especializado 8B moderation y seguridad screening modelo proporcionando bajo-latencia risk assessment, jailbreak defense, y cumplimiento clasificación.
Technical Specifications
- Provider
- NVIDIA
- Category
- open-source
- Release Date
- 2025-10
- 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-safety-guard-8b-v3
- 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 ($0.05/1M in) across full 128K window
- Prompt Cache Write & TTL Policy
- Cached input at $0.01/1M tokens
- Computer Use & Browser GUI Agent
- Text-only completion (No GUI action loop)
- MCP & Parallel Tool Calling
- Prompt-based JSON extraction only
- 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
- 5 GB VRAM (4-bit Quantized) · 17 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 Safety Guard 8B v3 Cost, Context, Hardware & API Integration
Pay-as-you-go API is cheaper for this volume (saves $16.70/mo vs $20 flat plan).
Break-even point: ~3,030 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 Safety Guard 8B v3:
Benchmark Scores & 6-Axis Capability Radar
- Chatbot Arena Elo
- 1160pts
Strengths
- ultra-rápido token clasificación velocidad (180 tps) con minimal compute latencia
- Granular detection un través de 13 risk categories including inyección de instrucciones y PII leakage
- Plugs seamlessly en NeMo Guardrails y empresarial API gateways
Limitations & Trade-offs
- especializado clasificación modelo; unsuitable para general generative texto o codificación
- requiere strict prompt framing para optimal seguridad taxonomy scoring
Editorial Assessment
un diseñado específicamente empresarial seguridad shield ensuring bulletproof moderation canalizaciones.
Best for: API gateway seguridad filtering, cumplimiento auditing, y inyección de instrucciones defense
Compare Llama 3.1 Nemotron Safety Guard 8B v3
Frequently Asked Questions about Llama 3.1 Nemotron Safety Guard 8B v3
How much does the Llama 3.1 Nemotron Safety Guard 8B v3 API cost per 1M tokens?+
Llama 3.1 Nemotron Safety Guard 8B v3 by NVIDIA is priced at $0.05 per 1M input tokens and $0.1 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.01 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 Safety Guard 8B v3 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 Safety Guard 8B v3's context window?+
Llama 3.1 Nemotron Safety Guard 8B v3 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 2,048 tokens.
Can I run Llama 3.1 Nemotron Safety Guard 8B v3 locally on my own GPU (Open Weights)?+
Yes, Llama 3.1 Nemotron Safety Guard 8B v3 (8B) 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 Safety Guard 8B v3 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 Safety Guard 8B v3 perform on coding and reasoning benchmarks?+
Llama 3.1 Nemotron Safety Guard 8B v3 achieves a composite Intelligence Index of 36/100, scoring N/A% on SWE-bench Verified, N/A% on GPQA Diamond, N/A% on MMLU-Pro, and 1160 pts on Chatbot Arena Elo.
What is Llama 3.1 Nemotron Safety Guard 8B v3 best used for?+
Llama 3.1 Nemotron Safety Guard 8B v3 is best suited for: API gateway seguridad filtering, cumplimiento auditing, y inyección de instrucciones defense. Assessment: un diseñado específicamente empresarial seguridad shield ensuring bulletproof moderation canalizaciones.
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 Safety Guard 8B v3:
La unidad básica de texto que procesa un modelo de lenguaje, que normalmente equivale a 3 o 4 caracteres o aproximadamente 0,75 palabras. Por ejemplo, una frase típica se divide en varios tokens.
Un conjunto estandarizado de pruebas para evaluar y comparar objetivamente las capacidades de distintos modelos de IA en tareas como programación (SWE-bench) o matemáticas avanzadas (AIME).
La cantidad máxima de tokens que un modelo puede considerar a la vez, incluyendo tanto la entrada que lee como la salida que genera. Una ventana de 1.000.000 de tokens puede procesar unas 750.000 palabras en una sola petición.
Una optimización donde los proveedores almacenan estados de atención previos para prefijos de prompts frecuentes, reduciendo el coste de entrada hasta en un 90% y acelerando la respuesta.
Una optimización de memoria GPU que almacena matrices de atención previas (Key y Value) para evitar recalcular tokens ya procesados, acelerando la generación de respuestas en streaming.
La arquitectura de red neuronal introducida en 2017 que impulsa los LLM modernos, empleando mecanismos de autoatención para procesar secuencias completas en paralelo.
Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.