Can Qwen 2.5 VL 7B run on RTX 3080 Ti 12GB?

YES — Runs Great

A85Great

Estimated from fit model

Qwen 2.5 VL 7B needs ~7.5 GB VRAM. RTX 3080 Ti 12GB has 12.0 GB. With Q4_K_M quantization, expect ~98 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 7.5 GB, 98.0 tok/s, Runs well

7.5 GB required12.0 GB available

63% VRAM used

Fit status

Runs well

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

33K

Memory

7.5 GB / 12.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsQwen 2.5 VL 7B on RTX 3080 Ti 12GB
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.

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	A	Runs well	98.0 tok/s	1078 ms	33K
Coding	A	Runs well	98.0 tok/s	1976 ms	33K
Agentic Coding	S	Runs well	98.0 tok/s	2873 ms	33K
Reasoning	A	Runs well	98.0 tok/s	2335 ms	33K
RAG	S	Runs well	98.0 tok/s	3592 ms	33K

Inference speed

Qwen 2.5 VL 7B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 2.5 VL 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	98.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	98.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	98.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	96.1	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	95.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	95.3	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	61.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	60.4	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	55.9	Fits
RTX 4060 8GB	8 GB	Q4_K_M	50.5	Tight
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	49.2	Fits

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Qwen 2.5 VL 7B (7B params) fits at each quantization level on RTX 3080 Ti 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A79
Q3_K_S	3	3.4 GB	Low	A79
NVFP4	4	3.9 GB	Medium	A80
Q4_K_M	4	4.3 GB	Medium	A81
Q5_K_M	5	5.0 GB	High	A82
Q6_K	6	5.7 GB	High	A82
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 RTX 3080 Ti 12GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	126 tok/s
Qwen 3 14B	14B	A	50.9 tok/s
Qwen 3 8B	8B	S	112 tok/s
Nemotron Nano 8B	8B	S	112 tok/s
Ministral 3 14B	14B	A	50.7 tok/s

Frequently asked questions

Can RTX 3080 Ti 12GB run Qwen 2.5 VL 7B?

Yes, RTX 3080 Ti 12GB 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 7.5 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 RTX 3080 Ti 12GB?

On RTX 3080 Ti 12GB, 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 RTX 3080 Ti 12GB run Qwen 2.5 VL 7B for coding?

For coding workloads, Qwen 2.5 VL 7B on RTX 3080 Ti 12GB receives a A grade with 98.0 tok/s and 33K context.

What context window can Qwen 2.5 VL 7B use on RTX 3080 Ti 12GB?

On RTX 3080 Ti 12GB, 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 RTX 3080 Ti 12GB See all hardware for Qwen 2.5 VL 7B

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<iframe src="https://willitrunai.com/embed/qwen-2.5-vl-7b-on-rtx-3080-ti-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

98.0 tok/s

1078 ms

33K

Coding

Runs well

98.0 tok/s

1976 ms

33K

Agentic Coding

Runs well

98.0 tok/s

2873 ms

33K

Reasoning

Runs well

98.0 tok/s

2335 ms

33K

RAG

Runs well

98.0 tok/s

3592 ms

33K

Qwen 2.5 VL 7B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

98.0

Fits

RTX 4090 24GB

24 GB

Q4_K_M

98.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

98.0

Fits

RTX 3090 24GB

24 GB

Q4_K_M

98.0

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

98.0

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

98.0

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

98.0

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

98.0

Fits

RTX 4070 12GB

12 GB

Q4_K_M

96.1

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

95.3

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

95.3

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

61.0

Fits

RTX 3060 12GB

12 GB

Q4_K_M

60.4

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

55.9

Fits

RTX 4060 8GB

8 GB

Q4_K_M

50.5

Tight

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

49.2

Fits

Quantization options

How Qwen 2.5 VL 7B (7B params) fits at each quantization level on RTX 3080 Ti 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A79
Q3_K_S	3	3.4 GB	Low	A79
NVFP4	4	3.9 GB	Medium	A80
Q4_K_M	4	4.3 GB	Medium	A81
Q5_K_M	5	5.0 GB	High	A82
Q6_K	6	5.7 GB	High	A82
Q8_0Best for your GPU	8	7.5 GB	Very High	A81
F16	16	14.3 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen 3.5 9B

126 tok/s

Qwen 3 14B

14B

50.9 tok/s

Qwen 3 8B

112 tok/s

Nemotron Nano 8B

112 tok/s

Ministral 3 14B

14B

50.7 tok/s

Frequently asked questions

Can RTX 3080 Ti 12GB run Qwen 2.5 VL 7B?

Yes, RTX 3080 Ti 12GB 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 7.5 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 RTX 3080 Ti 12GB?

On RTX 3080 Ti 12GB, 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 RTX 3080 Ti 12GB run Qwen 2.5 VL 7B for coding?

For coding workloads, Qwen 2.5 VL 7B on RTX 3080 Ti 12GB receives a A grade with 98.0 tok/s and 33K context.

What context window can Qwen 2.5 VL 7B use on RTX 3080 Ti 12GB?

On RTX 3080 Ti 12GB, 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.