DeepSeek R1 (671B MoE) VRAM & Cloud GPU Sizer
Frontier open reasoning model featuring 671B total parameters, 37B active per token, and Multi-Head Latent Attention (MLA). Use this interactive calculator to compute exact GPU memory requirements across FP16, FP8, INT4, QLoRA, and Full Fine-Tuning.
Popular Model Presets
Auto-ArchitectedVRAM Memory Footprint Breakdown
Sum: 376.14 GBLive Cloud GPU Cost & Pricing Engine
8 Available NodesReal-time verified pricing across RunPod, Lambda Labs, Vast.ai, AWS, GCP, and specialized clouds.
| GPU & Architecture | Provider | Total VRAM | Spot (1h) | On-Demand (1h) | Action |
|---|---|---|---|---|---|
8x NVIDIA A100 SXM4 (80GB) Ampere•NVLink-3 (600 GB/s) | Lambda Labs | 640 GB(8x 80GB) | $9.90($9.90/hr) | $13.52($13.52/hr) | Rent |
8x NVIDIA A100 SXM4 (80GB) Ampere•NVLink-3 (600 GB/s) | AWS (EC2 p4de.24xlarge) | 640 GB(8x 80GB) | $14.50($14.50/hr) | $40.97($40.97/hr) | Rent |
8x NVIDIA H100 SXM5 (80GB)Popular Hopper•NVLink-4 (900 GB/s) + Quantum-2 IB | Lambda Labs | 640 GB(8x 80GB) | $18.30($18.30/hr) | $23.92($23.92/hr) | Rent |
8x NVIDIA H100 SXM5 (80GB) Hopper•NVLink-4 (900 GB/s) + 3.2Tbps InfiniBand | RunPod | 640 GB(8x 80GB) | $19.80($19.80/hr) | $26.32($26.32/hr) | Rent |
8x NVIDIA H200 SXM5 (141GB) Hopper HBM3e•NVLink-4 (900 GB/s) + Quantum-2 IB | Lambda Labs | 1128 GB(8x 141GB) | $27.50($27.50/hr) | $34.32($34.32/hr) | Rent |
8x NVIDIA H100 SXM5 (80GB) Hopper•NVLink-4 (900 GB/s) + GPUDirect-TCPX | GCP (a3-highgpu-8g) | 640 GB(8x 80GB) | $32.00($32.00/hr) | $87.05($87.05/hr) | Rent |
8x NVIDIA H100 SXM5 (80GB) Hopper•NVLink-4 (900 GB/s) + 3.2Tbps EFA | AWS (EC2 p5.48xlarge) | 640 GB(8x 80GB) | $38.50($38.50/hr) | $98.32($98.32/hr) | Rent |
8x NVIDIA Blackwell B200 (192GB)Popular Blackwell•NVLink-5 (1.8 TB/s) + Quantum-X800 IB | Nebius / CoreWeave | 1536 GB(8x 192GB) | $44.00($44.00/hr) | $58.00($58.00/hr) | Rent |
Frequently Asked Questions & Transformer Math
Deep-dive technical answers on KV-cache calculation, CUDA Out-Of-Memory prevention, QLoRA fine-tuning benchmarks, and cloud GPU cost optimization.
DeepSeek R1 (671B MoE) Architectural Specs & Memory Scaling
Detailed breakdown of transformer parameters, attention mechanisms, and KV-cache expansion.
Quantization Precision vs VRAM Footprint
| Precision / Quant Type | Bytes / Param | Model Weights VRAM | Compatible Single GPU |
|---|---|---|---|
| FP32 (32-bit Float) | 4 B | 2499.67 GB | Requires Multi-GPU / A100 (80GB) |
| FP16 (16-bit Float) | 2 B | 1249.83 GB | Requires Multi-GPU / A100 (80GB) |
| FP8 (8-bit Float) | 1 B | 624.92 GB | Requires Multi-GPU / A100 (80GB) |
| INT8 (8-bit Integer) | 1 B | 624.92 GB | Requires Multi-GPU / A100 (80GB) |
| INT4 / AWQ / GPTQ | 0.55 B | 343.7 GB | Requires Multi-GPU / A100 (80GB) |
| GGUF Q4_K_M | 0.58 B | 362.45 GB | Requires Multi-GPU / A100 (80GB) |
KV-Cache Memory Growth with Sequence Length
| Context Window (Tokens) | FP16 KV Cache (2 B) | FP8 KV Cache (1 B - vLLM) | VRAM Savings |
|---|---|---|---|
| 4,096 tokens | 15.25 GB | 7.63 GB | -50% reduction |
| 8,192 tokens | 30.5 GB | 15.25 GB | -50% reduction |
| 16,384 tokens | 61 GB | 30.5 GB | -50% reduction |
| 32,768 tokens | 122 GB | 61 GB | -50% reduction |
| 65,536 tokens | 244 GB | 122 GB | -50% reduction |
| 1,31,072 tokens | 488 GB | 244 GB | -50% reduction |
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