Model overview
INT8 weight **and** activation (**W8A8**) quantization of **Qwen/Qwen3.8-27B** — a dense hybrid model (GatedDeltaNet linear-attention + periodic full-attention) with a native vision tower and an MTP head. ~30 GB, so it needs two 24 GB cards (tensor-parallel 2) but leaves a comfortable KV cache. Pick this over the INT4-W4A16 build when you want the fastest prefill and have the VRAM; pick INT4 when you want the fastest decode or a single-card fit. The **MTP head is included** (`mtp.safetensors`, bf16) and is listed in `quantization_config.ignore`, so vLLM loads it unquantized and speculative decoding works out of the box. Quantizers silently drop this head — if you re-quantize the base yourself, graft it back or spec-decode will accept 0 %.
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。