Can DeepSeek R1 Distill Qwen 1.5B run on NVIDIA V100 32GB?

YES — Runs Great

C41Usable

Estimated from fit model

DeepSeek R1 Distill Qwen 1.5B needs ~5.5 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~21 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) — 5.5 GB, 21.0 tok/s, Runs well

5.5 GB required32.0 GB available

17% VRAM used

Fit status

Runs well

Decode

21.0 tok/s

TTFT

9219 ms

Safe context

2.4M

Memory

5.5 GB / 32.0 GB

Memory breakdown

Weights0.9 GB

KV Cache0.2 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill Qwen 1.5B 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: 21.0 tok/s decode · 9.2s TTFT (warm) · 53 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	C	Runs well	21.0 tok/s	5029 ms	2.1M
Coding	C	Runs well	21.0 tok/s	9219 ms	2.4M
Agentic Coding	C	Runs well	21.0 tok/s	13410 ms	2.4M
Reasoning	C	Runs well	21.0 tok/s	10895 ms	2.4M
RAG	C	Runs well	21.0 tok/s	16762 ms	2.4M

Quantization options

How DeepSeek R1 Distill Qwen 1.5B (1.5B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	C43
Q3_K_S	3	0.7 GB	Low	C43
NVFP4	4

Get started

Copy-paste commands to run DeepSeek R1 Distill Qwen 1.5B on your machine.

Run

lms load hf-unsloth--deepseek-r1-distill-qwen-1-5b-gguf && lms server start

Upgrade options

Hardware that runs DeepSeek R1 Distill Qwen 1.5B well

Mac mini M4 64GBBudget pick

64 GB Unified (+32)

Adds memory headroom for longer context windows and future model growth.21 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$1,099 MSRP

MacBook Pro M4 Pro 64GBBest value

64 GB Unified (+32)

Adds memory headroom for longer context windows and future model growth.21 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$1,599 MSRP

Frequently asked questions

Can NVIDIA V100 32GB run DeepSeek R1 Distill Qwen 1.5B?

Yes, NVIDIA V100 32GB can run DeepSeek R1 Distill Qwen 1.5B with a C grade (Runs well). Expected decode speed: 21.0 tok/s.

How much VRAM does DeepSeek R1 Distill Qwen 1.5B need?

DeepSeek R1 Distill Qwen 1.5B (1.5B parameters) requires approximately 5.5 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 Distill Qwen 1.5B?

The recommended quantization for DeepSeek R1 Distill Qwen 1.5B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 Distill Qwen 1.5B run at on NVIDIA V100 32GB?

On NVIDIA V100 32GB, DeepSeek R1 Distill Qwen 1.5B achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run DeepSeek R1 Distill Qwen 1.5B for coding?

For coding workloads, DeepSeek R1 Distill Qwen 1.5B on NVIDIA V100 32GB receives a C grade with 21.0 tok/s and 2.4M context.

What context window can DeepSeek R1 Distill Qwen 1.5B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, DeepSeek R1 Distill Qwen 1.5B can safely use up to 2.4M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See all hardware for DeepSeek R1 Distill Qwen 1.5B

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