Model overview
GGUF quants of nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16 for llama.cpp: a 74B Mamba-2 / Transformer hybrid with LatentMoE (9B active) and a C-RADIO v4-H vision encoder. NVIDIA's own GGUF is a single 2-bit file trained for that precision (the 24 GB QAD checkpoint). These files are quantized from the full-precision BF16 weights instead, at 3 to 8 bits, for machines with more memory: 2 files from 28 to 45 GiB, plus the vision projector. The routed experts are ~89% of the weights, so every file runs on a GPU of any size by keeping some experts in system RAM. Rows: total memory you need = VRAM + RAM. Pick the largest file whose RAM column fits your machine.
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：other。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。