Can Qwen 2.5 VL 7B run on Radeon RX 7900M 16GB?

YES — Runs Great

A82Great

○Estimated from fit model

Qwen 2.5 VL 7B needs ~7.6 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q4_K_M quantization, expect ~86 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 7.6 GB, 86.4 tok/s, Runs well

7.6 GB required16.0 GB available

48% VRAM used

Fit status

Runs well

Decode

86.4 tok/s

TTFT

2240 ms

Safe context

33K

Memory

7.6 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsQwen 2.5 VL 7B on Radeon RX 7900M 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: 86.4 tok/s decode · 2.2s TTFT (warm) · 216 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	86.4 tok/s	1222 ms	33K
Coding	A	Runs well	86.4 tok/s	2240 ms	33K
Agentic Coding	A	Runs well	86.4 tok/s	3259 ms	33K
Reasoning	A	Runs well	86.4 tok/s	2648 ms	33K
RAG	A	Runs well	86.4 tok/s	4074 ms	33K

Quantization options

How Qwen 2.5 VL 7B (7B params) fits at each quantization level on Radeon RX 7900M 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 Radeon RX 7900M 16GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	66.5 tok/s
👁 Alibaba Qwen 3 14B	14B	S	43 tok/s
👁 Alibaba Qwen 3 8B	8B	S	74.9 tok/s
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	S	40.7 tok/s
👁 OpenAI GPT-OSS 20B	21B	A	39.3 tok/s

Frequently asked questions

See all results for Radeon RX 7900M 16GB See all hardware for Qwen 2.5 VL 7B

URL: https://willitrunai.com/can-run/qwen-2.5-vl-7b-on-rx-7900m-16gb

⇱ Qwen 2.5 VL 7B on Radeon RX 7900M 16GB? YES