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Minimum VRAM
141GB
FP16 (full model) • Q4 option ≈ 35GB
Most Affordable
Apple M3 Max
Q4 • ~10 tok/s • From $3,999
Optimal Performance
NVIDIA L40
Q4 • ~35 tok/s • $8,199
Best Performance
NVIDIA RTX 6000 Ada
~38 tok/s • Q4
Full-model (FP16) requirements are shown by default. Quantized builds like Q4 trade accuracy for lower VRAM usage.
Filter by quantization, price, and VRAM to compare performance estimates.
Showing FP16 compatibility. Switch tabs to explore other quantizations.
| GPU | Speed | VRAM Requirement | Typical price |
|---|---|---|---|
NVIDIA L40Estimated NVIDIA | ~13 tok/s FP16⚠ Insufficient VRAM | 141GB VRAM used48GB total on card | $8,199View GPU → |
RTX 4090Estimated NVIDIA | ~13 tok/s FP16⚠ Insufficient VRAM | 141GB VRAM used24GB total on card | $1,599View GPU → |
RTX 3090Estimated NVIDIA | ~12 tok/s FP16⚠ Insufficient VRAM | 141GB VRAM used24GB total on card | $1,099View GPU → |
NVIDIA RTX 6000 AdaEstimated NVIDIA | ~12 tok/s FP16⚠ Insufficient VRAM | 141GB VRAM used48GB total on card | $7,199View GPU → |
RX 7900 XTXEstimated AMD | ~11 tok/s FP16⚠ Insufficient VRAM | 141GB VRAM used24GB total on card | $899View GPU → |
NVIDIA A6000Estimated NVIDIA | ~10 tok/s FP16⚠ Insufficient VRAM | 141GB VRAM used48GB total on card | $4,899View GPU → |
RX 7900 XTEstimated AMD | ~10 tok/s FP16⚠ Insufficient VRAM | 141GB VRAM used20GB total on card | $899View GPU → |
RTX 4080Estimated NVIDIA | ~10 tok/s FP16⚠ Insufficient VRAM | 141GB VRAM used16GB total on card | $1,199View GPU → |
RTX 3080Estimated NVIDIA | ~9 tok/s FP16⚠ Insufficient VRAM | 141GB VRAM used10GB total on card | $699View GPU → |
NVIDIA A5000Estimated NVIDIA | ~9 tok/s FP16⚠ Insufficient VRAM | 141GB VRAM used24GB total on card | $2,499View GPU → |
Apple M2 UltraEstimated Apple | ~7 tok/s FP16 | 141GB VRAM used192GB total on card | $5,999View GPU → |
RTX 4070 TiEstimated NVIDIA | ~7 tok/s FP16⚠ Insufficient VRAM | 141GB VRAM used12GB total on card | $799View GPU → |
RTX 4070Estimated NVIDIA | ~7 tok/s FP16⚠ Insufficient VRAM | 141GB VRAM used12GB total on card | $599View GPU → |
RX 6800 XTEstimated AMD | ~6 tok/s FP16⚠ Insufficient VRAM | 141GB VRAM used16GB total on card | $579View GPU → |
RTX 3070Estimated NVIDIA | ~6 tok/s FP16⚠ Insufficient VRAM | 141GB VRAM used8GB total on card | $499View GPU → |
RX 6900 XTEstimated AMD | ~6 tok/s FP16⚠ Insufficient VRAM | 141GB VRAM used16GB total on card | $969View GPU → |
NVIDIA A4000Estimated NVIDIA | ~5 tok/s FP16⚠ Insufficient VRAM | 141GB VRAM used16GB total on card | $1,089View GPU → |
Apple M3 MaxEstimated Apple | ~4 tok/s FP16⚠ Insufficient VRAM | 141GB VRAM used128GB total on card | $3,999View GPU → |
RTX 3060 12GBEstimated NVIDIA | ~4 tok/s FP16⚠ Insufficient VRAM | 141GB VRAM used12GB total on card | $329View GPU → |
RTX 4060 Ti 16GBEstimated NVIDIA | ~4 tok/s FP16⚠ Insufficient VRAM | 141GB VRAM used16GB total on card | $499View GPU → |
Hardware requirements and model sizes at a glance.
| Component | Minimum | Recommended | Optimal |
|---|---|---|---|
| VRAM | 35GB (Q4) | 70GB (Q8) | 141GB (FP16) |
| RAM | 16GB | 32GB | 64GB |
| Disk | 50GB | 100GB | - |
| Model size | 35GB (Q4) | 70GB (Q8) | 141GB (FP16) |
| CPU | Modern CPU (Ryzen 5/Intel i5 or better) | Modern CPU (Ryzen 5/Intel i5 or better) | Modern CPU (Ryzen 5/Intel i5 or better) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
Common questions about running AI-MO/Kimina-Prover-72B locally
This model delivers strong local performance when paired with modern GPUs. Use the hardware guidance below to choose the right quantization tier for your build.
Use runtimes like llama.cpp, text-generation-webui, or vLLM. Download the quantized weights from Hugging Face, ensure you have enough VRAM for your target quantization, and launch with GPU acceleration (CUDA/ROCm/Metal).
Start with Q4 for wide GPU compatibility. Upgrade to Q8 if you have spare VRAM and want extra quality. FP16 delivers the highest fidelity but demands workstation or multi-GPU setups.
Official weights are available via Hugging Face. Quantized builds (Q4, Q8) can be loaded into runtimes like llama.cpp, text-generation-webui, or vLLM. Always verify the publisher before downloading.