MODEL PROFILE

Qwen3.8-Flash-Next-cmf

infosave · Qwen3.8-Flash-Next-cmf

Qwen3.8-Flash-Next — a 125B hybrid mixture-of-experts model, 6B active per token (36 Gated DeltaNet + 12 sparse-attention layers, 512 experts per layer, 51B n-gram table, 262k context, thinking mode) — as CMF files, text only. It runs with `cortiq`, a Rust engine with no Python. Experts stream from the memory-mapped file as tokens need them, so the default 77 GB file runs on one GPU (measured from a 12 GB VRAM budget up to a whole 32 GB card) with the rest in system RAM. **RTX 3090 (24 GB), cortiq 0.8.15: 49.1 tok/s decode; first token after a 1,000-token prompt in 9.9 s.** RTX 5090 (cortiq 0.8.8): 81.7 tok/s, 108.6 with speculative decoding. Use cortiq 0.8.15 or later (prebuilt binaries). Without `--no-think` the model reasons first; raise `--max-tokens` (default 256). Each `cortiq run` loads the model again (10–30 s to the first token); for chat, keep it loaded with `cortiq serve`.

Text generationlicense:other
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/30 21:15:53
OVERVIEW

Model overview

Qwen3.8-Flash-Next — a 125B hybrid mixture-of-experts model, 6B active per token (36 Gated DeltaNet + 12 sparse-attention layers, 512 experts per layer, 51B n-gram table, 262k context, thinking mode) — as CMF files, text only. It runs with `cortiq`, a Rust engine with no Python. Experts stream from the memory-mapped file as tokens need them, so the default 77 GB file runs on one GPU (measured from a 12 GB VRAM budget up to a whole 32 GB card) with the rest in system RAM. **RTX 3090 (24 GB), cortiq 0.8.15: 49.1 tok/s decode; first token after a 1,000-token prompt in 9.9 s.** RTX 5090 (cortiq 0.8.8): 81.7 tok/s, 108.6 with speculative decoding. Use cortiq 0.8.15 or later (prebuilt binaries). Without `--no-think` the model reasons first; raise `--max-tokens` (default 256). Each `cortiq run` loads the model again (10–30 s to the first token); for chat, keep it loaded with `cortiq serve`.

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

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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

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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。