Can DeepSeek R1 Distill Qwen 14B run on NVIDIA H100 PCIe 80GB?

YES — Runs Great

C47Usable

○Estimated from fit model

DeepSeek R1 Distill Qwen 14B needs ~19.4 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~196 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) — 19.4 GB, 196.0 tok/s, Runs well

19.4 GB required80.0 GB available

24% VRAM used

Fit status

Runs well

Decode

196.0 tok/s

TTFT

988 ms

Safe context

607K

Memory

19.4 GB / 80.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill Qwen 14B on NVIDIA H100 PCIe 80GB
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: 196.0 tok/s decode · 988ms TTFT (warm) · 490 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	196.0 tok/s	539 ms	607K
Coding	C	Runs well	196.0 tok/s	988 ms	607K
Agentic Coding	C	Runs well	196.0 tok/s	1437 ms	607K
Reasoning	C	Runs well	196.0 tok/s	1167 ms	607K
RAG	C	Runs well	196.0 tok/s	1796 ms	607K

Quantization options

How DeepSeek R1 Distill Qwen 14B (14B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	D40
Q3_K_S	3	6.9 GB	Low	D40
NVFP4	4	7.8 GB	Medium	C40
Q4_K_M	4	8.5 GB	Medium	C40
Q5_K_M	5	10.1 GB	High	C40
Q6_K	6	11.5 GB	High	C40
Q8_0	8	15.0 GB	Very High	C41
F16Best for your GPU	16	28.7 GB	Maximum	C43

Get started

Copy-paste commands to run DeepSeek R1 Distill Qwen 14B on your machine.

Run

lms load hf-unsloth--deepseek-r1-distill-qwen-14b-gguf && lms server start

Frequently asked questions

See all results for NVIDIA H100 PCIe 80GB See all hardware for DeepSeek R1 Distill Qwen 14B

URL: https://willitrunai.com/can-run/hf-unsloth--deepseek-r1-distill-qwen-14b-gguf-on-h100-pcie-80gb

⇱ DeepSeek R1 Distill Qwen 14B on NVIDIA H100 PCIe 80GB? YES