MODEL PROFILE

Qwen3.8-Flash-Next-MLX-Serve-mixed-4-8bit

ddalcu · Qwen3.8-Flash-Next-MLX-Serve-mixed-4-8bit

> **Use Qwen3.8-Flash-Next-MLX-Serve-iQ-MLX-4.7bpw instead.** > It is this pack re-quantized with an importance matrix collected on the bf16 > model: same layout, same 75 GB, same bytes read per token, closer to bf16 > (KLD median 0.0052 vs 0.0066, top-1 88.6% vs 87.9% against bf16's own > logits). This pack stays up for existing installs but gets no more updates. > > Switching from this pack? `ngram_table.bin` (32 GB) is identical in both. > Move it into the new pack's folder before downloading and the download > skips it: > > mlx-serve pack of Qwen/Qwen3.8-Flash-Next, the Qwen4 preview architecture (`model_type: qwen4_exp`). Runs on a 128 GB Mac with about 75 GB resident. Includes the MTP head and the vision tower (image and video input). Measured on an M4 Max 128 GB with mlx-serve 26.8.11: ~75 GB resident, decode ~60 tok/s serial and 78 tok/s with MTP (`--mtp`; +41% on code, a few percent slower on prose), prefill ~730 tok/s, a needle at 24.8k tokens recovered with sparse attention engaged. Prefix cache on, images included.

Text generationlicense:other
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/30 08:42:25
OVERVIEW

Model overview

> **Use Qwen3.8-Flash-Next-MLX-Serve-iQ-MLX-4.7bpw instead.** > It is this pack re-quantized with an importance matrix collected on the bf16 > model: same layout, same 75 GB, same bytes read per token, closer to bf16 > (KLD median 0.0052 vs 0.0066, top-1 88.6% vs 87.9% against bf16's own > logits). This pack stays up for existing installs but gets no more updates. > > Switching from this pack? `ngram_table.bin` (32 GB) is identical in both. > Move it into the new pack's folder before downloading and the download > skips it: > > mlx-serve pack of Qwen/Qwen3.8-Flash-Next, the Qwen4 preview architecture (`model_type: qwen4_exp`). Runs on a 128 GB Mac with about 75 GB resident. Includes the MTP head and the vision tower (image and video input). Measured on an M4 Max 128 GB with mlx-serve 26.8.11: ~75 GB resident, decode ~60 tok/s serial and 78 tok/s with MTP (`--mtp`; +41% on code, a few percent slower on prose), prefill ~730 tok/s, a needle at 24.8k tokens recovered with sparse attention engaged. Prefix cache on, images included.

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

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

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Estimate monthly base token costs

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

Do open weights permit free commercial use?

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Are these real-time billing prices?

These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.

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。