HARDWARE FIT CHECK โข DeepSeek-R1 Distill Qwen 32B + NVIDIA H100 SXM (80GB HBM3 Cloud GPU)25.41 GB / 80 GB VRAM
Can I Run DeepSeek-R1 Distill Qwen 32B on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running DeepSeek-R1 Distill Qwen 32B (32.8B) on NVIDIA H100 SXM (80GB HBM3 Cloud GPU) (80 GB).
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Runs 100% in GPU VRAM (Optimal Headroom)25.41 GB (32%)~117 t/s
DeepSeek-R1 Distill Qwen 32B on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)
โ Fits Comfortably in VRAM
โ FITS IN VRAM
Required VRAM
25.41 GB
32% of hardware capacity (80 GB)Est. Decode Speed
~117 tok/s
Dense model @ 3350 GB/sAvailable Headroom
+54.59 GB
Safe for OS display & full contextMemory Allocation & Headroom
25.41 GB allocated of 80 GB capacity
32% utilized
Weights: 19.68 GB
KV: (32K): 4.29 GB
Overhead: 1.44 GB
Free Headroom: +54.59 GB
โก Architecture & Throughput Transparency: Single-user decode speed (~117 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA H100 SXM (80GB HBM3 Cloud GPU) (3350 GB/s). Prompt prefill / TTFT (~1,697 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run deepseek-r1:32bCloud Instance Options for DeepSeek-R1 Distill Qwen 32B
On-DemandDeploy this model on an on-demand cloud GPU without local hardware constraints or power usage:
RunPod Cloud GPUsBest 1-Click Ollama & vLLM
$0.34/hr (RTX 4070 Ti) โ $2.39/hr (H100)Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090
$0.18/hr (RTX 3060) โ $0.55/hr (RTX 4090)Lambda GPU CloudEnterprise 8x H100 Clusters
$1.99/hr (GH200 / H100)Quantization Precision Matrix
32K ctx โข KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 65.6 GB | 4.29 GB | 72.94 GB (91%) | ~36 t/s | FITS |
| Q8_08.5 bpw | 34.77 GB | 4.29 GB | 41.03 GB (51%) | ~67 t/s | FITS |
| Q6_K6.56 bpw | 26.9 GB | 4.29 GB | 32.88 GB (41%) | ~86 t/s | FITS |
| Q5_K_M5.68 bpw | 23.29 GB | 4.29 GB | 29.15 GB (36%) | ~99 t/s | FITS |
| Q4_K_M4.8 bpw | 19.68 GB | 4.29 GB | 25.41 GB (32%) | ~117 t/s | FITS |
| FP44.25 bpw | 17.38 GB | 4.29 GB | 23.03 GB (29%) | ~131 t/s | FITS |
| IQ3_XS3.3 bpw | 13.45 GB | 4.29 GB | 18.96 GB (24%) | ~167 t/s | FITS |
Local Hardware Configurations & Pricing
High-bandwidth VRAM specs and current market pricesDatacenter Cloud GPU80 GB VRAM
NVIDIA H100 SXM (80GB HBM3 Cloud GPU)
Production vLLM high-concurrency serving & 70B FP8 inference
3350 GB/s700W TDP
$28,000Amazon Prime โ
Consumer GPU32 GB VRAM
NVIDIA GeForce RTX 5090 (32GB GDDR7)
Maximum tokens/sec on 27Bโ35B models & Blackwell FP4 quantization
1792 GB/s575W TDP
$1,999Amazon Prime โ
Consumer GPU24 GB VRAM
NVIDIA GeForce RTX 4090 (24GB GDDR6X)
The 24GB CUDA enthusiast standard for 14Bโ32B Q4_K_M models
1008 GB/s450W TDP
$1,699Amazon Prime โ
Multi-GPU Rig48 GB VRAM
2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
Best price-to-VRAM homelab rig for 70B Q4_K_M & Qwen 3.8 27B Q8
936 GB/s700W TDP
$1,550Amazon Prime โ
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