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Local AI

Can Your PC Run Llama 3? Free LLM Hardware Checker — GPU + RAM Calculator (2026)

Free interactive tool: select your GPU and RAM, see exactly which LLMs you can run locally — Llama 3, Qwen, Mistral, DeepSeek — with real tokens/sec estimates and upgrade recommendations.

Want to run Llama, Qwen, Mistral, or DeepSeek on your own hardware? This interactive tool tells you exactly which models will run on your GPU and RAM, with real tokens-per-second estimates. Then dive into our four companion guides for deeper detail.

Quick answer: Your VRAM determines what you can run. 4 GB handles small models like Phi-3 Mini. 12 GB (RTX 3060) runs Llama 3 8B at 45 tok/s comfortably. 24 GB (RTX 3090/4090) unlocks 70B models at Q2 quantization. Use the tool below for a precise match.

Can I Run This LLM Locally?

Select your hardware → see which models you can actually run, with real performance estimates.

Estimates are based on community-reported benchmarks. Actual performance varies by quantization, context length, and backend (Ollama, llama.cpp, vLLM). Amazon links are affiliate links — we may earn a commission at no extra cost to you.

Where to buy these GPUs

Affiliate links — using these helps support the testing on this site at no extra cost to you.

RTX 3060 12GB
Best entry — runs 7B/13B comfortably Check price → RTX 4060 Ti 16GB
Sweet spot — adds 34B reach Check price → RTX 4070 Super 12GB
Faster than 4060 Ti, less VRAM Check price → RTX 4090 24GB
Top-tier — 70B with offload Check price → Mac Studio M3 Max 64GB
Quiet 70B box on unified memory Check price → Try a GPU Droplet
DigitalOcean — pay-by-hour H100/A100 $200 credit →

Want speed numbers? See the tokens-per-second benchmarks for each card on 7B / 13B / 34B / 70B models.

Diagram showing how a local LLM processes a prompt through tokenizer, GPU VRAM with loaded model weights, transformer layers, and output tokens
The model lives in VRAM. That’s why VRAM size is the single most important spec when running LLMs locally.

How to use this tool

Pick your GPU (or select CPU-only), choose your system RAM, and select the type of model you want — general chat, coding, vision, or flagship. The tool shows every popular open-source LLM that can run on your setup, with a performance estimate.

Deep-dive guides

The tool gives you the answer. These guides explain the why:

💾 VRAM Requirements 2026

Full matrix mapping 4GB → 80GB VRAM buckets to every popular LLM. Know exactly what your GPU can handle.

⚡ Tokens/Second Benchmarks

Real-world speed numbers across RTX 4090, 3090, 4070, 3060, M3 Max, and CPU — for 7B through 70B models.

💰 Best GPU by Budget

Budget decision guide: under $300, $500-800, $1000-1500, $2000+ — which GPU unlocks which models.

🧮 Quantization Explained

What Q4_K_M, Q5, Q8 actually mean. The sweet spot between quality and size, demystified.

Which LLM runtime should I use?

Once you know your hardware can run a model, you need a runtime to actually load and use it:

Last updated: 2026-04-22. All benchmarks measured on Ubuntu 24.04, llama.cpp b3520, Q4_K_M quantization unless noted.