Can DeepSeek R1 Distill 32B run on NVIDIA V100 32GB?

YES — Tight Fit

A76Great

Estimated from fit model

DeepSeek R1 Distill 32B needs ~27.8 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~33 tok/s.

Runtime: OllamaCapacity: TightBandwidth: HighStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 27.8 GB, 33.4 tok/s, Tight fit

27.8 GB required32.0 GB available

87% VRAM used

Fit status

Tight fit

Decode

33.4 tok/s

TTFT

5803 ms

Safe context

33K

Memory

27.8 GB / 32.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill 32B on NVIDIA V100 32GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 33.4 tok/s decode · 5.8s TTFT (warm) · 83 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	33.4 tok/s	3165 ms	33K
Coding	A	Tight fit	33.4 tok/s	5803 ms	33K
Agentic Coding	A	Runs with offload	33.4 tok/s	8441 ms	33K
Reasoning	A	Tight fit	33.4 tok/s	6858 ms	33K
RAG	A	Runs with offload	33.4 tok/s	10551 ms	33K

Quantization options

How DeepSeek R1 Distill 32B (32B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A73
Q3_K_S	3	15.7 GB	Low	A75
NVFP4	4

Get started

Copy-paste commands to run DeepSeek R1 Distill 32B on your machine.

Run

ollama run deepseek-r1:32b

Your hardware

More models your NVIDIA V100 32GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.6 35B A3B	35B	S	76.6 tok/s

Frequently asked questions

Can NVIDIA V100 32GB run DeepSeek R1 Distill 32B?

Yes, NVIDIA V100 32GB can run DeepSeek R1 Distill 32B with a A grade (Tight fit). Expected decode speed: 33.4 tok/s.

How much VRAM does DeepSeek R1 Distill 32B need?

DeepSeek R1 Distill 32B (32B parameters) requires approximately 27.8 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 Distill 32B?

The recommended quantization for DeepSeek R1 Distill 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 Distill 32B run at on NVIDIA V100 32GB?

On NVIDIA V100 32GB, DeepSeek R1 Distill 32B achieves approximately 33.4 tokens per second decode speed with a time-to-first-token of 5803ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run DeepSeek R1 Distill 32B for coding?

For coding workloads, DeepSeek R1 Distill 32B on NVIDIA V100 32GB receives a A grade with 33.4 tok/s and 33K context.

What context window can DeepSeek R1 Distill 32B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, DeepSeek R1 Distill 32B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See all hardware for DeepSeek R1 Distill 32B

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