Nemotron 3 Ultra 550B A55B
NVIDIA 推出的 Nemotron 3 Ultra 550B A55B 人工智能模型，具备 1M 上下文窗口，输入价格为 $1/百万 Token，输出价格为 $3/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
Technical Specifications
- Provider
- NVIDIA
- Category
- open-source
- Release Date
- 2026-06
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Configurable (Low / Med / High)
- Structured Outputs
- Yes
- Output Modalities
- text
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- nemotron-3-ultra-550b-a55b
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2028+)
- Production Rate Limits (RPM & TPM)
- Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
- Long-Context Pricing Tier (>128K / >200K)
- Flat rate ($1/1M in) across full 1000K window
- Prompt Cache Write & TTL Policy
- Cached input at $0.25/1M tokens
- Computer Use & Browser GUI Agent
- API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
- MCP & Parallel Tool Calling
- Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
- Web Search Grounding & Code Sandbox
- Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
- Fine-Tuning & Distillation Support
- Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token)
- Weights Precision & Quantization
- BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
- Minimum GPU VRAM Requirement
- 341 GB VRAM (4-bit Quantized) · 1183 GB VRAM (Native BF16)
- Supported Inference Engines
- vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI
- System Card & Safety Framework
- Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Nemotron 3 Ultra 550B A55B Cost, Context, Hardware & API Integration
For single-user chat, a $20/mo subscription caps your bill below this API volume.
Break-even point: ~121 requests/day equals $20/month.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to Nemotron 3 Ultra 550B A55B:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 64.5%
- GPQA Diamond
- 76%
- MMLU-Pro
- 87.5%
- HumanEval
- 95%
- Terminal-Bench
- 53%
- AIME
- 86%
- Long-Context Reasoning
- 89%
- Chatbot Arena Elo
- 1425pts
Strengths
- 由 NVIDIA 研发的高精度 Colossal Sparse MoE (550B Total / 55B Active) 架构，支持 1M 上下文窗口
- 极具竞争力的 API 定价（输入 $1/1M，输出 $3/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Limitations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Editorial Assessment
Nemotron 3 Ultra 550B A55B 是 NVIDIA 旗下极具代表性的模型，在性能、速度与 API 成本之间实现了卓越平衡。
Best for: 企业级 AI 自动化、代码研发、智能体（Agent）工作流以及 NVIDIA 生态深度集成
Compare Nemotron 3 Ultra 550B A55B
Frequently Asked Questions about Nemotron 3 Ultra 550B A55B
How much does the Nemotron 3 Ultra 550B A55B API cost per 1M tokens?+
Nemotron 3 Ultra 550B A55B by NVIDIA is priced at $1 per 1M input tokens and $3 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.25 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the Nemotron 3 Ultra 550B A55B pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in Nemotron 3 Ultra 550B A55B's context window?+
Nemotron 3 Ultra 550B A55B features a 1M tokens context window, which equals approximately 750,000 English words or ~2,000 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 65,536 tokens.
Can I run Nemotron 3 Ultra 550B A55B locally on my own GPU (Open Weights)?+
Yes, Nemotron 3 Ultra 550B A55B (550B (55B active MoE)) releases open weights that can be self-hosted locally or on private cloud GPUs using vLLM, Ollama, SGLang, or llama.cpp.
Does NVIDIA train on data sent to the Nemotron 3 Ultra 550B A55B API, and is commercial use allowed?+
100% Isolated when self-hosted on private GPUs or VPC. License tier: Open-Weights Research & Commercial License. Full commercial ownership of generated outputs. Supported deployment channels include: Self-Hosted GPU (vLLM / Ollama), Private VPC, or Managed Serverless API.
How does Nemotron 3 Ultra 550B A55B perform on coding and reasoning benchmarks?+
Nemotron 3 Ultra 550B A55B achieves a composite Intelligence Index of 60/100, scoring 64.5% on SWE-bench Verified, 76% on GPQA Diamond, 87.5% on MMLU-Pro, and 1425 pts on Chatbot Arena Elo.
What is Nemotron 3 Ultra 550B A55B best used for?+
Nemotron 3 Ultra 550B A55B is best suited for: 企业级 AI 自动化、代码研发、智能体（Agent）工作流以及 NVIDIA 生态深度集成. Assessment: Nemotron 3 Ultra 550B A55B 是 NVIDIA 旗下极具代表性的模型，在性能、速度与 API 成本之间实现了卓越平衡。
NVIDIA Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to NVIDIA's flagship reasoning tier:
Nemotron 3.5 Content Safety
Nemotron 3 Nano Omni 30B A3B Reasoning
Nemotron 3 Content Safety
Nemotron 3 Super 120B A12B
Nemotron VoiceChat
Nemotron Cascade 2 30B A3B
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Nemotron 3 Ultra 550B A55B:
The basic unit of text that a language model processes, typically representing about 3 to 4 English characters or roughly 0.75 words. For example, the sentence "Artificial intelligence is fast" breaks down into about 5 tokens.
A standardized evaluation suite used to objectively measure and rank an AI model's capabilities across specific tasks — such as SWE-bench Verified for software engineering, AIME for competitive math, or MMLU for general knowledge.
The maximum number of tokens a model can consider at once — both the input it reads and the output it writes. A 1,000,000-token context window can hold roughly 750,000 English words in a single request.
An AI model trained via reinforcement learning to spend dynamic compute thinking through complex logic, math, and architectural problems before outputting a solution. Examples include OpenAI o1, o3, and DeepSeek R1.
A step-by-step reasoning technique where an AI model generates intermediate problem-solving thoughts before arriving at a final answer. Native reasoning models like OpenAI o3 and DeepSeek R1 use CoT to solve complex math and coding challenges.
The ability of a language model to recognize when an external capability is needed and output structured JSON arguments to invoke external APIs, databases, or terminal commands. The runtime executes the tool and passes results back to the model.
Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
数据来源: NVIDIA Computex Keynote & Technical Whitepaper (June 2026)↗