching \
  --no-enable-flashinfer-autotune \
  --enable-auto-tool-choice --tool-call-parser qwen3_xml \
  --reasoning-parser qwen3 \
  --port 8000

Then warm the sidecar — otherwise your first requests fault to NVMe:

cat ple-sidecar/ples_nvfp4/* > /dev/null

Startup takes 11-14 minutes cold, most of it streaming weights from NVMe.

Four things that will waste your afternoon

--distributed-executor-backend mp is load-bearing. Without it the PLE offload worker never starts, and the server sits there answering /health with 200 while never becoming usable.

Do not pass --gpu-memory-utilization. On a Spark the pool is unified; at 0.50 the engine sees ~60 GB, and you get NVRM: NV_ERR_NO_MEMORY after the weights are already loaded. Leave the flag off entirely and let vLLM size itself from --kv-cache-memory.

Do not cap container memory either, even though the upstream docs suggest it for the BF16 disk path. Same reason.

--kv-cache-memory has a floor of ~6.36 GiB at the full 262,144 context — the engine refuses to start below it and tells you the exact number. 4 GiB fails; 8 and 10 GiB work.

Measured performance

One DGX Spark, MTP off, sidecar warm, node freshly booted:

Concurrency	Aggregate	Per stream
1	16.9 tok/s	16.9
2	31.7	15.9
4	54.5	13.6
8	87.2	10.9

Prefill: 12.3 s at 25k tokens, 41.1 s at 99k, 70.4 s at 185k. Needle-in-haystack passes at 8k / 25k / 99k / 185k.

Memory at rest: 94-96 of 121.7 GiB (weights 75.9, KV 10.0, activations ~2), leaving 26-27 GiB of page cache for the sidecar. Swap use of a few GiB is normal here, not a fault — the n-gram table is mmapped read-only by design, so the kernel pages it in and out.

Do not enable MTP

The checkpoint ships an MTP module and it is tempting. A controlled sweep — four configs back-to-back, one script, identical prompts, sidecar warmed before each — says leave it off:

config	1 stream	8 streams
MTP-3	15.9	77.2
MTP-2	15.8	79.9
MTP-1	15.7	78.2
MTP off	16.1	81.6

Turning it off wins on every measure: +5.7% aggregate at 8 streams, 7 GiB freed to page cache, and KV capacity up from 345,746 to 411,496 tokens. Decode on a Spark is memory-bandwidth bound, so speculation competes with the page cache for the same pool.

Sampling and reasoning effort

Per the official Qwen card: thinking mode temperature=1.0, top_p=0.95, top_k=20; non-thinking temperature=0.7, top_p=0.80, top_k=20, presence_penalty=1.5. Thinking is on by default.

reasoning_effort accepts low, medium, xhigh — there is no none, the server returns HTTP 400. Same agentic prompt, both answers coherent:

effort	tokens	time	content
low	3,144	189 s	11,203 chars
xhigh	10,267	620 s	34,332 chars

xhigh costs 3.3x the tokens and time at the same throughput. Pick by how much chain-of-thought you want to pay for, not by expected quality.

Give it room to answer. A hard prompt with thinking on and max_tokens=4096 returns empty content — the whole budget goes to reasoning. Use 8192+, or reasoning_effort=low, or enable_thinking=false for routine steps. This is a budget issue, not quantization damage.

What could not be quantized

Only the routed experts. vllm/models/qwen3_8_flash_next/nvidia/model.py checks for the literal string modelopt_fp4, so a MIXED_PRECISION checkpoint loads as modelopt_mixed and the quant path is skipped entirely. GatedDeltaNet projections also fuse into MergedColumnParallelLinear, which per-tensor scales do not fit. FP8 for GDN and the shared experts was tried and reverted: it needs a vLLM code change, not a checkpoint format change.

About the sidecar

4-bit is the published floor for this table (per Unsloth) — do not go below it. This sidecar sits at that floor with worst_shard_rel_err 0.0896, against 0.1493 for the reference build, because it was quantized from the true BF16 release rather than from the FP8 one.

Two things not to try, both measured:

Do not mlock the sidecar. Pinning all 28.1 GiB made 8-stream throughput worse — 52.9 down to 42.0 tok/s — with swap doubling and free memory down to 4 GiB. The kernel loses its room and starts evicting the engine's own working pages instead.

Do not bother optimizing the dequant path. A rewritten gather (buffer slices instead of torch.stack, int32 indices, view+broadcast instead of repeat_interleave) is 26% faster on 2048-row batches, slower on small ones, and worth about 1% end to end — the n-gram table is one layer out of 48.

Reboot the node instead. After a chain of restarts throughput drops by roughly a quarter and swap climbs; a reboot restores it. Measured the same evening: 48.8 tok/s at 8 streams, 50.8 after warming the sidecar, 65.6 right after a reboot. Warming is worth ~4%, rebooting ~30%.

Vision and video

This is a multimodal build — the vision tower is not quantized and not stripped. vision_config, the image and video token ids and both preprocessor configs all survive quantization, and the launch command above needs no extra flags for it.

Verified working: the model correctly identified the colour of a test image passed as a base64 data URL through the standard image_url content block. Video input is wired the same way (video_token_id is present, video_preprocessor_config.json ships with the checkpoint) but I have not put a real clip through it, so treat video as untested rather than broken.

Two things to expect:

• The log prints ERROR lines about min_frames / max_frames not being documented in Qwen3VLVideoProcessorInitKwargs. They are harmless transformers warnings mislabelled as errors, and they appear on the unquantized model too.
• MTP does not support external multimodal embeddings shows up if you enable speculation. Since the measurements above say to leave MTP off anyway, this is not a conflict in practice.

Give vision requests room to answer: at max_tokens=60 a simple "what colour is this" came back with empty content because the whole budget went to reasoning. 800 was plenty.

License

The weights in this repository are derived from Qwen/Qwen3.8-Flash-Next and stay under the Qwen Community License 1.0 — full text. Quantizing does not change that. Two clauses are worth reading before you deploy:

• The copyright and permission notice must travel with any copy or substantial portion.
• If you run a Model as a Service or an AI Work Assistant business, you need a separate licence from Qwen. Their own definitions: "Model as a Service" is giving a third party access to inference or fine-tuning, e.g. via an API or a hosted endpoint; "AI Work Assistant" is an independent AI product primarily aimed at AI-assisted coding or office productivity.

Questions about the licence itself go to Qwen (model-business@notice.qwencloud.com), not to me.

The two files in ple-ref/ are separate work by primitive-ai under Apache-2.0 — see credits.

Credits

• Qwen for the base model
• primitive-ai for the PLE quantization approach and the two vLLM overlay files this build depends on
• Claude Opus 5 (Anthropic) did the quantization work and the inference tuning: producing the NVFP4 checkpoint and the PLE sidecar, finding the flag combination that actually serves this model on one node, and running the measurements quoted throughout this card — including the controlled MTP sweep that overturned an earlier claim of its own, and the two dead ends (mlock, dequant rewrite) documented above so nobody repeats them.