MODEL PROFILE

Qwen3.8-Flash-Next-REAP-288-MLX-4bit

sh0wie · Qwen3.8-Flash-Next-REAP-288-MLX-4bit

Qwen3.8-Flash-Next with 288 of 512 experts per MoE layer, pruned with REAP saliency calibrated on the quantized weights, on the machine that serves them. It is a 180B-class MoE that runs on a 128 GB Mac for Claude Code-style agentic workloads and scores 91.5% on HumanEval. Decode is runtime-dependent: ~28 tok/s on stock mlx-vlm today, and 37 to 65 tok/s on the pmlx engine that ships with the tiered release (see "Decode speed" for the full breakdown). It supports MTP speculative decoding out of the box. Resident memory is ~68 GB as shipped, or ~39 GB when the n-gram table is streamed from NVMe (see "Running it"). For the ~39 GB streamed footprint, run under omlx v0.6.4 or newer. omlx offloads the n-gram table to NVMe automatically when the full model would not fit under the memory ceiling, so the build serves in about 39 GB resident. mlx-vlm runs the same build today at about 68 GB resident, with the n-gram table in memory. It streams to about 39 GB once PR #2045 (external PLE storage) lands in a release, since this repo already ships the `ple-store.json` manifest and `ple_storage` config that path reads. mlx-vlm needs `qwen4_exp` MTP support (git main after 2026-08-27, or any release that includes it):

Visual QAlicense:other
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/11/2 19:16:28
OVERVIEW

Model overview

Qwen3.8-Flash-Next with 288 of 512 experts per MoE layer, pruned with REAP saliency calibrated on the quantized weights, on the machine that serves them. It is a 180B-class MoE that runs on a 128 GB Mac for Claude Code-style agentic workloads and scores 91.5% on HumanEval. Decode is runtime-dependent: ~28 tok/s on stock mlx-vlm today, and 37 to 65 tok/s on the pmlx engine that ships with the tiered release (see "Decode speed" for the full breakdown). It supports MTP speculative decoding out of the box. Resident memory is ~68 GB as shipped, or ~39 GB when the n-gram table is streamed from NVMe (see "Running it"). For the ~39 GB streamed footprint, run under omlx v0.6.4 or newer. omlx offloads the n-gram table to NVMe automatically when the full model would not fit under the memory ceiling, so the build serves in about 39 GB resident. mlx-vlm runs the same build today at about 68 GB resident, with the n-gram table in memory. It streams to about 39 GB once PR #2045 (external PLE storage) lands in a release, since this repo already ships the `ple-store.json` manifest and `ple_storage` config that path reads. mlx-vlm needs `qwen4_exp` MTP support (git main after 2026-08-27, or any release that includes it):

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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Sources & traceability

许可证:other。模型介绍与第三方评测均以原来源的具体版本和方法为准。