Can DeepSeek R1 Distill 32B run on H100 NVL 188GB?

YES — Runs Great

A72Great

Estimated from fit model

DeepSeek R1 Distill 32B needs ~43.4 GB VRAM. H100 NVL 188GB has 188.0 GB. With Q4_K_M quantization, expect ~350 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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) — 43.4 GB, 349.6 tok/s, Runs well

43.4 GB required188.0 GB available

23% VRAM used

Fit status

Runs well

Decode

349.6 tok/s

TTFT

554 ms

Safe context

33K

Memory

43.4 GB / 188.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom18.8 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill 32B on H100 NVL 188GB
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: 349.6 tok/s decode · 554ms TTFT (warm) · 874 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	349.6 tok/s	350 ms	33K
Coding	A	Runs well	349.6 tok/s	554 ms	33K
Agentic Coding	A	Runs well	349.6 tok/s	806 ms	33K
Reasoning	A	Runs well	349.6 tok/s	655 ms	33K
RAG	A	Runs well	349.6 tok/s	1007 ms	33K

Inference speed

DeepSeek R1 Distill 32B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for DeepSeek R1 Distill 32B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~66 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	66.4	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	33.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	33.2	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	30.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	25.7	Fits
RTX 4090 24GB	24 GB	Q4_K_M	24.8	Heavy offload
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	24.3	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	22.9	Heavy offload
RTX 3090 24GB	24 GB	Q4_K_M	21.2	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	20.9	Tight
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	13.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	12.2	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	9.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.1	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How DeepSeek R1 Distill 32B (32B params) fits at each quantization level on H100 NVL 188GB (188.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	B63
Q3_K_S	3	15.7 GB	Low	B63
NVFP4	4	17.9 GB	Medium	B63
Q4_K_M	4	19.5 GB	Medium	B63
Q5_K_M	5	23.0 GB	High	B63
Q6_K	6	26.2 GB	High	B64
Q8_0	8	34.2 GB	Very High	B65
F16Best for your GPU	16	65.6 GB	Maximum	B68

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 H100 NVL 188GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	91.6 tok/s
Qwen 3.5 122B A10B	122B	S	254 tok/s
DeepSeek V4 Flash	284B	S	136.1 tok/s
Qwen 3.6 35B A3B	35B	S	802.9 tok/s
Qwen 3.5 35B A3B	35B	S	873.2 tok/s

Frequently asked questions

Can H100 NVL 188GB run DeepSeek R1 Distill 32B?

Yes, H100 NVL 188GB can run DeepSeek R1 Distill 32B with a A grade (Runs well). Expected decode speed: 349.6 tok/s.

How much VRAM does DeepSeek R1 Distill 32B need?

DeepSeek R1 Distill 32B (32B parameters) requires approximately 43.4 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 H100 NVL 188GB?

On H100 NVL 188GB, DeepSeek R1 Distill 32B achieves approximately 349.6 tokens per second decode speed with a time-to-first-token of 554ms using Q4_K_M quantization.

Can H100 NVL 188GB run DeepSeek R1 Distill 32B for coding?

For coding workloads, DeepSeek R1 Distill 32B on H100 NVL 188GB receives a A grade with 349.6 tok/s and 33K context.

What context window can DeepSeek R1 Distill 32B use on H100 NVL 188GB?

On H100 NVL 188GB, 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 H100 NVL 188GB See all hardware for DeepSeek R1 Distill 32B

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