Model overview
INT4 (W4A16) AutoRound quantization of Qwen/Qwen3.8-27B — **18 GB**, with the **MTP speculative-decoding head quantized and working**. The MTP head is quantized along with the model, so `--speculative-config` in vLLM works out of the box and roughly **doubles decode throughput** (measured +98 % on temp-0 prose vs the same GPU without MTP; draft acceptance 41–67 % depending on content). At the time of upload, the available 4-bit options either dropped the MTP tensors (no speculative decoding) or kept hundreds of tensors in bf16 (28 GB — no longer fits a 24–32 GB GPU next to a useful KV cache). This one follows the recipe that served Qwen3.6-27B in production for months:
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.
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 / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
| No verified prices yet. Check the source channel. | ||||
Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
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。
View full indexed rankings →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。