Hardware Requirements

Understanding hardware requirements is crucial for successful LLM deployment, as models have vastly different resource needs depending on their size, architecture, and intended use case.

Key Hardware Components

Memory (RAM/VRAM):

• Primary constraint for most LLM deployments
• Models require memory roughly proportional to parameter count
• VRAM for GPU inference, system RAM for CPU-only deployment
• Buffer needed beyond model size for inference overhead

Processing Units:

• CPU: Required for all deployments, affects inference speed
• GPU: Dramatically accelerates inference for supported models
• Specialized Hardware: TPUs, NPUs for specific optimization

Storage:

• Model file storage (can be 1-100+ GB per model)
• Fast storage improves model loading times
• Consider quantized model variants for size optimization

Sizing Guidelines

Small Models (1-7B parameters):

• 4-16 GB RAM/VRAM typically sufficient
• Can run on consumer hardware
• Good for development and testing

Medium Models (7-30B parameters):

• 16-64 GB memory requirements
• Benefit significantly from GPU acceleration
• Professional/enthusiast hardware level

Large Models (30B+ parameters):

• 64+ GB memory requirements
• Often require distributed deployment
• Enterprise/research-grade hardware

Optimization Strategies

Quantization:

• Reduces memory requirements by 2-8x
• Automatic tools can find optimal quantization levels
• Trade-off between efficiency and quality

Model Variants:

• Choose appropriately-sized models for use case
• Consider instruction-tuned vs. base model needs
• Evaluate context window requirements

Assessment Tools

Modern model-selection-tools like llmfit automate hardware assessment:

• Scan system specifications automatically
• Calculate model compatibility scores
• Recommend optimal quantization levels
• Prevent deployment failures due to insufficient resources

See also

• model-selection-tools
• model-quantization
• on-device-inference
• edge-models