Meta de AI foundational abierto investigación modelo que ignited el moderno de código abierto AI revolution con rotary positional embeddings y efficient pre-training.
Technical Specifications
- Provider
- Meta
- Category
- historical
- Release Date
- 2023-02
- Batch API Discount
- Standard rate
- 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-1-(65B)
- 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)
- Free across full context window (only KV-cache VRAM scales)
- Prompt Cache Write & TTL Policy
- Automatic prefix KV-caching in vLLM / SGLang (0 cost)
- 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
- 40 GB VRAM (4-bit Quantized) · 140 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-1 (65B) Cost, Context, Hardware & API Integration
Open-weights or free-tier model: $0 API license cost when self-hosted.
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-1 (65B):
Benchmark Scores & 6-Axis Capability Radar
- MMLU-Pro
- 48%
- HumanEval
- 23.7%
- Chatbot Arena Elo
- 1020pts
Strengths
- Catalyzed el entire de código abierto LLM community (Alpaca, Vicuna, llama.cpp)
- Introduced Rotary Position Embeddings (RoPE) y RMSNorm now estándar un través de LLMs
- Outperformed GPT-3 (175B) despite being menos que half su parámetro size
Limitations & Trade-offs
- Original lanzamiento restricted un non-comercial académico investigación
- pequeño 2,048 ventana de contexto y lacked instrucción tuning
Editorial Assessment
el spark que ignited el global democratized de código abierto AI desarrollador revolution.
Best for: académico arquitectura study y running ultra-quantized weights en local devices
Compare LLaMA-1 (65B)
Frequently Asked Questions about LLaMA-1 (65B)
How much does the LLaMA-1 (65B) API cost per 1M tokens?+
LLaMA-1 (65B) by Meta is priced at $0 per 1M input tokens and $0 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0 per 1M tokens, and asynchronous Batch API requests receive a 0% discount.
Should I use a $20/month subscription or the LLaMA-1 (65B) 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-1 (65B)'s context window?+
LLaMA-1 (65B) features a 2K tokens context window, which equals approximately 1,536 English words or ~4 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 2,048 tokens.
Can I run LLaMA-1 (65B) locally on my own GPU (Open Weights)?+
Yes, LLaMA-1 (65B) (65B (also 7B, 13B, 33B)) 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-1 (65B) 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-1 (65B) perform on coding and reasoning benchmarks?+
LLaMA-1 (65B) achieves a composite Intelligence Index of 25/100, scoring N/A% on SWE-bench Verified, N/A% on GPQA Diamond, 48% on MMLU-Pro, and 1020 pts on Chatbot Arena Elo.
What is LLaMA-1 (65B) best used for?+
LLaMA-1 (65B) is best suited for: académico arquitectura study y running ultra-quantized weights en local devices. Assessment: el spark que ignited el global democratized de código abierto AI desarrollador revolution.
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-1 (65B):
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.
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 paper: LLaMA: Open and Efficient Foundation Language Models (2023)↗