AI RECOMMENDATION WIZARD
Answer 6 quick questions. Get your GPU.
No specs knowledge needed — pick a budget and what you want to do, and we'll compute the best card, alternatives, the models it runs, honest pros & cons, and your upgrade path. Runs entirely in your browser.
YOUR RECOMMENDATION
Alternatives
Models it runs
Pros & cons
Upgrade path
Prices are approximate street prices and move often. Recommendations are computed from the same parity-tested memory engine as our calculators — a card is never suggested for a model it can't run.
Evidence & method
How this calculation works
Walks through your model, precision and context, estimates the memory footprint, and recommends GPUs that clear it with headroom.
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