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LLM Model Hardware Requirements & VRAM Allocation Specs

This spec sheet lists memory, storage, and GPU requirements for running local models at different parameter sizes and quantization levels.

Local LLM VRAM & GPU Specification Recommender

Minimum VRAM Required
41.2 GB
Includes context cache & OS overhead
Recommended GPU Configuration
2x RTX 3090 / RTX 4090 (48GB VRAM total) or Mac Studio (64GB)
Estimated Inference Speeds
Medium (15-25 tokens/sec)

1. VRAM Allocation Calculations

Use these specs to plan hardware configurations: - **8B Parameter model**: Q4 requires 5GB VRAM, FP16 requires 16GB VRAM. - **70B Parameter model**: Q4 requires 40GB VRAM, FP16 requires 140GB VRAM.

2. Workstation Tiers and GPU Configurations

Workstation setups scale with model requirements: - **Tier 1 (Budget)**: RTX 3060/4060 GPU, runs 8B models. - **Tier 2 (Developer)**: Dual RTX 3090/4090 GPUs, runs 70B models. - **Tier 3 (Workstation)**: Mac Studio with 128GB+ unified memory, runs 70B and quantized 405B models.

Frequently Asked Questions

How much VRAM does Llama 3.3 70B Q4 require?

Llama 3.3 70B Q4 requires roughly 40GB of VRAM to load, requiring dual GPU configurations or Apple Silicon unified memory.

What is the disk storage requirement for local models?

Llama 3 8B files consume ~5GB (quantized) to 16GB (FP16). Llama 3 70B files consume ~40GB (quantized) to 140GB (FP16), requiring sufficient SSD storage.