Can Qwen 2.5 VL 72B run on NVIDIA H200 PCIe 141GB?

YES — Runs Great

S92Excellent

○Estimated from fit model

Qwen 2.5 VL 72B needs ~63.8 GB VRAM. NVIDIA H200 PCIe 141GB has 141.0 GB. With Q4_K_M quantization, expect ~100 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 63.8 GB, 99.8 tok/s, Runs well

63.8 GB required141.0 GB available

45% VRAM used

Fit status

Runs well

Decode

99.8 tok/s

TTFT

1939 ms

Safe context

33K

Memory

63.8 GB / 141.0 GB

Memory breakdown

Weights43.9 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom14.1 GB

See how fast it feels

See how fast it feelsQwen 2.5 VL 72B on NVIDIA H200 PCIe 141GB
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: 99.8 tok/s decode · 1.9s TTFT (warm) · 250 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	99.8 tok/s	1058 ms	33K
Coding	S	Runs well	99.8 tok/s	1939 ms	33K
Agentic Coding	S	Runs well	99.8 tok/s	2821 ms	33K
Reasoning	S	Runs well	99.8 tok/s	2292 ms	33K
RAG	S	Runs well	99.8 tok/s	3526 ms	33K

Quantization options

How Qwen 2.5 VL 72B (72B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	28.1 GB	Low	A80
Q3_K_S	3	35.3 GB	Low	A82
NVFP4	4	40.3 GB	Medium	A82
Q4_K_M	4	43.9 GB	Medium	A83
Q5_K_M	5	51.8 GB	High	A84
Q6_K	6	59.0 GB	High	S85
Q8_0Best for your GPU	8	77.0 GB	Very High	S88
F16	16	147.6 GB	Maximum	F0

Get started

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

Run

lms load Qwen2.5-VL-72B-Instruct && lms server start

Your hardware

More models your NVIDIA H200 PCIe 141GB can run

Model	Params	Grade	Decode
👁 Mistral Devstral 2 123B Instruct	123B	S	58.4 tok/s
👁 Alibaba Qwen 3.5 122B A10B	122B	S	162.1 tok/s
👁 Mistral Mistral Small 4 119B	119B	S	175.8 tok/s
👁 OpenAI GPT-OSS 120B	117B	S	61.4 tok/s
👁 Cohere Command A 111B	111B	S	65 tok/s

Frequently asked questions

See all results for NVIDIA H200 PCIe 141GB See all hardware for Qwen 2.5 VL 72B

URL: https://willitrunai.com/can-run/qwen-2.5-vl-72b-on-h200-pcie-141gb

⇱ Qwen 2.5 VL 72B on NVIDIA H200 PCIe 141GB? YES