Best AI Models for 8GB RAM — What Can You Run Locally?

8GB of RAM is the sweet spot for getting started with local AI. You can run several excellent models that handle chat, coding, and general tasks — all without leaving your computer.

Quick Answer

Yes, you can run useful AI models with 8GB of RAM. Here are the best options.

The Models

All models listed use Q4_K_M quantization, which provides the best balance of quality and speed at this RAM tier.

Llama 3.1 8B

Meta's most popular model in a size that fits your machine.

• Size: 4.9 GB (Q4_K_M)
• Strengths: Excellent general-purpose performance, strong coding, active community
• Weaknesses: Not the best at specialized tasks like math reasoning
• Best for: Daily chat, writing assistance, coding help

# Run with Ollama
ollama run llama3.1

# Or with LM Studio — search "llama 3.1 8b" in the model browser

Qwen 2.5 7B

Alibaba's multilingual powerhouse.

• Size: 4.7 GB (Q4_K_M)
• Strengths: Excellent at coding, strong multilingual support (especially Chinese), good reasoning
• Weaknesses: Slightly less polished English output than Llama
• Best for: Coding tasks, multilingual users, technical writing

ollama run qwen2.5:7b

Mistral 7B

Fast and efficient conversational AI.

• Size: 4.4 GB (Q4_K_M)
• Strengths: Very fast inference, great at conversation, efficient memory usage
• Weaknesses: Less capable at complex reasoning tasks
• Best for: Quick conversations, brainstorming, when speed matters most

ollama run mistral:7b

DeepSeek R1 8B

The reasoning specialist.

• Size: 4.9 GB (Q4_K_M)
• Strengths: Chain-of-thought reasoning, excellent at math and logical problems, strong coding
• Weaknesses: Slower due to reasoning chains, verbose output
• Best for: Math problems, logical reasoning, complex coding tasks, analysis

ollama run deepseek-r1:8b

Which One Should You Pick?

For most users: Start with Llama 3.1 8B — it's the most well-rounded.

For coding: Use Qwen 2.5 7B or DeepSeek R1 8B.

For conversation: Mistral 7B is fastest; Llama 3.1 8B is most capable.

For math/reasoning: DeepSeek R1 8B is the clear winner.

Tips for 8GB Systems

1. Close other apps — browsers and IDEs use significant RAM
2. Run one model at a time — don't try to load multiple models simultaneously
3. Use Q4 quantization — it's the best quality/size trade-off
4. Try Ollama over LM Studio — Ollama uses less overhead, leaving more RAM for the model
5. Use an M-series Mac if possible — unified memory handles models more efficiently than discrete RAM

What If 8GB Isn't Enough?

If you want to run larger, more capable models like Qwen 2.5 14B or Llama 3.1 70B, you have options:

• Upgrade to 16GB+ — check our 16GB RAM model guide
• Use cloud GPU — Runpod lets you run any model from $0.20/hr
• Deploy Ollama on the cloud — our Runpod deployment guide shows you how

Related Guides

• Getting Started with Local AI
• How to Install Ollama
• Ollama vs LM Studio