Can Qwen 3 8B run on RX 7600 XT 16GB?

YES — Runs Great

S92Excellent

Estimated from fit model

Qwen 3 8B needs ~9.6 GB VRAM. RX 7600 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~37 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 9.6 GB, 36.8 tok/s, Runs well

9.6 GB required16.0 GB available

60% VRAM used

Fit status

Runs well

Decode

36.8 tok/s

TTFT

5261 ms

Safe context

63K

Memory

9.6 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsQwen 3 8B on RX 7600 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: 36.8 tok/s decode · 5.3s TTFT (warm) · 92 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	36.8 tok/s	2870 ms	63K
Coding	S	Runs well	36.8 tok/s	5261 ms	63K
Agentic Coding	S	Runs well	36.8 tok/s	7653 ms	63K
Reasoning	S	Runs well	36.8 tok/s	6218 ms	63K
RAG	S	Runs well	36.8 tok/s	9566 ms	63K

Quantization options

How Qwen 3 8B (8B params) fits at each quantization level on RX 7600 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	S87
Q3_K_S	3	3.9 GB	Low	S88
NVFP4	4	4.5 GB	Medium	S88
Q4_K_M	4	4.9 GB	Medium	S89
Q5_K_M	5	5.8 GB	High	S89
Q6_K	6	6.6 GB	High	S90
Q8_0Best for your GPU	8	8.6 GB	Very High	S91
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 3 8B on your machine.

Run

ollama run qwen3:8b

Your hardware

More models your RX 7600 XT 16GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	S	32.7 tok/s
Qwen 3 14B	14B	S	21.1 tok/s

Frequently asked questions

Can RX 7600 XT 16GB run Qwen 3 8B?

Yes, RX 7600 XT 16GB can run Qwen 3 8B with a S grade (Runs well). Expected decode speed: 36.8 tok/s.

How much VRAM does Qwen 3 8B need?

Qwen 3 8B (8B parameters) requires approximately 9.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3 8B?

The recommended quantization for Qwen 3 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3 8B run at on RX 7600 XT 16GB?

On RX 7600 XT 16GB, Qwen 3 8B achieves approximately 36.8 tokens per second decode speed with a time-to-first-token of 5261ms using Q4_K_M quantization.

Can RX 7600 XT 16GB run Qwen 3 8B for coding?

For coding workloads, Qwen 3 8B on RX 7600 XT 16GB receives a S grade with 36.8 tok/s and 63K context.

What context window can Qwen 3 8B use on RX 7600 XT 16GB?

On RX 7600 XT 16GB, Qwen 3 8B can safely use up to 63K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RX 7600 XT 16GB See all hardware for Qwen 3 8B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/qwen-3-8b-on-rx-7600-xt-16gb" 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

36.8 tok/s

2870 ms

63K

Coding

Runs well

36.8 tok/s

5261 ms

63K

Agentic Coding

Runs well

36.8 tok/s

7653 ms

63K

Reasoning

Runs well

36.8 tok/s

6218 ms

63K

RAG

Runs well

36.8 tok/s

9566 ms

63K

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.1 GB

Low

S87

Q3_K_S

3.9 GB

Low

S88

NVFP4

4.5 GB

Medium

S88

Q4_K_M

4.9 GB

Medium

S89

Q5_K_M

5.8 GB

High

S89

Q6_K

6.6 GB

High

S90

Q8_0Best for your GPU

8.6 GB

Very High

S91

F16

16.4 GB

Maximum

Model

Params

Grade

Decode

Capabilities

Qwen 3.5 9B

32.7 tok/s

Qwen 3 14B

14B

21.1 tok/s

Frequently asked questions

Can RX 7600 XT 16GB run Qwen 3 8B?

Yes, RX 7600 XT 16GB can run Qwen 3 8B with a S grade (Runs well). Expected decode speed: 36.8 tok/s.

How much VRAM does Qwen 3 8B need?

Qwen 3 8B (8B parameters) requires approximately 9.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3 8B?

The recommended quantization for Qwen 3 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3 8B run at on RX 7600 XT 16GB?

On RX 7600 XT 16GB, Qwen 3 8B achieves approximately 36.8 tokens per second decode speed with a time-to-first-token of 5261ms using Q4_K_M quantization.

Can RX 7600 XT 16GB run Qwen 3 8B for coding?

For coding workloads, Qwen 3 8B on RX 7600 XT 16GB receives a S grade with 36.8 tok/s and 63K context.

What context window can Qwen 3 8B use on RX 7600 XT 16GB?

On RX 7600 XT 16GB, Qwen 3 8B can safely use up to 63K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.