Model overview
Prism ML Website | Whitepaper | Demo & Examples | Colab Notebook | Discord > **14.1x** smaller than FP16 | **6.2x** faster on RTX 4090 | **4-5x** lower energy/token - **1.15 GB** parameter memory (down from 16.38 GB FP16) — fits on virtually any device with a GPU - **End-to-end 1-bit weights** across embeddings, attention projections, MLP projections, and LM head - **GGUF Q1_0 (g128)** format with inline dequantization kernels — no FP16 materialization - **Cross-platform**: CUDA (RTX/datacenter), Metal (Mac), Android, CPU - **Competitive benchmarks**: 70.5 avg score across 6 categories, matching full-precision 8B models at 1/14th the size - **MLX companion**: also available as MLX 1-bit g128 for native Apple Silicon inference
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。