Model overview
**MTPLX: the fastest way to run Ternary Bonsai 2 27B on a Mac. Native multi-token-prediction speculative decoding on Apple Silicon, exact at any temperature.** Prism ML's Ternary Bonsai 2 27B is Qwen 3.8 27B rebuilt with ternary weights, and Prism ML reports it keeps 98.2 percent of the full model's benchmark average in 8.85 GB. On MTPLX it runs on GPU kernels written for it, with the Qwen 3.8 27B multi-token-prediction head drafting ahead. That makes it faster than the 4-bit Qwen 3.8 27B in half the memory. It reads images, and it runs on a Mac with 16 GB. Prism ML's own figure for Bonsai 2 27B on an M5 Max with MLX is 46.8 tok/s. On MTPLX the same chip decodes it at 64.4 tok/s after a 4,061-token prompt.
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。