Nemotron 3.5 Super VL 120B-A12B NVFP4
NVIDIA Nemotron 3.5 Super VL is a 120B-total, 12B-active vision-language MoE for reasoning, coding, tool use, document understanding, and video understanding. This official NVFP4 checkpoint supports up to a 1M-token context and targets multi-GPU NVIDIA deployments.
Task Fit
Not marked for typed decisions in the current library.
Tool use, repo work, terminal workflows, and coding benchmarks.
Code generation, debugging, refactoring, and benchmark signal.
General writing, Q&A, and assistant use.
Document QA benefits from long context and instruction following.
Image or visual understanding, not necessarily image generation.
Not marked for image generation in the current library.
Not marked for video generation in the current library.
Not marked for voice in the current library.
Source Confidence
Variants and Quant Artifacts
Choose the artifact first; hardware fit follows from RAM, VRAM, format, and runtime.
| Quant | Format | Quality | Min RAM | Reco RAM | Runtime | Action |
|---|---|---|---|---|---|---|
| NVFP4 | safetensors | compact | 80GB | 96GB | transformers, vllm, sglang | Plan with this |
Recommended Hardware
Lowest estimated 5-year cost that can run this model.
Plenty of fast-memory headroom for this model.
Highest local performance signal among compatible hardware.
Benchmarks
No benchmark data is available for this model yet.
Source and Review
Execution evidence
Run Nemotron 3.5 Super VL 120B-A12B NVFP4 with a documented recipe
Recipes connect hardware, a model artifact, tools, settings, verification, and a reportable result.
No verified recipe is linked to this record yet.
Compatibility estimates remain available in the planner. A recipe appears here only after its exact stack and verification protocol are documented.
Similar Models
DeepSeek V4.1 Flash is DeepSeek's MIT-licensed open-weight multimodal MoE for reasoning, coding, agents, and native image understanding. The complete checkpoint is approximately 763B parameters, including a 552B backbone; its Causal Encoder-Decoder architecture activates 8B parameters during input prefill and 16B during output decoding. CSA2 and FP4 KV caching reduce long-context cache cost. The model supports a 1M-token context window and is available through official FP8 weights, the DeepSeek API, and Ollama Cloud. It remains a data-center-class deployment despite the low active-parameter count.
DeepSeek V4.1 Flash 1017 is an updated MIT-licensed 763B-parameter multimodal MoE checkpoint. It keeps the V4.1 family's 1M-token context, coding, reasoning, tool use, and image understanding, while remaining a data-center-class deployment despite activating far fewer parameters per token.
Moonshot AI's Kimi K2.7 Code. Auto-imported from Hugging Face seed; review memory and benchmark details before using for final ranking.