de NVIDIA customized 70B alignment centrale trained avec HelpSteer2 à rival commercial flagships sur conversationnel qualité et codage.
Technical Specifications
- Provider
- NVIDIA
- Category
- open-source
- Release Date
- 2025-04
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Fixed
- 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)
- llama-3-1-nemotron-70b
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2027+)
- 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 ($0.35/1M in) across full 128K window
- Prompt Cache Write & TTL Policy
- Cached input at $0.1/1M 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
- 43 GB VRAM (4-bit Quantized) · 151 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
Llama 3.1 Nemotron 70B Instruct Cost, Context, Hardware & API Integration
For single-user chat, a $20/mo subscription caps your bill below this API volume.
Break-even point: ~417 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 Llama 3.1 Nemotron 70B Instruct:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 41.2%
- GPQA Diamond
- 58.5%
- MMLU-Pro
- 79.2%
- HumanEval
- 88%
- Terminal-Bench
- 38%
- AIME
- 68%
- Long-Context Reasoning
- 75%
- Chatbot Arena Elo
- 1318pts
Strengths
- Trained avec HelpSteer2 reward modèles pour exceptionnel conversationnel nuance
- Rivals larger 100B+ modèles sur instruction-following et codage clarity
- optimisé pour d'entreprise NVIDIA NIM microservice déploiement
Limitations & Trade-offs
- nécessite dual-GPU d'entreprise matériel pour local unquantized exécution
- manque de natif visuel image compréhension
Editorial Assessment
One du finest ouvert 70B instruction tunings ever created, demonstrating de NVIDIA alignment prowess.
Best for: d'entreprise client support, synthetic données génération, et local agent orchestration
Compare Llama 3.1 Nemotron 70B Instruct
Frequently Asked Questions about Llama 3.1 Nemotron 70B Instruct
How much does the Llama 3.1 Nemotron 70B Instruct API cost per 1M tokens?+
Llama 3.1 Nemotron 70B Instruct by NVIDIA is priced at $0.35 per 1M input tokens and $0.7 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.1 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the Llama 3.1 Nemotron 70B Instruct 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 Llama 3.1 Nemotron 70B Instruct's context window?+
Llama 3.1 Nemotron 70B Instruct features a 128K tokens context window, which equals approximately 96,000 English words or ~256 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 8,192 tokens.
Can I run Llama 3.1 Nemotron 70B Instruct locally on my own GPU (Open Weights)?+
Yes, Llama 3.1 Nemotron 70B Instruct (70B) 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 Llama 3.1 Nemotron 70B Instruct 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 Llama 3.1 Nemotron 70B Instruct perform on coding and reasoning benchmarks?+
Llama 3.1 Nemotron 70B Instruct achieves a composite Intelligence Index of 47/100, scoring 41.2% on SWE-bench Verified, 58.5% on GPQA Diamond, 79.2% on MMLU-Pro, and 1318 pts on Chatbot Arena Elo.
What is Llama 3.1 Nemotron 70B Instruct best used for?+
Llama 3.1 Nemotron 70B Instruct is best suited for: d'entreprise client support, synthetic données génération, et local agent orchestration. Assessment: One du finest ouvert 70B instruction tunings ever created, demonstrating de NVIDIA alignment prowess.
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 Ultra 550B A55B
Nemotron 3.5 Content Safety
Nemotron 3 Nano Omni 30B A3B Reasoning
Nemotron 3 Content Safety
Nemotron 3 Super 120B A12B
Nemotron VoiceChat
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Llama 3.1 Nemotron 70B Instruct:
L'unité textuelle de base traitée par un modèle linguistique, représentant généralement 3 à 4 caractères ou environ 0,75 mot. Par exemple, une phrase courante est découpée en plusieurs jetons.
Une suite de tests standardisés permettant d'évaluer et de comparer objectivement les performances des modèles d'IA sur des tâches ciblées (code avec SWE-bench, mathématiques avec AIME, etc.).
Le nombre maximal de jetons qu'un modèle peut traiter simultanément (entrée et sortie comprises). Une fenêtre de 1 000 000 jetons permet d'analyser environ 750 000 mots en une seule requête.
La capacité d'un LLM à déterminer quand une action externe est requise et à générer des arguments structurés pour invoquer des API, calculatrices ou bases de données avant de poursuivre.
Un standard ouvert créé par Anthropic permettant aux agents d'IA de se connecter de façon uniforme aux systèmes de fichiers locaux, aux environnements de développement et aux API tierces.
Une optimisation tarifaire et technique conservant les états d'attention des préfixes de prompts récurrents, réduisant les coûts d'entrée de 50 à 90 % tout en abaissant la latence.
Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: NVIDIA Research & Hugging Face Leaderboard (April 2025)↗