Model overview
**A 27B model with its whole trained context window, at q8_0 KV precision, on one RTX 4070 12 GB. 78 tok/s of new text at 32k tokens of context (175 on code), 69 at 64k, 61 at 112k, and the window keeps going to 262k. Lossless speculative decoding at every depth. A server that answers the requests agents and apps actually send. One command on Windows.** This is PrismML's Ternary Bonsai 2 27B (1.58-bit ternary `PTQ1_0`, 5.9 GB, Apache 2.0), the same weights, nothing re-quantized, with its MTP draft head grafted back on (byte-identical trunk, proof below) and the serving stack from github.com/professorpalmer/bonsai-ada-surgery: a tiered KV cache that puts the first ~119k positions in VRAM and the rest in pinned system RAM with bit-identical output, MTP drafting that pays at every depth, harness-proofing for the apps that send `effort: "high"` or tiny output caps, and an optional server-side layer (exact API cards, an API check, a sandboxed Python tool) that lifts the model's coding and agentic scores without touching the weights. Measured 2026-10-08 (`bench/depth_table.sh`), a fresh prompt at each depth: 400-token greedy answers with the default drafting (lookup + MTP head); new text = an essay, code = a Python module; prefill = cumulative for the whole prompt; display on the CPU's iGPU. Every number has a receipt in the repository (`docs/Q8_FULL_CONTEXT.md`, `docs/RECEIPTS.md`, `docs/QUALITY.md`).
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。