MODEL PROFILE

Qwen3.8-27B-Flash-MTP-GGUF

SuperLogic · Qwen3.8-27B-Flash-MTP-GGUF

No rented API, no cloud, no datacentre GPU. A 27B-parameter model, ternary-quantised, with a self-speculative draft head packed into the same file — running on a consumer laptop GPU, at interactive speed. Measured on an **RTX 4060 Laptop, 8 GB**, with thinking off, driving `llama-server` from this project's fork in real development work. Treat these as the usual range rather than a guarantee: how fast you land depends on how much of your output is text an ngram drafter can predict, which is why code and editing beat open-ended prose here. Your own numbers will land somewhere in this neighbourhood. **88K of context, fully in VRAM.** With `q3_0` — a 3-bit K/V cache, 14 KB per token against q4_0's 18 KB — the same 8 GB card keeps an 88K-token context entirely in VRAM. As far as we know, this is the first time a 27B-class model runs a full long-context agent session at this speed on consumer hardware.

Text generationcode-generationlicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/31 04:54:27
OVERVIEW

Model overview

No rented API, no cloud, no datacentre GPU. A 27B-parameter model, ternary-quantised, with a self-speculative draft head packed into the same file — running on a consumer laptop GPU, at interactive speed. Measured on an **RTX 4060 Laptop, 8 GB**, with thinking off, driving `llama-server` from this project's fork in real development work. Treat these as the usual range rather than a guarantee: how fast you land depends on how much of your output is text an ngram drafter can predict, which is why code and editing beat open-ended prose here. Your own numbers will land somewhere in this neighbourhood. **88K of context, fully in VRAM.** With `q3_0` — a 3-bit K/V cache, 14 KB per token against q4_0's 18 KB — the same 8 GB card keeps an 88K-token context entirely in VRAM. As far as we know, this is the first time a 27B-class model runs a full long-context agent session at this speed on consumer hardware.

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.

Source / channelInputOutputBilling unitCollected at
No verified prices yet. Check the source channel.

Estimate monthly base token costs

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Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.

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?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

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:apache-2.0。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。