Can Gemma 3 4B run on RX 5600 XT 6GB?

YES — With Offload

A72Great

Estimated from fit model

Gemma 3 4B needs ~6.0 GB VRAM. RX 5600 XT 6GB has 6.0 GB. With Q4_K_M quantization, expect ~36 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 6.0 GB, 36.4 tok/s, Runs with offload (needs ~0 GB host RAM)

6.0 GB required6.0 GB available

100% VRAM used

Fit status

Runs with offload (needs ~0 GB host RAM)

Decode

36.4 tok/s

TTFT

5313 ms

Safe context

16K

Memory

6.0 GB / 6.0 GB

Memory breakdown

Weights2.4 GB

KV Cache2.1 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsGemma 3 4B on RX 5600 XT 6GB
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: 36.4 tok/s decode · 5.3s TTFT (warm) · 91 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	A	Tight fit	48.8 tok/s	2162 ms	16K
Coding	A	Runs with offload (needs ~0 GB host RAM)	36.4 tok/s	5313 ms	16K
Agentic Coding	F	Too heavy	19.5 tok/s	14423 ms	16K
Reasoning	A	Runs with offload (needs ~0 GB host RAM)	36.4 tok/s	6279 ms	16K
RAG	F	Too heavy	19.5 tok/s	18029 ms	16K

Inference speed

Gemma 3 4B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 3 4B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~76 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	76.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	64.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	64.0	Fits
RTX 4060 8GB	8 GB	Q4_K_M	57.2	Fits
RTX 3090 24GB	24 GB	Q4_K_M	56.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	56.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	56.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	56.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	56.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	56.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	56.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	56.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	56.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	56.0	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	56.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	56.0	Fits

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Gemma 3 4B (4B params) fits at each quantization level on RX 5600 XT 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	A75
Q3_K_S	3	2.0 GB	Low	A76
NVFP4	4	2.2 GB	Medium	A76
Q4_K_M	4	2.4 GB	Medium	A76
Q5_K_M	5	2.9 GB	High	A75
Q6_KBest for your GPU	6	3.3 GB	High	A75
Q8_0	8	4.3 GB	Very High	F0
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 5600 XT 6GB can run

Model	Params	Grade	Decode
Qwen 2.5 VL 7B	7B	B	23.2 tok/s
Qwen 2.5 7B	7B	B	23.2 tok/s
Codestral Mamba 7B	7B	A	27.7 tok/s
LFM2.5 8B A1B	8.5B	B	39.7 tok/s
Gemma 4 E2B	5.1B	A	39.7 tok/s

Frequently asked questions

Can RX 5600 XT 6GB run Gemma 3 4B?

Yes, RX 5600 XT 6GB can run Gemma 3 4B with a A grade (Runs with offload (needs ~0 GB host RAM)). Expected decode speed: 36.4 tok/s.

How much VRAM does Gemma 3 4B need?

Gemma 3 4B (4B parameters) requires approximately 6.0 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 5600 XT 6GB?

On RX 5600 XT 6GB, Gemma 3 4B achieves approximately 36.4 tokens per second decode speed with a time-to-first-token of 5313ms using Q4_K_M quantization.

Can RX 5600 XT 6GB run Gemma 3 4B for coding?

For coding workloads, Gemma 3 4B on RX 5600 XT 6GB receives a A grade with 36.4 tok/s and 16K context.

What context window can Gemma 3 4B use on RX 5600 XT 6GB?

On RX 5600 XT 6GB, Gemma 3 4B can safely use up to 16K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 3 4B feels slow on RX 5600 XT 6GB?

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 RX 5600 XT 6GB See all hardware for Gemma 3 4B

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