Can Qwen 3 30B A3B run on RX 6900 XT 16GB?

YES — With Q3_K_S

A74Great

Estimated from fit model

Qwen 3 30B A3B needs ~18.9 GB VRAM. RX 6900 XT 16GB has 16.0 GB. With Q3_K_S quantization, expect ~27 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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.

Qwen 3 30B A3B at Q4_K_M needs 22.6 GB — too much for RX 6900 XT 16GB (16.0 GB). Runs at Q3_K_S (18.9 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 22.6 GB, exceeds 16.0 GB available

22.6 GB required16.0 GB available

141% VRAM needed

6.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

16.0 tok/s

TTFT

12068 ms

Safe context

Memory

22.6 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights18.6 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 3 30B A3B on RX 6900 XT 16GB
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: 16.0 tok/s decode · 12.1s TTFT (warm) · 40 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 2.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	17.2 tok/s	6141 ms	4K
Coding	F	Too heavy	16.0 tok/s	12068 ms	4K
Agentic Coding	F	Too heavy	14.1 tok/s	20037 ms	4K
Reasoning	F	Too heavy	16.0 tok/s	14262 ms	4K
RAG	F	Too heavy	14.1 tok/s	25046 ms	4K

Quantization options

How Qwen 3 30B A3B (30.5B params) fits at each quantization level on RX 6900 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.9 GB	Low	F0
Q3_K_S	3	14.9 GB	Low	F0
NVFP4	4	17.1 GB	Medium	F0
Q4_K_M	4	18.6 GB	Medium	F0
Q5_K_M	5	22.0 GB	High	F0
Q6_K	6	25.0 GB	High	F0
Q8_0	8	32.6 GB	Very High	F0
F16	16	62.5 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 3 30B A3B on your machine.

Run

ollama run qwen3:30b-a3b

Upgrade options

Hardware that runs Qwen 3 30B A3B well

RX 7900 XT 20GBBest value

20 GB VRAM (+4)800 GB/s (+288)

Makes the model fit on the accelerator instead of staying completely out of reach.40.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 154%.

~$899 MSRP

RX 7900 XTX 24GBBudget pick

24 GB VRAM (+8)960 GB/s (+448)

Makes the model fit on the accelerator instead of staying completely out of reach.104.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$999 MSRP

Radeon AI PRO R9700 32GBAMD upgrade

32 GB VRAM (+16)640 GB/s (+128)

Makes the model fit on the accelerator instead of staying completely out of reach.57.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,899 MSRP

Frequently asked questions

Can RX 6900 XT 16GB run Qwen 3 30B A3B?

Yes, RX 6900 XT 16GB can run Qwen 3 30B A3B at Q3_K_S quantization (Very compromised (needs ~2.3 GB host RAM)). The recommended Q4_K_M requires 22.6 GB which exceeds available memory, but at Q3_K_S it needs only 18.9 GB. Expected decode speed: 27.0 tok/s.

How much VRAM does Qwen 3 30B A3B need?

Qwen 3 30B A3B (30.5B parameters) requires approximately 22.6 GB at Q4_K_M quantization. On RX 6900 XT 16GB, it fits at Q3_K_S using 18.9 GB.

What is the best quantization for Qwen 3 30B A3B?

The recommended quantization is Q4_K_M, but on RX 6900 XT 16GB the best fitting quantization is Q3_K_S, which uses 18.9 GB.

What speed will Qwen 3 30B A3B run at on RX 6900 XT 16GB?

On RX 6900 XT 16GB, Qwen 3 30B A3B achieves approximately 27.0 tokens per second decode speed with a time-to-first-token of 7182ms using Q3_K_S quantization.

Can RX 6900 XT 16GB run Qwen 3 30B A3B for coding?

For coding workloads, Qwen 3 30B A3B on RX 6900 XT 16GB receives a F grade with 16.0 tok/s and 4K context.

What context window can Qwen 3 30B A3B use on RX 6900 XT 16GB?

On RX 6900 XT 16GB, Qwen 3 30B A3B can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 3 30B A3B feels slow on RX 6900 XT 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RX 6900 XT 16GB See all hardware for Qwen 3 30B A3B

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Preview:

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 2.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

17.2 tok/s

6141 ms

Coding

Too heavy

16.0 tok/s

12068 ms

Agentic Coding

Too heavy

14.1 tok/s

20037 ms

Reasoning

Too heavy

16.0 tok/s

14262 ms

RAG

Too heavy

14.1 tok/s

25046 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

11.9 GB

Low

Q3_K_S

14.9 GB

Low

NVFP4

17.1 GB

Medium

Q4_K_M

18.6 GB

Medium

Q5_K_M

22.0 GB

High

Q6_K

25.0 GB

High

Q8_0

32.6 GB

Very High

F16

62.5 GB

Maximum

Frequently asked questions

Can RX 6900 XT 16GB run Qwen 3 30B A3B?

How much VRAM does Qwen 3 30B A3B need?

Qwen 3 30B A3B (30.5B parameters) requires approximately 22.6 GB at Q4_K_M quantization. On RX 6900 XT 16GB, it fits at Q3_K_S using 18.9 GB.

What is the best quantization for Qwen 3 30B A3B?

The recommended quantization is Q4_K_M, but on RX 6900 XT 16GB the best fitting quantization is Q3_K_S, which uses 18.9 GB.

What speed will Qwen 3 30B A3B run at on RX 6900 XT 16GB?

On RX 6900 XT 16GB, Qwen 3 30B A3B achieves approximately 27.0 tokens per second decode speed with a time-to-first-token of 7182ms using Q3_K_S quantization.

Can RX 6900 XT 16GB run Qwen 3 30B A3B for coding?

For coding workloads, Qwen 3 30B A3B on RX 6900 XT 16GB receives a F grade with 16.0 tok/s and 4K context.

What context window can Qwen 3 30B A3B use on RX 6900 XT 16GB?

On RX 6900 XT 16GB, Qwen 3 30B A3B can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 3 30B A3B feels slow on RX 6900 XT 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.