Nemotron 3 Ultra
NVIDIA's frontier MoE, 550B total parameters with ~55B active per token, built for enterprise-scale reasoning and code at datacenter scale.
By NVIDIA · United States
Updated 2026-09-15
When to pick this model
- Enterprise datacenter deployments with multi-GPU H100/MI300 clusters
- Frontier-level reasoning and code generation at scale
- Teams standardized on NVIDIA's Open Model License
- Workloads where MoE sparsity offsets the massive total parameter count
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 319 GB |
| Q5_K_M | 390 GB |
| Q8_0 | 588 GB |
| FP16 (no quantization) | 1100 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Nemotron 3 Ultra is server-class even at Q4_K_M (319 GB). Stepping up to Q8_0 nearly doubles the footprint to 588 GB, and unquantized FP16 weights take 1100 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Nemotron 3 Ultra needs roughly 715 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 1.5 tokens/sec on entry-level GPUs, on the order of 2.5 tokens/sec on a mid-range card, and up to 5 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Nemotron 3 Ultra to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Nemotron 3 Ultra |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 319 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 319 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 319 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Does not fit — needs 319 GB at Q4_K_M |
| 32 GB | RTX 5090 | Does not fit — needs 319 GB at Q4_K_M |
Which hardware should you buy to run Nemotron 3 Ultra?
To run Nemotron 3 Ultra locally at Q4, you need ~319 GB for Q4 weights alone. Hardware option to compare: BOSGAME M5 128GB / 2TB (Ryzen AI Max+ 395). This model exceeds the practical GPU memory of this mini PC. Choose a smaller model or larger infrastructure.
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Strengths
- Frontier scale (550B total, 55B active)
- MoE sparsity keeps compute cost closer to a dense 55B model
- Native 128K context
- NVIDIA Open Model License permits commercial use
Limitations
- ~319GB VRAM at Q4 requires multi-GPU H100/MI300 or a dedicated server
- Gated on Hugging Face (click-through access required)
- Out of reach for consumer hardware
Typical workloads
In our catalog grid, Nemotron 3 Ultra is filed under Enterprise Datacenter, Reasoning, Code — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: code generation and review (pair it with an editor integration like Continue.dev or Cline); multi-step reasoning and math-flavoured tasks.
The 125k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. It ships under the NVIDIA Open Model License license — commercial use is generally possible but read the specific terms before embedding it in a product.
Architecture & training
Architecture: Mixture-of-Experts · 550B total parameters · ~55B active per token · 128K context
Training: NVIDIA's Nemotron 3 family. Frontier variant above Nemotron 3 Super (120B). Optimized for reasoning, code, and agents.
NVIDIA's largest Nemotron — frontier reasoning and code performance strictly for well-resourced enterprise datacenter deployments.
Quick start
Install the runtime for your system: Windows, macOS or Linux. Check the exact model tag or GGUF quantization below; catalog IDs are not necessarily Ollama tags.
Start at 4096 tokens of context, then use ollama ps to check GPU/CPU placement. A default download may use a different quantization from the configurator’s memory estimate. Keep the free setup working before considering a kit.
ollama pull nemotron-3-ultraOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Nemotron 3 Ultra need?
At the recommended Q4_K_M quantization, Nemotron 3 Ultra needs about 319 GB of VRAM. Q8_0 takes 588 GB, and unquantized FP16 weights take 1100 GB.
Can Nemotron 3 Ultra run without a GPU?
Yes — with roughly 715 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Nemotron 3 Ultra support?
Nemotron 3 Ultra supports a 125k-token context window (128,000 tokens).
Can I use Nemotron 3 Ultra commercially?
Nemotron 3 Ultra ships under the NVIDIA Open Model License license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.
How fast is Nemotron 3 Ultra on consumer hardware?
Our compatibility engine estimates on the order of 2.5 tokens/sec on a mid-range GPU and up to 5 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Nemotron 3 Ultra should I download first?
Start with Q4_K_M (319 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It does not fit a single 24 GB consumer card — plan for multi-GPU or server hardware.