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Ollama: Llama 3.1 8B Instruct

Ollama
Vision
128K
Context
Free
Input /1M
Free
Output /1M
N/A
Max Output
Run API Call Open in Chat

Ollama: Llama 3.1 8B Instruct

Overview

Property Value
Model ID ollama/llama3.1:8b-instruct
Provider Meta (formerly Facebook)
Display Name Llama 3.1 8B Instruct
Parameters 8 Billion (8B)
Type Instruction-Tuned Large Language Model
Release Date 2024

Description

Llama 3.1 8B Instruct is Meta's state-of-the-art instruction-tuned language model with 8 billion parameters. It's a compact yet powerful model designed for general-purpose conversational AI, reasoning tasks, and instruction following. This model is ideal for resource-constrained environments while maintaining strong performance across diverse tasks including text generation, summarization, question answering, and code assistance.

The model achieves performance comparable to much larger models through its optimized architecture and training process, making it an excellent choice for production deployments on consumer-grade hardware.

Specifications

  • Parameters: 8 Billion (8B)
  • Context Window: 128,000 tokens (128K)
  • Quantization Formats: Q4, Q5, FP16 (Full Precision)
  • Architecture: Transformer-based with Group Query Attention (GQA)
  • Training Data: 15+ Trillion tokens
  • Multilingual Support: Yes - English, German, French, Italian, Portuguese, Hindi, Spanish, Thai
  • Function Calling: Supported
  • Memory Required (Float16): ~16GB VRAM
  • Memory Required (Quantized Q4): ~4-5GB VRAM
  • Inference Speed: 30-40 tokens/second on RTX 3090, 50-60 on RTX 4090

Use Cases

  • Customer Support: Multi-turn conversational AI
  • Content Creation: Blog posts, articles, creative writing
  • Code Development: Code generation, debugging, explanation
  • Research: Document analysis, summarization, Q&A
  • Educational: Tutoring, explanation, learning assistance
  • Automation: Task automation, workflow assistance
  • Local Deployment: Privacy-focused applications

Limitations

  • Not as powerful as 70B or 405B models
  • May struggle with highly specialized tasks
  • Creative writing less rich than larger models
  • Limited vision capabilities (use vision variants for images)
  • llama3.1:70b-instruct - 70B version for more complex tasks
  • llama3.1:405b - Largest version for maximum performance
  • llama3.2:1b - Lightweight version for edge devices
  • llama3.2-vision:11b - Vision-capable variant

Key Capabilities

  • Multi-turn conversation
  • Long document processing (128K context)
  • Code generation and understanding
  • Reasoning and problem-solving
  • Instruction following
  • Tool/function calling
  • Knowledge retrieval
  • Summarization
  • Translation (multilingual)

Installation

Quick Start

# Pull and run the model directly
ollama pull llama3.1:8b-instruct
ollama run llama3.1:8b-instruct

With Specific Quantization

# Q4 Quantization (4GB RAM) - recommended for consumer GPUs
ollama pull llama3.1:8b-instruct:q4

# Q5 Quantization (6GB RAM)
ollama pull llama3.1:8b-instruct:q5

# Full Precision (Float16 - 16GB RAM)
ollama pull llama3.1:8b-instruct:fp16

Usage

Basic Chat

ollama run llama3.1:8b-instruct "What is machine learning?"

Interactive Conversation

ollama run llama3.1:8b-instruct
# Type your message and press Enter
# Type /quit to exit

API Usage (if Ollama server is running on port 11434)

curl -X POST https://api.langmart.ai/v1/generate \
  -d '{
    "model": "llama3.1:8b-instruct",
    "prompt": "Explain quantum computing",
    "stream": false
  }' | jq

OpenAI Compatible API

# If running Ollama with OpenAI-compatible endpoint
curl https://api.langmart.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama3.1:8b-instruct",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Performance & Hardware Requirements

Minimum Requirements

  • CPU: 4+ cores, 2.5GHz+
  • RAM: 8GB minimum (16GB recommended)
  • VRAM: 4-6GB for quantized versions, 16GB for full precision
  • Disk: 5-10GB free space
  • CPU: 8+ cores, 3.0GHz+
  • RAM: 16GB or more
  • GPU: NVIDIA (6GB+ VRAM), AMD, or Apple Silicon
  • Quantization: Q4 or Q5 for best balance

Inference Speed

  • GPU (RTX 3090): 30-40 tokens/second
  • GPU (RTX 4090): 50-60 tokens/second
  • CPU Only: 1-3 tokens/second (not recommended for production)

Memory by Format

Quantization VRAM Required Quality Speed
Context Window 128,000 tokens
Q4 (GGML) 4-5 GB Good Fast
Q5 (GGML) 6-8 GB Very Good Medium
Q6 (GGML) 8-10 GB Excellent Slower
FP16 16 GB Excellent Slowest

Comparison with Other Models

Model Parameters Context Speed Memory
Llama 3.1 8B 8B 128K Fast 4-16GB
Mistral 7B 7B 32K Very Fast 4-15GB
Qwen2.5 7B 7.6B 128K Fast 4-16GB
Llama 3.1 70B 70B 128K Slower 40-80GB

Advantages

  • Excellent instruction-following capability
  • Extended context window (128K tokens)
  • Multilingual support
  • Strong reasoning capabilities
  • Efficient for its size
  • Good balance between performance and resource requirements
  • Active community and support

Troubleshooting

Out of Memory Error

# Use smaller quantization
ollama pull llama3.1:8b-instruct:q4

# Set GPU memory limit
OLLAMA_GPU_MEMORY_PERCENT=0.8 ollama serve

Slow Inference

# Check if GPU acceleration is enabled
ollama ps

# If no GPU, try quantized version
ollama run llama3.1:8b-instruct:q4

Connection Issues

# Ensure Ollama server is running
ollama serve

# Check API availability
curl https://api.langmart.ai/v1/tags

Pricing

Type Price
Input Free
Output Free

Note: Ollama models run locally on your own hardware. No API costs apply - the model is free and open-source.

Resources & Documentation

Version History

  • Llama 3.1 8B: Latest version (2024)
  • Llama 3 8B: Previous generation
  • Llama 2 7B: Older generation