Can Qwen 2.5 VL 7B run on Tesla P100 16GB?

YES — Runs Great

A82Great

Estimated from fit model

Qwen 2.5 VL 7B needs ~7.9 GB VRAM. Tesla P100 16GB has 16.0 GB. With Q4_K_M quantization, expect ~98 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 7.9 GB, 98.0 tok/s, Runs well

7.9 GB required16.0 GB available

49% VRAM used

Fit status

Runs well

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

33K

Memory

7.9 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsQwen 2.5 VL 7B on Tesla P100 16GB
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: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 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	98.0 tok/s	1078 ms	33K
Coding	A	Runs well	98.0 tok/s	1976 ms	33K
Agentic Coding	A	Runs well	98.0 tok/s	2873 ms	33K
Reasoning	A	Runs well	98.0 tok/s	2335 ms	33K
RAG	A	Runs well	98.0 tok/s	3592 ms	33K

Quantization options

How Qwen 2.5 VL 7B (7B params) fits at each quantization level on Tesla P100 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A76
Q3_K_S	3	3.4 GB	Low	A77
NVFP4	4	3.9 GB	Medium	A77
Q4_K_M	4	4.3 GB	Medium	A78
Q5_K_M	5	5.0 GB	High	A78
Q6_K	6	5.7 GB	High	A79
Q8_0Best for your GPU	8	7.5 GB	Very High	A81
F16	16	14.3 GB	Maximum	F0

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 P100 16GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	84.6 tok/s
Qwen 3 14B	14B	S	54.6 tok/s
Qwen 3 8B	8B	S	95.1 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	51.8 tok/s
GPT-OSS 20B	21B	A	46.4 tok/s

Frequently asked questions

Can Tesla P100 16GB run Qwen 2.5 VL 7B?

Yes, Tesla P100 16GB can run Qwen 2.5 VL 7B with a A grade (Runs well). Expected decode speed: 98.0 tok/s.

How much VRAM does Qwen 2.5 VL 7B need?

Qwen 2.5 VL 7B (7B parameters) requires approximately 7.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 VL 7B?

The recommended quantization for Qwen 2.5 VL 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 VL 7B run at on Tesla P100 16GB?

On Tesla P100 16GB, Qwen 2.5 VL 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.

Can Tesla P100 16GB run Qwen 2.5 VL 7B for coding?

For coding workloads, Qwen 2.5 VL 7B on Tesla P100 16GB receives a A grade with 98.0 tok/s and 33K context.

What context window can Qwen 2.5 VL 7B use on Tesla P100 16GB?

On Tesla P100 16GB, Qwen 2.5 VL 7B 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 Tesla P100 16GB See all hardware for Qwen 2.5 VL 7B

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