de Meta commercially licensed de pesos abiertos modelo fundacional developed con Grouped-Query Attention y Ghost Attention para extendido dialogues.
Technical Specifications
- Provider
- Meta
- Category
- historical
- Release Date
- 2023-07
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Fixed
- Structured Outputs
- No
- 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)
- meta-llama/Llama-2-(70B)
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Legacy Stable (Upgrade path available in Meta lineup)
- 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 ($0.4/1M in) across full 4K window
- Prompt Cache Write & TTL Policy
- Cached input at $0.1/1M 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
- 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 2 (70B) Cost, Context, Hardware & API Integration
For single-user chat, a $20/mo subscription caps your bill below this API volume.
Break-even point: ~370 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 2 (70B):
Benchmark Scores & 6-Axis Capability Radar
- MMLU-Pro
- 56.8%
- HumanEval
- 37.8%
- Chatbot Arena Elo
- 1105pts
Strengths
- primer Llama family lanzamiento con permissive comercial license (bajo 700M MAU)
- Incorporated Grouped-Query Attention (GQA) para 2x más rápido KV-cache inferencia
- Extensive red-teaming y seguridad tuning via dual RLHF reward modelos
Limitations & Trade-offs
- ventana de contexto limitado un 4,096 tokens
- Modest codificación benchmark scores en comparación con propietario de frontera modelos
Editorial Assessment
el hito que established de pesos abiertos AI as safe y viable para comercial producción.
Best for: autohospedado empresarial privado clouds y local en-premises cumplimiento implementaciones
Compare Llama 2 (70B)
Frequently Asked Questions about Llama 2 (70B)
How much does the Llama 2 (70B) API cost per 1M tokens?+
Llama 2 (70B) by Meta is priced at $0.4 per 1M input tokens and $0.8 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 2 (70B) 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 2 (70B)'s context window?+
Llama 2 (70B) features a 4K tokens context window, which equals approximately 3,072 English words or ~8 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 4,096 tokens.
Can I run Llama 2 (70B) locally on my own GPU (Open Weights)?+
Yes, Llama 2 (70B) (70B (also 7B, 13B)) releases open weights that can be self-hosted locally or on private cloud GPUs using vLLM, Ollama, SGLang, or llama.cpp.
Does Meta train on data sent to the Llama 2 (70B) API, and is commercial use allowed?+
100% Isolated when self-hosted on private GPUs or VPC. License tier: Llama Community License (Commercial Permitted). Full commercial ownership of generated outputs. Supported deployment channels include: Self-Hosted GPU (vLLM / Ollama), Private VPC, or Managed Serverless API.
How does Llama 2 (70B) perform on coding and reasoning benchmarks?+
Llama 2 (70B) achieves a composite Intelligence Index of 31/100, scoring N/A% on SWE-bench Verified, N/A% on GPQA Diamond, 56.8% on MMLU-Pro, and 1105 pts on Chatbot Arena Elo.
What is Llama 2 (70B) best used for?+
Llama 2 (70B) is best suited for: autohospedado empresarial privado clouds y local en-premises cumplimiento implementaciones. Assessment: el hito que established de pesos abiertos AI as safe y viable para comercial producción.
Meta Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to Meta's flagship reasoning tier:
Llama 4 Behemoth
Llama 5
Muse Spark 1.1
Llama 4 Maverick
Llama 4 Scout
Llama 3.3 70B
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Llama 2 (70B):
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.
Fuentes: Meta AI: Llama 2: Open Foundation and Fine-Tuned Chat Models (2023)↗