By VRLA Tech  ·  AI Computing  ·  April 2026

LLaMA 3 is Meta’s open-weight large language model family, widely considered among the best open-source LLMs available in 2026. Running LLaMA 3 locally — on your own hardware, without sending data to external APIs — requires a GPU with sufficient VRAM to hold the model weights. This guide covers the exact hardware requirements for every LLaMA 3 model size and how to get it running in under an hour.

LLaMA 3 model sizes and VRAM requirements

The fastest way: Ollama

Ollama is the simplest tool for running LLaMA 3 locally in 2026. Install it with a single command, pull the model, and you have a local OpenAI-compatible API running in minutes. On a system with a compatible NVIDIA GPU, Ollama automatically uses GPU acceleration.

To run LLaMA 3 8B with Ollama: install Ollama from ollama.com, then run ollama run llama3. Ollama downloads the model and starts serving it. The local API is available at localhost:11434/v1 and is compatible with OpenAI client libraries by changing one line of code.

On a system with 32GB GPU VRAM (RTX 5090), Ollama loads LLaMA 3 8B at full quality and generates approximately 150–250 tokens per second. On a system with 96GB VRAM (RTX PRO 6000 Blackwell), Ollama loads LLaMA 3 70B at Q6 or Q8 quantization for near-full-quality output.

For production serving: vLLM

For teams serving LLaMA 3 to multiple users simultaneously, vLLM is the production-grade option. It uses paged attention for efficient KV cache management, supports continuous batching for multiple concurrent requests, and exposes an OpenAI-compatible API. Install with pip install vllm and start serving with vllm serve meta-llama/Llama-3-8B-Instruct.

GPU recommendations by model size

• LLaMA 3 8B: NVIDIA RTX 5080 (16GB) or RTX 5090 (32GB). Full FP16 fits on 16GB with comfortable KV cache headroom on 32GB.
• LLaMA 3 13B: NVIDIA RTX 5090 (32GB). Full FP16 fits with room for KV cache.
• LLaMA 3 70B: NVIDIA RTX PRO 6000 Blackwell (96GB). FP8 weights (~70GB) fit with 26GB remaining for KV cache on a single GPU.
• LLaMA 3 405B: Multi-GPU server required. VRLA Tech 4–8 GPU EPYC servers handle 405B at FP8 on 4–8 RTX PRO 6000 GPUs.

Browse local LLM hardware on the VRLA Tech LLM Workstation page.

VRLA Tech has been building custom workstations since 2016. All systems ship with a 3-year parts warranty and lifetime US-based engineer support.