Llama 3.3 70B Instruct

Llama 3.3 70B Instruct is a 70B-parameter open-weight large language model from Meta, optimized for instruction following, complex reasoning, and multi-turn conversations. It is well suited for enterprise use cases such as advanced chat assistants, code reasoning, and long-document analysis with large context windows.

Meta Chat 128K Tokens
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api_example.sh

curl -X POST "https://platform.qubrid.com/v1/chat/completions" \
  -H "Authorization: Bearer $QUBRID_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "meta-llama/Llama-3.3-70B-Instruct",
  "messages": [
    {
      "role": "user",
      "content": "Explain quantum computing in simple terms"
    }
  ],
  "temperature": 0.7,
  "max_tokens": 4096,
  "stream": false,
  "top_p": 0.9
}'

Technical Specifications

Model Architecture & Performance

Variant Instruct
Model Size 70B params
Context Length 128K Tokens
Quantization fp16
Tokens/Second 120
Architecture Transformer with Grouped-Query Attention (GQA)
Precision bfloat16
License Meta Llama License
Release Date 2024
Developers Meta

Pricing

Pay-per-use, no commitments

Input Tokens $0.00027/1K Tokens
Output Tokens $0.00085/1K Tokens

API Reference

Complete parameter documentation

Parameter Type Default Description
stream boolean true Enable streaming responses for real-time output.
temperature number 0.7 Controls randomness. Higher values mean more creative but less predictable output.
max_tokens number 4096 Maximum number of tokens to generate in the response
top_p number 0.9 Nucleus sampling: considers tokens with top_p probability mass.

Explore the full request and response schema in our external API documentation

Performance

Strengths & considerations

Strengths Considerations
High-quality reasoning and instruction adherence
Strong performance on code and analytical tasks
Large context window for long-document processing
Open-weight model suitable for private and on-prem deployments
Production-ready for enterprise workloads
Smaller context window compared to largest models
Can struggle with highly complex, multi-step reasoning

Use cases

Recommended applications for this model

Enterprise chat assistants
Advanced code generation and review
Long-document question answering
Summarization at scale
Retrieval-Augmented Generation (RAG)
AI agents and workflow automation

Enterprise
Platform Integration

Docker

Docker Support

Official Docker images for containerized deployments

Kubernetes

Kubernetes Ready

Production-grade KBS manifests and Helm charts

SDK

SDK Libraries

Official SDKs for Python, Javascript, Go, and Java

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