LLMRequirements.com
hardware to LLMs
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What is LLMRequirements.com?
LLMRequirements.com is a comprehensive resource for anyone looking to run large language models locally on their own hardware. The site focuses on hardware recommendations for local AI, providing detailed guides on building PCs specifically optimized for LLM inference, GPU comparisons, and budget-based build recommendations.
Key features include an interactive hardware picker that matches users to 74 real builds across categories like Apple Silicon, NVIDIA consumer/pro/datacenter GPUs, AMD Instinct, Intel Arc Pro, and Strix Halo. The site catalogs 66 total builds with VRAM requirements, model sizing guides, and performance benchmarks. It offers a 'State of Local AI' section covering dense vs MoE architecture comparisons, current model recommendations, and hardware picks organized by budget.
The website is designed for individuals and professionals who want to run AI locally for privacy, cost savings, or customization. This includes developers building AI applications, researchers working with open-weight models, hobbyists interested in local AI, and businesses wanting to avoid cloud API costs. The site provides copy-paste install commands for Ollama and llama.cpp, quantization recommendations, and guidance on memory bandwidth, VRAM requirements, and GPU compute capabilities.
LLMRequirements.com emphasizes that VRAM is the most critical metric for LLM performance, with model sizing cheat sheets showing 7B models need ~5GB VRAM, 14B need ~10GB, 32B need ~20GB, and 70B need ~40GB at 4-bit compression. The site covers tiered builds from $1,200 starter builds with RTX 4060 Ti 16GB to enthusiast builds with RTX 490 or Mac Studio M3 Ultra with 96GB unified memory.
LLMRequirements.com pricing
Pricing model: Freemium
The website itself is free to access with all hardware guides, build recommendations, interactive picker, and State of Local AI content available at no cost. The site provides budget-based recommendations ranging from $180 for Raspberry Pi 5 + AI HAT to $700+ for Mac Mini M4, $1,200-$1,500 for Tier 1 starter builds, and $2,000+ for enthusiast builds. No paid tiers or subscription plans are offered - all content including the interactive picker with 74 builds and 66 catalogued hardware configurations is freely available.
LLMRequirements.com pros
- Interactive picker matched to 74 real builds for personalized recommendations
- 66 total catalogued builds across all major hardware categories
- Clear VRAM-first approach simplifies hardware selection
- Model sizing cheat sheet with 4-bit compression VRAM requirements
- Three clear budget tiers from $1,200 to $2,000+
- Covers Apple Silicon, NVIDIA, AMD, and Intel options comprehensively
- Copy-paste install commands for Ollama and llama.cpp included
- State of Local AI section with dense vs MoE comparisons
- Real-world token-per-second benchmarks for different hardware
- 3-year total cost calculations including power consumption
- Hybrid local+cloud approach recommendations for limited RAM
- Detailed GPU comparisons with price-to-performance analysis
- Quantization sweet spot guidance for optimal quality/speed tradeoff
- Apple unified memory equivalents provided for each tier
- Used GPU options included for budget-conscious builders like RTX 3090
- Memory bandwidth explained as key metric beyond just VRAM amount
- NPU TOPS clarification that it doesn't matter for LLM inference
LLMRequirements.com cons
- Focuses primarily on NVIDIA hardware with less AMD ROCm detail
- No direct purchasing links, users must source components separately
- Linux/WSL setup comfort required for many recommended builds
- Limited coverage of enterprise/datacenter scaling options
- No built-in model hosting or serving platform, just hardware guidance
- Used GPU recommendations carry risk for inexperienced buyers
- Power consumption estimates may vary significantly by usage pattern
- No mobile/edge device coverage beyond brief Raspberry Pi mention
Frequently asked questions about LLMRequirements.com
What is the most important spec for running local LLMs?
VRAM (Video RAM) is the single most important metric. Memory bandwidth determines inference speed, available VRAM/unified memory determines max model size, and GPU compute (CUDA/Metal) affects actual token generation speed. NPU TOPS almost never matters for LLM inference.
How much VRAM do I need for different model sizes?
At 4-bit compression: 7B models need ~5GB VRAM, 14B models need ~10GB VRAM, 32B models need ~20GB VRAM, and 70B models need ~40GB VRAM. For 7-8B models, 8GB is sufficient. For bigger tasks, you need 16GB+ or hybrid local+cloud approach.
What is the best budget build for local LLMs?
The Tier 1 starter build at $1,200-$1,500 with RTX 4060 Ti 16GB is the sweet spot. It runs 7B-8B models like Llama 3 8B or DeepSeek Distill 7B for coding assistance and document summaries. Do not buy the 8GB version of the 4060 Ti.
Is 8GB RAM enough for local AI?
For 7-8B models running locally, yes. For bigger tasks, 8GB is limiting. The site recommends hybrid approach where local handles privacy-sensitive tasks (voice, personal data) and cloud APIs (Claude, GPT, Gemini) handle larger requests when needed.
What software do I need to run local LLMs?
Ollama is the primary command-line tool - type one command and the model downloads and runs. LM Studio provides a visual ChatGPT-style interface for downloading, GPU detection, and serving. Both handle the complexity of model management.
Should I build a gaming PC for local AI?
No. Most people mistake prioritizing faster processor and GPU clock speeds. For local LLMs, VRAM matters more than anything else combined. Build specifically for AI with maximum VRAM in your budget rather than gaming optimization.
What about Mac vs PC for local LLMs?
Apple uses unified memory where GPU and main computer share the same memory pool. Mac Mini M4 with 16GB runs 30B+ models comfortably at ~30 tok/s. M4 Pro with 32GB unified memory is unmatched for local LLMs but costs 2-3x more than equivalent PC builds.
Are ARM SBCs like Raspberry Pi good for local AI?
No ARM SBC under €300 has a GPU capable of running 8B LLMs at usable speed. Raspberry Pi 5 + AI HAT only achieves ~2-3 tok/s with 3B models. Jetson's 1024 CUDA cores make local AI viable; other SBCs use CPU inference which is 3-10x slower.
What quantization should I use?
4-bit compression (quantization) is the sweet spot that shrinks models with minimal quality loss. The interactive picker automatically picks the right GGUF quantization based on your memory budget with 15% safety margin.
Can I use used GPUs for local AI builds?
Yes, used RTX 3090 with 24GB VRAM is recommended for Tier 2 power user builds. 24GB is a game-changer allowing 32B models with room for long conversations. Runs slightly slower than new Nvidia setups at 11-12 tokens per second but is quiet, efficient, and simple.