DeepSeekby DeepSeek AI
DeepSeek R1 Distill 32B (Qwen) VRAM & Cloud GPU Sizer
Distilled reasoning traces inside Qwen 2.5 32B architecture, matching larger 70B frontier benchmarks. Use this interactive calculator to compute exact GPU memory requirements across FP16, FP8, INT4, QLoRA, and Full Fine-Tuning.
Inference VRAM (INT4 AWQ)
~20.46 GB
1x RTX 4090 (24GB INT4) or 1x L40S (48GB)Native Precision (FP16 / BF16)
~64.35 GB
Fits on 1x 24GB GPUQLoRA 4-bit Training
~33.9 GB
2x RTX 4090 or 1x A100 80GBInteractive VRAM Sizer & Live Cloud Pricing Matrix Active
Popular Model Presets
Auto-ArchitectedB
0.5B (Edge)7B14B32B70B140B+
2 B/param
tokens
reqs
Minimum Required VRAMActive
64.35GB
Recommended Target:74 GB(+15% buffer)
Suggested Minimum GPU Tier:
1x A100 / H100 (80GB)
VRAM Memory Footprint Breakdown
Sum: 64.35 GBModel Weights
60.54 GB
KV-Cache Context
2 GB
Activations
0.31 GB
CUDA & Runtime
1.5 GB
Estimated Decoding Speed (Tokens/sec):
A100 (80GB): 21.9 t/sH100 (80GB): 35.9 t/s
Live Cloud GPU Cost & Pricing Engine
18 Available NodesReal-time verified pricing across RunPod, Lambda Labs, Vast.ai, AWS, GCP, and specialized clouds.
Lowest Cost Compatible OptionNVIDIA A100 SXM4 (80GB) on Lambda Labs (80GB VRAM)
Spot Rate$1.25 / 1h
Rent NodeBuy vs. Rent TCO LabHardware break-even simulator
Providers:
| GPU & Architecture | Provider | Total VRAM | Spot (1h) | On-Demand (1h) | Action |
|---|---|---|---|---|---|
NVIDIA A100 SXM4 (80GB)Popular Ampere•NVLink-3 (600 GB/s) | Lambda Labs | 80 GB | $1.25($1.25/hr) | $1.69($1.69/hr) | Rent |
NVIDIA A100 SXM4 (80GB) Ampere•NVLink-3 (600 GB/s) | RunPod | 80 GB | $1.39($1.39/hr) | $1.89($1.89/hr) | Rent |
NVIDIA A100 SXM4 (80GB) Ampere•NVLink-3 (600 GB/s) | GCP (a2-ultragpu-1g) | 80 GB | $1.75($1.75/hr) | $3.67($3.67/hr) | Rent |
4x NVIDIA RTX 4090 Ada Lovelace•PCIe 4.0 | RunPod | 96 GB(4x 24GB) | $1.76($1.76/hr) | $2.76($2.76/hr) | Rent |
NVIDIA H100 SXM5 (80GB) Hopper•NVLink-4 (900 GB/s) + InfiniBand | Nebius AI Studio | 80 GB | $2.19($2.19/hr) | $2.85($2.85/hr) | Rent |
NVIDIA H100 SXM5 (80GB)Popular Hopper•NVLink-4 (900 GB/s) | Lambda Labs | 80 GB | $2.29($2.29/hr) | $2.99($2.99/hr) | Rent |
NVIDIA H100 SXM5 (80GB) Hopper•NVLink-4 (900 GB/s) | CoreWeave | 80 GB | $2.35($2.35/hr) | $3.15($3.15/hr) | Rent |
NVIDIA H100 SXM5 (80GB)Popular Hopper•NVLink-4 (900 GB/s) | RunPod | 80 GB | $2.49($2.49/hr) | $3.29($3.29/hr) | Rent |
4x NVIDIA RTX 5090 Blackwell•PCIe 5.0 | RunPod | 128 GB(4x 32GB) | $2.76($2.76/hr) | $3.96($3.96/hr) | Rent |
NVIDIA H200 SXM5 (141GB)Popular Hopper HBM3e•NVLink-4 (900 GB/s) | Lambda Labs | 141 GB | $3.49($3.49/hr) | $4.29($4.29/hr) | Rent |
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 |
Showing all 18 nodes • Scroll table vertically to view full hardware catalog
Prices updated February 2026Technical Knowledge Hub
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 Distill 32B (Qwen) Architectural Specs & Memory Scaling
Detailed breakdown of transformer parameters, attention mechanisms, and KV-cache expansion.
Total Parameters32.5 Billion
Transformer Layers64 Layers
Attention Heads / GQA40 Q / 8 KV (5:1)
Max Native Context1,31,072 Tokens
Quantization Precision vs VRAM Footprint
| Precision / Quant Type | Bytes / Param | Model Weights VRAM | Compatible Single GPU |
|---|---|---|---|
| FP32 (32-bit Float) | 4 B | 121.07 GB | Requires Multi-GPU / A100 (80GB) |
| FP16 (16-bit Float) | 2 B | 60.54 GB | Requires Multi-GPU / A100 (80GB) |
| FP8 (8-bit Float) | 1 B | 30.27 GB | ✓ Fits on 1x RTX 5090 (32GB) |
| INT8 (8-bit Integer) | 1 B | 30.27 GB | ✓ Fits on 1x RTX 5090 (32GB) |
| INT4 / AWQ / GPTQ | 0.55 B | 16.65 GB | ✓ Fits on 1x RTX 4090 (24GB) |
| GGUF Q4_K_M | 0.58 B | 17.56 GB | ✓ Fits on 1x RTX 4090 (24GB) |
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 | 1 GB | 0.5 GB | -50% reduction |
| 8,192 tokens | 2 GB | 1 GB | -50% reduction |
| 16,384 tokens | 4 GB | 2 GB | -50% reduction |
| 32,768 tokens | 8 GB | 4 GB | -50% reduction |
| 65,536 tokens | 16 GB | 8 GB | -50% reduction |
| 1,31,072 tokens | 32 GB | 16 GB | -50% reduction |
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