Can Qwen 2.5 72B run on NVIDIA H100 80GB?

YES — Runs Great

S87Excellent

○Estimated from fit model

Qwen 2.5 72B needs ~58.0 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~70 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) — 58.0 GB, 69.7 tok/s, Runs well

58.0 GB required80.0 GB available

73% VRAM used

Fit status

Runs well

Decode

69.7 tok/s

TTFT

2779 ms

Safe context

88K

Memory

58.0 GB / 80.0 GB

Memory breakdown

Weights43.9 GB

KV Cache4.9 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsQwen 2.5 72B on NVIDIA H100 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: 69.7 tok/s decode · 2.8s TTFT (warm) · 174 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	S	Runs well	69.7 tok/s	1516 ms	88K
Coding	S	Runs well	69.7 tok/s	2779 ms	88K
Agentic Coding	S	Runs well	69.7 tok/s	4041 ms	88K
Reasoning	S	Runs well	69.7 tok/s	3284 ms	88K
RAG	S	Runs well	69.7 tok/s	5052 ms	88K

Quantization options

How Qwen 2.5 72B (72B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	28.1 GB	Low	A75
Q3_K_S	3	35.3 GB	Low	A77
NVFP4	4	40.3 GB	Medium	A78
Q4_K_M	4	43.9 GB	Medium	A79
Q5_K_M	5	51.8 GB	High	A79
Q6_KBest for your GPU	6	59.0 GB	High	A79
Q8_0	8	77.0 GB	Very High	F0
F16	16	147.6 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 2.5 72B on your machine.

Run

ollama run qwen2.5:72b

Your hardware

More models your NVIDIA H100 80GB can run

Model	Params	Grade	Decode
👁 Mistral Devstral 2 123B Instruct	123B	A	28.9 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	S	85.5 tok/s
👁 Mistral Mistral Small 4 119B	119B	A	90.8 tok/s
👁 OpenAI GPT-OSS 120B	117B	A	32.8 tok/s
👁 Cohere Command A 111B	111B	S	38.1 tok/s

Frequently asked questions

See all results for NVIDIA H100 80GB See all hardware for Qwen 2.5 72B

URL: https://willitrunai.com/can-run/qwen-2.5-72b-on-h100-80gb