Can Qwen 2.5 VL 7B run on RX 590 8GB?

YES — Tight Fit

A80Great

○Estimated from fit model

Qwen 2.5 VL 7B needs ~6.8 GB VRAM. RX 590 8GB has 8.0 GB. With Q4_K_M quantization, expect ~28 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: 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) — 6.8 GB, 28.0 tok/s, Tight fit

6.8 GB required8.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

28.0 tok/s

TTFT

6917 ms

Safe context

33K

Memory

6.8 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsQwen 2.5 VL 7B on RX 590 8GB
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: 28.0 tok/s decode · 6.9s TTFT (warm) · 70 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	28.0 tok/s	3773 ms	33K
Coding	A	Tight fit	28.0 tok/s	6917 ms	33K
Agentic Coding	A	Runs with offload	28.0 tok/s	10061 ms	33K
Reasoning	A	Tight fit	28.0 tok/s	8174 ms	33K
RAG	A	Runs with offload	28.0 tok/s	12576 ms	33K

Quantization options

How Qwen 2.5 VL 7B (7B params) fits at each quantization level on RX 590 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A83
Q3_K_S	3	3.4 GB	Low	A83
NVFP4	4	3.9 GB	Medium	A83
Q4_K_M	4	4.3 GB	Medium	A83
Q5_K_MBest for your GPU	5	5.0 GB	High	A82
Q6_K	6	5.7 GB	High	F0
Q8_0	8	7.5 GB	Very High	F0
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 RX 590 8GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	A	11.1 tok/s
👁 Alibaba Qwen 3 8B	8B	A	14.4 tok/s
👁 NVIDIA Nemotron Nano 8B	8B	A	15.4 tok/s
👁 InternLM InternVL2 8B	8B	A	15.4 tok/s
👁 Mistral Ministral 3 8B	8B	B	14.4 tok/s

Frequently asked questions

See all results for RX 590 8GB See all hardware for Qwen 2.5 VL 7B

URL: https://willitrunai.com/can-run/qwen-2.5-vl-7b-on-rx-590-8gb