AI Hardware Advisor
Ask once.
Get the whole plan.
Tell us your machine and goal in simple language. We'll analyze everything and give you the best AI setup, model recommendations, and cost insights.
Instant analysis
Accurate results
Privacy first
Describe your setup and goal…
Natural language is OK!
0/500
Primary use
AKM Trusted by 35,000+ builders and AI enthusiasts
Last updated: —
Here's your AI hardware plan Overall Score: —/100
Analyzing your setup…
Best Models For Your Setup
View allUpgrade Suggestions
All calculations are done locally in your browser.
Your data never leaves your device.
Evidence & method
How this calculation works
Estimates the VRAM an AI model needs at a given precision — weights, KV cache where the architecture is known, and runtime overhead — then matches that against GPUs to show what fits.
Data sources
- NVIDIA, AMD & Intel GPU documentation
- Hugging Face model cards
- Official model papers
- Bitpute Methodology
Assumptions
- Weights = parameters × bits-per-weight ÷ 8
- Runtime overhead = 0.75 GB + 5% of weights (CUDA context, allocator, buffers)
- Recommended VRAM = total × 1.10 (10% headroom)
- Usable VRAM = card capacity × 0.95
- KV cache counted only when the model architecture is known; when applied, batch size 1, FP16 KV, context as entered
- Single GPU, inference workload
Limitations
- Actual VRAM varies by framework (PyTorch, llama.cpp, vLLM, TGI), driver/CUDA version and OS
- Quantization implementations differ; real bits-per-weight can vary from the nominal value
- When architecture is unknown the KV cache is omitted, so long-context use will exceed the estimate
- Estimates are for planning and comparison, not a guarantee
Data status: Hardware specs — vendor documentation. Model metadata — community model cards, not independently verified.
Related
Why does this estimate differ from other calculators?
- Different footprint formulas and rounding
- Different KV-cache assumptions (batch, context, precision)
- Framework and inference-engine differences
- Reserved and fragmented VRAM
- Driver and CUDA overhead
- Precision and quantization choices