Can Qwen 2.5 VL 7B run on Tesla P40 24GB?

YES — Runs Great

A77Great

○Estimated from fit model

Qwen 2.5 VL 7B needs ~8.7 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~52 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 8.7 GB, 51.9 tok/s, Runs well

8.7 GB required24.0 GB available

36% VRAM used

Fit status

Runs well

Decode

51.9 tok/s

TTFT

3730 ms

Safe context

33K

Memory

8.7 GB / 24.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsQwen 2.5 VL 7B on Tesla P40 24GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
 - Full privacy: your prompts never leave your machine
 - No per-token cost or rate limits
 - Works offline once the model is downloaded
 - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

 Model size Q4 (4-bit) Q8 (8-bit) FP16
 7B params ~4.3 GB ~7.5 GB ~14 GB
 13B params ~7.9 GB ~13.9 GB ~26 GB
 70B params ~42.7 GB ~74.9 GB ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 51.9 tok/s decode · 3.7s TTFT (warm) · 130 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	51.9 tok/s	2034 ms	33K
Coding	A	Runs well	51.9 tok/s	3730 ms	33K
Agentic Coding	A	Runs well	51.9 tok/s	5425 ms	33K
Reasoning	A	Runs well	51.9 tok/s	4408 ms	33K
RAG	A	Runs well	51.9 tok/s	6782 ms	33K

Quantization options

How Qwen 2.5 VL 7B (7B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A74
Q3_K_S	3	3.4 GB	Low	A74
NVFP4	4	3.9 GB	Medium	A75
Q4_K_M	4	4.3 GB	Medium	A75
Q5_K_M	5	5.0 GB	High	A75
Q6_K	6	5.7 GB	High	A75
Q8_0	8	7.5 GB	Very High	A77
F16Best for your GPU	16	14.3 GB	Maximum	A80

Get started

Copy-paste commands to run Qwen 2.5 VL 7B on your machine.

Run

lms load Qwen2.5-VL-7B-Instruct && lms server start

Your hardware

More models your Tesla P40 24GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	30.9 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	13.4 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	13.4 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	31.9 tok/s
👁 Alibaba Qwen 3.5 9B	9B	S	40 tok/s

Frequently asked questions

See all results for Tesla P40 24GB See all hardware for Qwen 2.5 VL 7B

URL: https://willitrunai.com/can-run/qwen-2.5-vl-7b-on-tesla-p40-24gb