HARDWARE FIT CHECK • DeepSeek-R1 Distill Qwen 7B + NVIDIA H100 SXM (80GB HBM3 Cloud GPU)6.41 GB / 80 GB VRAM

Can I Run DeepSeek-R1 Distill Qwen 7B 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 7B (7.6B) on NVIDIA H100 SXM (80GB HBM3 Cloud GPU) (80 GB).

✓ FITS IN VRAM6.41 GB (8%)~503 t/s

DeepSeek-R1 Distill Qwen 7B on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)

✓ Fits Comfortably in VRAM

✓ FITS IN VRAM

Required VRAM

6.41 GB

8% of hardware capacity (80 GB)

Est. Decode Speed

~503 tok/s

Dense model @ 3350 GB/s

Available Headroom

+73.59 GB

Safe for OS display & full context

Memory Allocation & Headroom

6.41 GB allocated of 80 GB capacity

8% utilized

Weights: 4.56 GB

KV: (32K): 0.94 GB

Overhead: 0.91 GB

Free Headroom: +73.59 GB

Architecture & Throughput Transparency: Single-user decode speed (~503 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA H100 SXM (80GB HBM3 Cloud GPU) (3350 GB/s). Prompt prefill / TTFT (~7,294 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).

Local Hardware Configurations & Pricing

High-bandwidth VRAM specs and current market prices

Consumer GPU

32 GB VRAM

NVIDIA GeForce RTX 5090 (32GB GDDR7)

Maximum tokens/sec on 27B–35B models & Blackwell FP4 quantization

1792 GB/s575W TDP

$1,999Amazon Prime →

Consumer GPU

24 GB VRAM

NVIDIA GeForce RTX 4090 (24GB GDDR6X)

The 24GB CUDA enthusiast standard for 14B–32B Q4_K_M models

1008 GB/s450W TDP

$1,699Amazon Prime →

Multi-GPU Rig

48 GB VRAM

2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)

Best price-to-VRAM homelab rig for Llama 3.3 70B Q4_K_M & Qwen 2.5 Coder 32B Q8_0

1648 GB/s700W TDP

$1,550Amazon Prime →

Datacenter Cloud GPU

80 GB VRAM

NVIDIA H100 SXM (80GB HBM3 Cloud GPU)

Production vLLM high-concurrency serving & 70B FP8 inference

3350 GB/s700W TDP

$28,000Amazon Prime →

Cloud Instance Options for DeepSeek-R1 Distill Qwen 7B

On-Demand

Deploy this model on an on-demand cloud GPU without local hardware constraints or power usage:

RunPod Cloud GPUsBest 1-Click Ollama & vLLM

$0.38/hr (RTX 4090 Spot) – $2.39/hr (H100)

Launch 1-Click GPU Pod →

Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090

$0.18/hr (RTX 3060) – $0.78/hr (2x RTX 3090)

Rent Spot GPU →

Lambda GPU CloudEnterprise 8x H100 Clusters

$2.49/hr (H100) – $19.92/hr (8x H100)

Check H100 Availability →

Quantization Precision Matrix

32K ctx • KV: Q8_0

Quantization	Weights	KV Cache	Total VRAM	Est. Speed	Fit Status
FP1616 bpw	15.2 GB	0.94 GB	17.42 GB (22%)	~156 t/s	FITS
Q8_08.5 bpw	8.06 GB	0.94 GB	10.03 GB (13%)	~291 t/s	FITS
Q6_K6.56 bpw	6.23 GB	0.94 GB	8.14 GB (10%)	~373 t/s	FITS
Q5_K_M5.68 bpw	5.4 GB	0.94 GB	7.28 GB (9%)	~428 t/s	FITS
Q4_K_M4.8 bpw	4.56 GB	0.94 GB	6.41 GB (8%)	~503 t/s	FITS
FP44.25 bpw	4.03 GB	0.94 GB	5.86 GB (7%)	~566 t/s	FITS
IQ3_XS3.3 bpw	3.12 GB	0.94 GB	4.92 GB (6%)	~719 t/s	FITS

FAQ & Reference

Frequently Asked Questions: DeepSeek-R1 Distill Qwen 7B on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)

How much VRAM does DeepSeek-R1 Distill Qwen 7B need on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)?

At standard 4-bit quantization (Q4_K_M) with 32K context, DeepSeek-R1 Distill Qwen 7B requires 6.41 GB of total VRAM (weights + KV cache + CUDA runtime overhead).

What inference speed will DeepSeek-R1 Distill Qwen 7B achieve on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)?

Based on NVIDIA H100 SXM (80GB HBM3 Cloud GPU)'s 3350 GB/s memory bandwidth, expected decode speed is approximately 503 tokens per second.

Can I expand context beyond 32K tokens?

Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.