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Model Library
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Nemotron 3.5 Lightning 30B A3BNemotron 3 Ultra 550B A55BNemotron 3 Nano Omni 30B A3B Reasoning BF16Nemotron 3 Super 120B A12B BF16Qwen3 Nemotron 235B A22B GenRMNemotron 3 Nano 30B A3B BF16Nemotron 3 Nano 30B A3B Base BF16
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  5. Nemotron 3 Ultra 550B A55B

Nemotron 3 Ultra 550B A55B

NVIDIA Nemotron 3 Ultra 550B A55B BF16 is a frontier-scale Mixture-of-Experts reasoning model designed for advanced agentic workflows, long-context analysis, and high-accuracy reasoning across code, mathematics, science, and multilingual tasks. It features 550B total parameters with 55B active, utilizing a hybrid LatentMoE architecture that combines Mamba-2, MoE, and attention layers with multi-token prediction for efficient inference. The model uses an 8,192 hidden size and 64 attention heads, activating 22 experts per token across 512 routed experts and a shared expert. Supporting up to a 1M token context window, it is optimized for complex reasoning, tool use, large-scale document analysis, and high-stakes retrieval-augmented generation workloads.

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About this model

BlogHF Model Card

NVIDIA Nemotron 3 Ultra 550B A55B BF16 is a frontier-scale Mixture-of-Experts reasoning model designed for advanced agentic workflows, long-context analysis, and high-accuracy reasoning across code, mathematics, science, and multilingual tasks. It features 550B total parameters with 55B active, utilizing a hybrid LatentMoE architecture that combines Mamba-2, MoE, and attention layers with multi-token prediction for efficient inference. The model uses an 8,192 hidden size and 64 attention heads, activating 22 experts per token across 512 routed experts and a shared expert. Supporting up to a 1M token context window, it is optimized for complex reasoning, tool use, large-scale document analysis, and high-stakes retrieval-augmented generation workloads.

Model Type

MoE LLM

Released

June 2, 2026

License: OpenMDW-1.1

Parameters

550B

Model ID
nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
Capabilities
Text GenerationInstruction FollowingReasoningMathematical ReasoningCode Generation+4

Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16). Released under OpenMDW-1.1. Further reading: Blog

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Hardware

B200

1,440 GB

Engine

vLLM
SGLang

Deployment code

INFERENCE COMMAND
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Performance under load

12k9.2k6.9k4.6k2.3k0163264128256512Total Tokens / secConcurrent requests
Tokens/s (total)

Benchmarks measured by Vultr on B200 · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.

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Shivam Sharma

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