About LLMHardware.io

What We Do

LLMHardware.io is an independent reference site that helps people choose the right hardware for running open-source large language models locally — on their own machine, without cloud APIs.

For every model in the Hugging Face Hub, we publish three tiered hardware recommendations: a minimum configuration that runs the model at Q4 quantization, a comfortable tier for Q5/Q8 with extended context, and a headroom tier for FP16 or larger model sizes. Recommendations are calculated from first principles using published VRAM requirements and real hardware specifications.

Our Methodology

Hardware recommendations are generated by a rules-based VRAM calculator using the formula:

VRAM (GB) = parameters (B) × bytes_per_param × 1.2 overhead

Where bytes_per_param is 2.0 for FP16, 1.0 for Q8, 0.625 for Q5_K_M, and 0.5 for Q4_K_M. An additional 2 GB is reserved for OS and inference engine overhead.

Hardware specifications — VRAM capacity, memory bandwidth, TDP — are sourced from manufacturer spec sheets. We do not display prices: they change too often to keep accurate, so each product link goes straight to Amazon for the current price.

Model data is sourced from the Hugging Face Hub API and is refreshed daily. Parameter counts are extracted from model card metadata when available, and estimated from model name conventions as a fallback.

Who runs this site

LLMHardware.io is written and maintained by Billy G.R., a software engineer and home lab hobbyist who has been running open-source LLMs on consumer hardware since 2023. The current rig list spans an RTX 4090, two RTX 3090s on a dual-GPU build, an Apple Silicon Mac mini for low-power overnight jobs, and a small AMD box for ROCm regression testing.

Reach Billy at [email protected] or via LinkedIn.

No vendor sponsorships, no paid placements. Hardware recommendations are determined by measured VRAM capacity, memory bandwidth, and price efficiency. Affiliate commissions on Amazon links do not change which hardware appears in which tier; see the methodology page for the full ranking process.

What We Cover

• Hardware recommendations for 2,800+ open-source models from Hugging Face
• Consumer GPU reviews and comparisons (NVIDIA, AMD)
• Apple Silicon performance for LLM inference (M4, M3 series)
• Mini-PC and budget builds for local AI
• Quantization guides and VRAM optimization techniques
• Setup guides for Ollama, llama.cpp, LM Studio, and related tools

Affiliate Disclosure

LLMHardware.io participates in the Amazon Associates program. When you purchase through our links, we may earn a commission at no additional cost to you. This helps us maintain the site and keep content free.

Affiliate relationships do not influence our recommendations. We link to the hardware our methodology identifies as the best fit for each use case — not to the products with the highest commission rates. See our full disclosure page for details.

Data Accuracy

Hardware prices and availability change frequently, so we do not publish prices on this site. Every product link takes you to Amazon, where the current price and availability are shown. Always confirm the price on the retailer's site before purchasing.

Model parameter counts and VRAM requirements are calculated estimates. Actual memory usage may vary depending on inference engine, context length, batch size, and other factors. Our figures are a practical starting point, not a guarantee.

Contact

For corrections, partnership inquiries, or general questions, visit our contact page.