Can Qwen3-VL 30B A3B Instruct run on RTX A5500 24GB?

YES — With Offload

S96Excellent

Estimated from fit model

Qwen3-VL 30B A3B Instruct needs ~23.6 GB VRAM. RTX A5500 24GB has 24.0 GB. With Q4_K_M quantization, expect ~94 tok/s.

Runtime: LM StudioCapacity: OffloadBandwidth: 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) — 23.6 GB, 93.7 tok/s, Runs with offload

23.6 GB required24.0 GB available

98% VRAM used

Fit status

Runs with offload

Decode

93.7 tok/s

TTFT

2067 ms

Safe context

21K

Memory

23.6 GB / 24.0 GB

Memory breakdown

Weights18.3 GB

KV Cache1.5 GB

Runtime1.4 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsQwen3-VL 30B A3B Instruct on RTX A5500 24GB
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: 93.7 tok/s decode · 2.1s TTFT (warm) · 234 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs with offload	93.7 tok/s	1127 ms	21K
Coding	S	Runs with offload	93.7 tok/s	2067 ms	21K
Agentic Coding	S	Runs with offload (needs ~0.8 GB host RAM)	64.3 tok/s	4378 ms	21K
Reasoning	S	Runs with offload	93.7 tok/s	2442 ms	21K
RAG	S	Runs with offload (needs ~0.8 GB host RAM)	64.3 tok/s	5473 ms	21K

Quantization options

How Qwen3-VL 30B A3B Instruct (30B params) fits at each quantization level on RTX A5500 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	S93
Q3_K_S	3	14.7 GB	Low	S92
NVFP4	4	16.8 GB	Medium	S92
Q4_K_MBest for your GPU	4	18.3 GB	Medium	S92
Q5_K_M	5	21.6 GB	High	F0
Q6_K	6	24.6 GB	High	F0
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 RTX A5500 24GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	90.6 tok/s

Frequently asked questions

Can RTX A5500 24GB run Qwen3-VL 30B A3B Instruct?

Yes, RTX A5500 24GB can run Qwen3-VL 30B A3B Instruct with a S grade (Runs with offload). Expected decode speed: 93.7 tok/s.

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

Qwen3-VL 30B A3B Instruct (30B parameters) requires approximately 23.6 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 RTX A5500 24GB?

On RTX A5500 24GB, Qwen3-VL 30B A3B Instruct achieves approximately 93.7 tokens per second decode speed with a time-to-first-token of 2067ms using Q4_K_M quantization.

Can RTX A5500 24GB run Qwen3-VL 30B A3B Instruct for coding?

For coding workloads, Qwen3-VL 30B A3B Instruct on RTX A5500 24GB receives a S grade with 93.7 tok/s and 21K context.

What context window can Qwen3-VL 30B A3B Instruct use on RTX A5500 24GB?

On RTX A5500 24GB, Qwen3-VL 30B A3B Instruct can safely use up to 21K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen3-VL 30B A3B Instruct feels slow on RTX A5500 24GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX A5500 24GB See all hardware for Qwen3-VL 30B A3B Instruct

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Preview:

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs with offload	93.7 tok/s	1127 ms	21K
Coding	S	Runs with offload	93.7 tok/s	2067 ms	21K
Agentic Coding	S	Runs with offload (needs ~0.8 GB host RAM)	64.3 tok/s	4378 ms	21K
Reasoning	S	Runs with offload	93.7 tok/s	2442 ms	21K
RAG	S	Runs with offload (needs ~0.8 GB host RAM)	64.3 tok/s	5473 ms	21K

Quantization options

How Qwen3-VL 30B A3B Instruct (30B params) fits at each quantization level on RTX A5500 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	S93
Q3_K_S	3	14.7 GB	Low	S92
NVFP4	4	16.8 GB	Medium	S92
Q4_K_MBest for your GPU	4	18.3 GB	Medium	S92
Q5_K_M	5	21.6 GB	High	F0
Q6_K	6	24.6 GB	High	F0
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

90.6 tok/s