Qwen3.8-Flash-Next REAP-288 Q8E (MLX, 8-bit experts on a 4-bit backbone)

On stock mlx-vlm the n-gram table is resident and this build is ~100 GB, which does not leave working room on a 128 GB Mac. The 70 GB figure needs a runtime that streams the table from NVMe: omlx v0.6.4+ does this today (see below).

Qwen3.8-Flash-Next with 288 of 512 experts per MoE layer, pruned with REAP saliency calibrated on the quantized weights, and the surviving expert tensors re-quantized to 8-bit. The backbone (attention, DeltaNet, norms, embeddings) stays at 4-bit, so all of the extra 32 GB over the plain 4-bit prune goes to expert precision. Loads on stock mlx-vlm with no patches.

• 180B-parameter class: 125B main model, 51B n-gram embedding table, 48 layers alternating Gated DeltaNet and Qwen sparse attention, each with a 288-expert MoE routing top-10
• Expert tensors (gate_proj/up_proj/down_proj of every MoE layer) at 8-bit, group size 64; everything else 4-bit (n-gram table group size 32), carried as per-module overrides in the quantization config
• Multimodal weights (vision tower) are intact but only text quality has been evaluated

Where this build sits

Be aware of what the extra bits buy: in our measurements the 8-bit experts scored at or below the plain 4-bit prune on HumanEval (90.9% vs 91.5%) and tied it on rare-name sampling reliability (9 of 10). The pruning error dominates the quantization error at this width, so for most users the recommended download is the smaller sh0wie/Qwen3.8-Flash-Next-REAP-288-MLX-4bit. This build exists for users who want expert-precision headroom anyway — the one place it led in our testing was a single conventions-adherence style evaluation that we have not replicated, so we publish it as an option, not a recommendation.

Run it

For the streamed ~70 GB footprint, run under omlx v0.6.4 or newer. It offloads the n-gram table to NVMe automatically when the full model would not fit under the memory ceiling.

brew install jundot/omlx/omlx
hf download sh0wie/Qwen3.8-Flash-Next-REAP-288-Q8E-MLX \
  --local-dir ~/models/Qwen3.8-Flash-Next-REAP-288-Q8E-MLX
# A ceiling below the ~100 GB full-resident size makes omlx stream the table
omlx serve --model-dir ~/models --memory-guard-gb 72

The server is OpenAI-compatible at http://localhost:8000/v1.

mlx-vlm runs the build today at ~100 GB resident, with the n-gram table in memory. mlx-vlm streaming lands once PR #2045 ships and we add a Q8E ple-store.json (the manifest is int4-only today; see "The NVMe n-gram mode"). mlx-vlm needs qwen4_exp MTP support (git main after 2026-08-27, or any release that includes it):

pip install git+https://github.com/Blaizzy/mlx-vlm.git

# One-shot generate
python -m mlx_vlm.generate \
  --model sh0wie/Qwen3.8-Flash-Next-REAP-288-Q8E-MLX \
  --prompt "Refactor this function to add input validation." \
  --max-tokens 512

# OpenAI-compatible server
python -m mlx_vlm.server \
  --model sh0wie/Qwen3.8-Flash-Next-REAP-288-Q8E-MLX --port 8080

Speculative decoding with the model's own MTP head, using the companion drafter sh0wie/Qwen3.8-Flash-Next-MTP-Drafter-MLX-bf16:

python -m mlx_vlm.generate \
  --model sh0wie/Qwen3.8-Flash-Next-REAP-288-Q8E-MLX \
  --draft-model sh0wie/Qwen3.8-Flash-Next-MTP-Drafter-MLX-bf16 \
  --draft-kind mtp \
  --prompt "..." --max-tokens 512

A note on speculative speed: the drafter's acceptance rate is healthy (~44-68% depending on sampling), but the net speedup depends on how cheaply your hardware runs the verification pass. M5-class GPUs report 1.5-2.6x; on M4 it is roughly break-even. Quality is unaffected either way, since the target model verifies every drafted token.

The NVMe n-gram mode

Per token the model reads only a few hundred bytes of the 51B n-gram table, so the table does not need to be resident. A runtime that streams it from NVMe drops resident memory from ~100 GB to 70 GB (MLX peak 70.4 GiB during a short greedy run), with logits identical to the in-memory path.

omlx v0.6.4+ does this today: it offloads the n-gram table automatically when the full model would not fit under the memory ceiling. The commands are in "Run it" above.

mlx-vlm gained the same capability through PR #2045 (external PLE storage), merged to main on 2026-08-28 and awaiting a tagged release. That path reads a ple-store.json manifest, which we currently ship only for the int4 build; a Q8E manifest is a follow-up. Until then, use omlx for the streamed footprint on this build. The kept-expert manifest ships here as reap_kept_experts.json, which makes the prune reproducible from the source conversion.

Provenance and what was fixed

• Qwen/Qwen3.8-Flash-Next: upstream weights
• Sawfwair/Qwen3.8-Flash-Next-MLX-4bit: MLX affine 4-bit conversion (group size 64; n-gram table group size 32)
• This build: REAP expert pruning 512 -> 288 per layer, calibrated on-device over ~686K tokens of agentic-coding traffic, with the surviving expert tensors re-quantized to 8-bit

Two defects of the source conversion are corrected in the weights, so no loader patches are needed: RMSNorm tensors stored un-centered (+1) are re-centered to the zero-centered convention the runtime's (1 + w) norm expects, and the n-gram table tensors plus their per-tensor quantization overrides are renamed shard_N -> shards.N to match the runtime module path. The 8-bit expert tensors and their per-module quantization overrides pass through untouched. Stock-runtime logits on this build match our patched-runtime reference (max abs diff 0.0 at the final prefill position).

Limitations

• Calibration reflects one team's agentic-coding distribution. Retention numbers should not be read as general-domain; domains far from code may degrade more.
• Single-run evaluations, no confidence intervals. Differences of a point or two between neighboring builds are within noise.
• Rare-name sampling reliability is 9/10, not 10/10; pruned models benefit from clearing context after a visible garbled name, since a corruption that enters the context conditions later turns.
• Vision input is untested after pruning.

License

Qwen Community License 1.0, inherited from the base model; see LICENSE.