Can Qwen3-VL 30B A3B Instruct run on Radeon AI PRO R9700 32GB?

YES — Runs Great

S98Excellent

Estimated from fit model

Qwen3-VL 30B A3B Instruct needs ~24.4 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~59 tok/s.

Runtime: LM StudioCapacity: 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) — 24.4 GB, 59.1 tok/s, Runs well

24.4 GB required32.0 GB available

76% VRAM used

Fit status

Runs well

Decode

59.1 tok/s

TTFT

3279 ms

Safe context

99K

Memory

24.4 GB / 32.0 GB

Memory breakdown

Weights18.3 GB

KV Cache1.5 GB

Runtime1.4 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsQwen3-VL 30B A3B Instruct on Radeon AI PRO R9700 32GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 59.1 tok/s decode · 3.3s TTFT (warm) · 148 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	S	Runs well	59.1 tok/s	1788 ms	99K
Coding	S	Runs well	59.1 tok/s	3279 ms	99K
Agentic Coding	S	Runs well	59.1 tok/s	4769 ms	99K
Reasoning	S	Runs well	59.1 tok/s	3875 ms	99K
RAG	S	Runs well	59.1 tok/s	5961 ms	99K

Quantization options

How Qwen3-VL 30B A3B Instruct (30B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	S89
Q3_K_S	3	14.7 GB	Low	S91
NVFP4	4	16.8 GB	Medium	S92
Q4_K_M	4	18.3 GB	Medium	S91
Q5_K_M	5	21.6 GB	High	S91
Q6_KBest for your GPU	6	24.6 GB	High	S91
Q8_0	8	32.1 GB	Very High	F0
F16	16	61.5 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen3-VL 30B A3B Instruct on your machine.

Run

lms load Qwen3-VL-30B-A3B-Instruct && lms server start

Your hardware

More models your Radeon AI PRO R9700 32GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	57.1 tok/s
Qwen 3.6 35B A3B	35B	S	48 tok/s

Frequently asked questions

Can Radeon AI PRO R9700 32GB run Qwen3-VL 30B A3B Instruct?

Yes, Radeon AI PRO R9700 32GB can run Qwen3-VL 30B A3B Instruct with a S grade (Runs well). Expected decode speed: 59.1 tok/s.

How much VRAM does Qwen3-VL 30B A3B Instruct need?

Qwen3-VL 30B A3B Instruct (30B parameters) requires approximately 24.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3-VL 30B A3B Instruct?

The recommended quantization for Qwen3-VL 30B A3B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3-VL 30B A3B Instruct run at on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Qwen3-VL 30B A3B Instruct achieves approximately 59.1 tokens per second decode speed with a time-to-first-token of 3279ms using Q4_K_M quantization.

Can Radeon AI PRO R9700 32GB run Qwen3-VL 30B A3B Instruct for coding?

For coding workloads, Qwen3-VL 30B A3B Instruct on Radeon AI PRO R9700 32GB receives a S grade with 59.1 tok/s and 99K context.

What context window can Qwen3-VL 30B A3B Instruct use on Radeon AI PRO R9700 32GB?

On Radeon AI PRO R9700 32GB, Qwen3-VL 30B A3B Instruct can safely use up to 99K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for Radeon AI PRO R9700 32GB See all hardware for Qwen3-VL 30B A3B Instruct

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<iframe src="https://willitrunai.com/embed/qwen-3-vl-30b-a3b-on-radeon-ai-pro-r9700-32gb" 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

59.1 tok/s

1788 ms

99K

Coding

Runs well

59.1 tok/s

3279 ms

99K

Agentic Coding

Runs well

59.1 tok/s

4769 ms

99K

Reasoning

Runs well

59.1 tok/s

3875 ms

99K

RAG

Runs well

59.1 tok/s

5961 ms

99K

Quantization options

How Qwen3-VL 30B A3B Instruct (30B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	S89
Q3_K_S	3	14.7 GB	Low	S91
NVFP4	4	16.8 GB	Medium	S92
Q4_K_M	4	18.3 GB	Medium	S91
Q5_K_M	5	21.6 GB	High	S91
Q6_KBest for your GPU	6	24.6 GB	High	S91
Q8_0	8	32.1 GB	Very High	F0
F16	16	61.5 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

57.1 tok/s

Qwen 3.6 35B A3B

35B

48 tok/s