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

YES — Runs Great

S96Excellent

Estimated from fit model

Qwen 2.5 VL 72B needs ~57.7 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~70 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) — 57.7 GB, 69.7 tok/s, Runs well

57.7 GB required80.0 GB available

72% VRAM used

Fit status

Runs well

Decode

69.7 tok/s

TTFT

2779 ms

Safe context

33K

Memory

57.7 GB / 80.0 GB

Memory breakdown

Weights43.9 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsQwen 2.5 VL 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	33K
Coding	S	Runs well	69.7 tok/s	2779 ms	33K
Agentic Coding	S	Runs well	69.7 tok/s	4041 ms	33K
Reasoning	S	Runs well	69.7 tok/s	3284 ms	33K
RAG	S	Runs well	69.7 tok/s	5052 ms	33K

Quantization options

How Qwen 2.5 VL 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	A84
Q3_K_S	3	35.3 GB	Low	S86
NVFP4	4	40.3 GB	Medium	S88
Q4_K_M	4	43.9 GB	Medium	S88
Q5_K_M	5	51.8 GB	High	S88
Q6_KBest for your GPU	6	59.0 GB	High	S88
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 VL 72B on your machine.

Run

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

Your hardware

More models your NVIDIA H100 80GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	A	29 tok/s
Qwen 3.5 122B A10B	122B	S	86 tok/s
Mistral Small 4 119B	119B	A	91.3 tok/s
GPT-OSS 120B	117B	A	33 tok/s
Command A 111B	111B	S	38.3 tok/s

Frequently asked questions

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

Yes, NVIDIA H100 80GB can run Qwen 2.5 VL 72B with a S grade (Runs well). Expected decode speed: 69.7 tok/s.

How much VRAM does Qwen 2.5 VL 72B need?

Qwen 2.5 VL 72B (72B parameters) requires approximately 57.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 VL 72B?

The recommended quantization for Qwen 2.5 VL 72B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 VL 72B run at on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Qwen 2.5 VL 72B achieves approximately 69.7 tokens per second decode speed with a time-to-first-token of 2779ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run Qwen 2.5 VL 72B for coding?

For coding workloads, Qwen 2.5 VL 72B on NVIDIA H100 80GB receives a S grade with 69.7 tok/s and 33K context.

What context window can Qwen 2.5 VL 72B use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Qwen 2.5 VL 72B 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 NVIDIA H100 80GB See all hardware for Qwen 2.5 VL 72B

Embed this result▼

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

69.7 tok/s

1516 ms

33K

Coding

Runs well

69.7 tok/s

2779 ms

33K

Agentic Coding

Runs well

69.7 tok/s

4041 ms

33K

Reasoning

Runs well

69.7 tok/s

3284 ms

33K

RAG

Runs well

69.7 tok/s

5052 ms

33K

Quantization options

How Qwen 2.5 VL 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	A84
Q3_K_S	3	35.3 GB	Low	S86
NVFP4	4	40.3 GB	Medium	S88
Q4_K_M	4	43.9 GB	Medium	S88
Q5_K_M	5	51.8 GB	High	S88
Q6_KBest for your GPU	6	59.0 GB	High	S88
Q8_0	8	77.0 GB	Very High	F0
F16	16	147.6 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

29 tok/s

Qwen 3.5 122B A10B

122B

86 tok/s

Mistral Small 4 119B

119B

91.3 tok/s

GPT-OSS 120B

117B

33 tok/s

Command A 111B

111B

38.3 tok/s

Frequently asked questions

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

Yes, NVIDIA H100 80GB can run Qwen 2.5 VL 72B with a S grade (Runs well). Expected decode speed: 69.7 tok/s.

How much VRAM does Qwen 2.5 VL 72B need?

Qwen 2.5 VL 72B (72B parameters) requires approximately 57.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 VL 72B?

The recommended quantization for Qwen 2.5 VL 72B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 VL 72B run at on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Qwen 2.5 VL 72B achieves approximately 69.7 tokens per second decode speed with a time-to-first-token of 2779ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run Qwen 2.5 VL 72B for coding?

For coding workloads, Qwen 2.5 VL 72B on NVIDIA H100 80GB receives a S grade with 69.7 tok/s and 33K context.

What context window can Qwen 2.5 VL 72B use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Qwen 2.5 VL 72B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.