Can Qwen 2.5 VL 72B run on Gaudi 3 128GB?

YES — Runs Great

S92Excellent

Estimated from fit model

Qwen 2.5 VL 72B needs ~62.5 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~64 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) — 62.5 GB, 64.1 tok/s, Runs well

62.5 GB required128.0 GB available

49% VRAM used

Fit status

Runs well

Decode

64.1 tok/s

TTFT

3019 ms

Safe context

33K

Memory

62.5 GB / 128.0 GB

Memory breakdown

Weights43.9 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsQwen 2.5 VL 72B on Gaudi 3 128GB
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: 64.1 tok/s decode · 3.0s TTFT (warm) · 160 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	64.1 tok/s	1647 ms	33K
Coding	S	Runs well	64.1 tok/s	3019 ms	33K
Agentic Coding	S	Runs well	64.1 tok/s	4391 ms	33K
Reasoning	S	Runs well	64.1 tok/s	3568 ms	33K
RAG	S	Runs well	64.1 tok/s	5489 ms	33K

Quantization options

How Qwen 2.5 VL 72B (72B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	28.1 GB	Low	A81
Q3_K_S	3	35.3 GB	Low	A82
NVFP4	4	40.3 GB	Medium	A83
Q4_K_M	4	43.9 GB	Medium	A84
Q5_K_M	5	51.8 GB	High	A85
Q6_K	6	59.0 GB	High	S86
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 Gaudi 3 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	37.5 tok/s
Qwen 3.5 122B A10B	122B	S	104.1 tok/s
Mistral Small 4 119B	119B	S	112.9 tok/s
GPT-OSS 120B	117B	S	39.5 tok/s
Command A 111B	111B	S	41.8 tok/s

Frequently asked questions

Can Gaudi 3 128GB run Qwen 2.5 VL 72B?

Yes, Gaudi 3 128GB can run Qwen 2.5 VL 72B with a S grade (Runs well). Expected decode speed: 64.1 tok/s.

How much VRAM does Qwen 2.5 VL 72B need?

Qwen 2.5 VL 72B (72B parameters) requires approximately 62.5 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 Gaudi 3 128GB?

On Gaudi 3 128GB, Qwen 2.5 VL 72B achieves approximately 64.1 tokens per second decode speed with a time-to-first-token of 3019ms using Q4_K_M quantization.

Can Gaudi 3 128GB run Qwen 2.5 VL 72B for coding?

For coding workloads, Qwen 2.5 VL 72B on Gaudi 3 128GB receives a S grade with 64.1 tok/s and 33K context.

What context window can Qwen 2.5 VL 72B use on Gaudi 3 128GB?

On Gaudi 3 128GB, 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.

What should I upgrade first if Qwen 2.5 VL 72B feels slow on Gaudi 3 128GB?

Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Would CUDA be a better path than Gaudi 3 128GB for Qwen 2.5 VL 72B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Gaudi 3 128GB See all hardware for Qwen 2.5 VL 72B

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