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meta-llama/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": true,
  "top_p": 0.9
}'

Pricing

Pay-per-use, no commitments

Input Tokens $0.27/1M Tokens
Output Tokens $0.85/1M Tokens

Technical Specifications

Model Architecture & Performance

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

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

Resources

Learn, watch, and build faster

Video Watch the walkthrough
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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

Build with meta-llama/Llama-3.3-70B-Instruct faster

Get deployment recipes, benchmark alerts, and GPU pricing updates for meta-llama/Llama-3.3-70B-Instruct (Llama 3.3 70B Instruct) and other chat models straight from the Qubrid team.

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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