Can Qwen 2.5 VL 72B run on NVIDIA A16 64GB?

YES — Tight Fit

S87Excellent

○Estimated from fit model

Qwen 2.5 VL 72B needs ~56.1 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~11 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: 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) — 56.1 GB, 11.6 tok/s, Tight fit

56.1 GB required64.0 GB available

88% VRAM used

Fit status

Tight fit

Decode

11.6 tok/s

TTFT

16707 ms

Safe context

33K

Memory

56.1 GB / 64.0 GB

Memory breakdown

Weights43.9 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsQwen 2.5 VL 72B on NVIDIA A16 64GB
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: 11.6 tok/s decode · 16.7s TTFT (warm) · 29 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	Tight fit	10.7 tok/s	9910 ms	33K
Coding	S	Tight fit	10.7 tok/s	18169 ms	33K
Agentic Coding	S	Runs with offload	10.7 tok/s	26427 ms	33K
Reasoning	S	Tight fit	10.7 tok/s	21472 ms	33K
RAG	S	Runs with offload	10.7 tok/s	33034 ms	33K

Quantization options

How Qwen 2.5 VL 72B (72B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	28.1 GB	Low	S87
Q3_K_S	3	35.3 GB	Low	S88
NVFP4	4

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

Frequently asked questions

See all results for NVIDIA A16 64GB See all hardware for Qwen 2.5 VL 72B

URL: https://willitrunai.com/can-run/qwen-2.5-vl-72b-on-a16-64gb

⇱