Nemotron (Nvidia)
B Tier · 7.8/10
Nvidia's open-weights family -- hybrid Mamba-Transformer MoE architecture, optimized for efficient reasoning on Nvidia hardware
Score Breakdown
Benchmark Scores
Benchmarks for Nemotron 3 Ultra (253B)
| Benchmark | Description | Score | |
|---|---|---|---|
| MMLU-Pro | Harder multi-subject reasoning | 79.8% | |
| GPQA Diamond | Graduate-level science questions | 70.5% | |
| AIME 2025 | 84.5% | ||
| HumanEval | Python code generation | 89.6% | |
| MMLU (Llama-Nemotron 70B) | 88.4% |
Last updated: 2026-04-13
Personality & Tone
Nvidia's enterprise-tuned model
Tone: Polished, safe, and aimed at business use. Nemotron responses feel engineered -- consistent length, clear structure, little snark -- like it was optimized for predictability rather than personality.
Quirks: Heavy RLHF for workplace-friendly outputs. Great for enterprise deployment; less interesting for open-ended chat. Runs best on Nvidia stacks, which is the whole point -- you pay (or don't) for that optimization.
The Good and the Bad
What we like
- +Hybrid Mamba-Transformer architecture dramatically reduces memory per token at long context
- +Nemotron 3 Super activates only 3.6B params -- runs on 8 GB VRAM with top-tier reasoning quality
- +Nvidia-optimized inference: first-class TensorRT-LLM, vLLM, and NIM deployment
- +Llama-Nemotron 70B scores MMLU 88.4% -- within a point of GPT-4o on a model you can run locally
- +Permissive Nvidia Open Model License allows commercial deployment
What could be better
- −Mamba inference ecosystem is still catching up -- Ollama and llama.cpp support is partial
- −Not the absolute frontier on benchmarks -- DeepSeek, Qwen, Kimi outscore on most leaderboards
- −Smaller community than Llama/Qwen -- fewer fine-tunes available
- −Release cadence is slow compared to Chinese labs
Pricing
Self-hosted (Free)
- ✓NVIDIA Open Model License
- ✓Commercial use permitted
- ✓Weights on Hugging Face and NGC
API (build.nvidia.com)
- ✓Free tier for experimentation
- ✓NIM microservices for production
- ✓Pricing via Nvidia Cloud partners
System Requirements
Hardware needed to self-host. Min = smallest viable setup (usually heavy quantization). Max = full-precision / production-grade.
| Model variant | Min | Max |
|---|---|---|
| Nemotron 3 Super (31.6B total, 3.6B active Mamba-MoE)Mamba hybrid gives unusually low memory per token at long context | 8 GB VRAM Q4 (RTX 3070) | 1× A100 40 GB FP16 |
| Nemotron 3 Ultra (253B reasoning) | 128 GB RAM + 24 GB GPU (Q3) | 4× H100 FP8 |
| Llama-Nemotron 70B | 24 GB VRAM Q4 (RTX 3090/4090) | 1× H100 80 GB FP16 |
Known Issues
- Nemotron 3 now has dedicated sub-families for voice, retrieval, and safety beyond the original reasoning family. Published sub-lineup: (1) Nemotron Speech -- open-source ASR models, claimed 10x faster than class competitors at comparable WER; (2) Nemotron RAG -- multimodal embedding + reranker VLMs for enterprise retrieval; (3) Nemotron Safety -- Llama Nemotron Content Safety + Nemotron PII detector; (4) Nemotron 3 VoiceChat -- full-duplex voice agent in early access post-GTC 2026; (5) Nemotron 3 Content Safety guardrail. All released under the permissive Nvidia Open Model LicenseSource: Nvidia developer blog -- agents for reasoning, multimodal RAG, voice, and safety · 2026-04
- NEMOTRON COALITION: Announced at GTC March 2026, Nvidia leads an open-frontier coalition with Black Forest Labs, Cursor, LangChain, Mistral, Perplexity, Reflection AI, Sarvam, and Thinking Machines. Nemotron 4 (the first coalition model) has no public release date yet. Nemotron 3 Super/Ultra expected first half of 2026; Nemotron 3 Nano already shippedSource: Nvidia press release, Nemotron Coalition announcement · 2026-03
- Mamba-hybrid layers require custom CUDA kernels -- non-Nvidia hardware (Apple Silicon, AMD ROCm) has limited supportSource: Hugging Face discussions, GitHub issues · 2026-02
- Early Nemotron 3 Super quantizations below Q4 showed degraded reasoning quality vs. dense Llama at same bit-widthSource: Reddit r/LocalLLaMA · 2026-03
Best for
Teams running on Nvidia hardware (TensorRT-LLM, NIM) who need efficient long-context reasoning. Nemotron 3 Super is a standout for its 8 GB VRAM footprint with strong reasoning.
Not for
Apple Silicon / AMD GPU users -- Mamba hybrid kernels are Nvidia-first. Also not ideal if you want maximum community support (use Llama or Qwen).
Our Verdict
Nemotron is Nvidia's bet that architecture innovation (hybrid Mamba-Transformer MoE) beats pure scale. The bet largely pays off: Nemotron 3 Super runs on a gaming GPU while posting reasoning scores that rival much larger dense models. If you're deployed on Nvidia hardware and need efficient long-context inference, Nemotron is the natural pick. If you're not on Nvidia or need absolute frontier quality, Qwen3 or DeepSeek are stronger options.
Sources
- Nvidia press release: Nemotron Coalition (accessed 2026-04-17)
- Nvidia press release: Nemotron 3 family of open models (accessed 2026-04-17)
- Nvidia blog: Nemotron 3 Super agentic AI (accessed 2026-04-17)
- Artificial Analysis Nemotron Ultra 253B (accessed 2026-04-17)
- Hugging Face nvidia collection (accessed 2026-04-17)
- Reddit r/LocalLLaMA Nemotron 3 discussion (accessed 2026-04-17)
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