Can Gemma 3 4B run on RX 6650 XT 8GB?

YES — Runs Great

A76Great

Estimated from fit model

Gemma 3 4B needs ~6.2 GB VRAM. RX 6650 XT 8GB has 8.0 GB. With Q4_K_M quantization, expect ~47 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) — 6.2 GB, 46.6 tok/s, Runs well

6.2 GB required8.0 GB available

78% VRAM used

Fit status

Runs well

Decode

46.6 tok/s

TTFT

4157 ms

Safe context

30K

Memory

6.2 GB / 8.0 GB

Memory breakdown

Weights2.4 GB

KV Cache2.1 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsGemma 3 4B on RX 6650 XT 8GB
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: 46.6 tok/s decode · 4.2s TTFT (warm) · 116 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	A	Runs well	46.6 tok/s	2268 ms	30K
Coding	A	Runs well	46.6 tok/s	4157 ms	30K
Agentic Coding	A	Runs with offload (needs ~0.1 GB host RAM)	32.4 tok/s	8691 ms	30K
Reasoning	A	Runs well	46.6 tok/s	4913 ms	30K
RAG	A	Runs with offload (needs ~0.1 GB host RAM)	32.4 tok/s	10863 ms	30K

Quantization options

How Gemma 3 4B (4B params) fits at each quantization level on RX 6650 XT 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	A72
Q3_K_S	3	2.0 GB	Low	A73
NVFP4	4	2.2 GB	Medium	A73
Q4_K_M	4	2.4 GB	Medium	A74
Q5_K_M	5	2.9 GB	High	A75
Q6_K	6	3.3 GB	High	A75
Q8_0Best for your GPU	8	4.3 GB	Very High	A74
F16	16	8.2 GB	Maximum	F0

Get started

Copy-paste commands to run Gemma 3 4B on your machine.

Run

ollama run gemma3:4b

Your hardware

More models your RX 6650 XT 8GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	A	15 tok/s
Qwen 3 8B	8B	A	19.4 tok/s
Nemotron Nano 8B	8B	A	20.6 tok/s
InternVL2 8B	8B	A	20.6 tok/s
Ministral 3 8B	8B	B	19.4 tok/s

Frequently asked questions

Can RX 6650 XT 8GB run Gemma 3 4B?

Yes, RX 6650 XT 8GB can run Gemma 3 4B with a A grade (Runs well). Expected decode speed: 46.6 tok/s.

How much VRAM does Gemma 3 4B need?

Gemma 3 4B (4B parameters) requires approximately 6.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 3 4B?

The recommended quantization for Gemma 3 4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 3 4B run at on RX 6650 XT 8GB?

On RX 6650 XT 8GB, Gemma 3 4B achieves approximately 46.6 tokens per second decode speed with a time-to-first-token of 4157ms using Q4_K_M quantization.

Can RX 6650 XT 8GB run Gemma 3 4B for coding?

For coding workloads, Gemma 3 4B on RX 6650 XT 8GB receives a A grade with 46.6 tok/s and 30K context.

What context window can Gemma 3 4B use on RX 6650 XT 8GB?

On RX 6650 XT 8GB, Gemma 3 4B can safely use up to 30K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RX 6650 XT 8GB See all hardware for Gemma 3 4B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

46.6 tok/s

2268 ms

30K

Coding

Runs well

46.6 tok/s

4157 ms

30K

Agentic Coding

Runs with offload (needs ~0.1 GB host RAM)

32.4 tok/s

8691 ms

30K

Reasoning

Runs well

46.6 tok/s

4913 ms

30K

RAG

Runs with offload (needs ~0.1 GB host RAM)

32.4 tok/s

10863 ms

30K

Quant

Bits

VRAM

Quality

Fit

Q2_K

1.6 GB

Low

A72

Q3_K_S

2.0 GB

Low

A73

NVFP4

2.2 GB

Medium

A73

Q4_K_M

2.4 GB

Medium

A74

Q5_K_M

2.9 GB

High

A75

Q6_K

3.3 GB

High

A75

Q8_0Best for your GPU

4.3 GB

Very High

A74

F16

8.2 GB

Maximum

Model

Params

Grade

Decode

Capabilities

15 tok/s

19.4 tok/s

20.6 tok/s

20.6 tok/s

19.4 tok/s

Frequently asked questions

Can RX 6650 XT 8GB run Gemma 3 4B?

Yes, RX 6650 XT 8GB can run Gemma 3 4B with a A grade (Runs well). Expected decode speed: 46.6 tok/s.

How much VRAM does Gemma 3 4B need?

Gemma 3 4B (4B parameters) requires approximately 6.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 3 4B?

The recommended quantization for Gemma 3 4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 3 4B run at on RX 6650 XT 8GB?

On RX 6650 XT 8GB, Gemma 3 4B achieves approximately 46.6 tokens per second decode speed with a time-to-first-token of 4157ms using Q4_K_M quantization.

Can RX 6650 XT 8GB run Gemma 3 4B for coding?

For coding workloads, Gemma 3 4B on RX 6650 XT 8GB receives a A grade with 46.6 tok/s and 30K context.

What context window can Gemma 3 4B use on RX 6650 XT 8GB?

On RX 6650 XT 8GB, Gemma 3 4B can safely use up to 30K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.