Can Gemma 3 4B run on Intel Arc Pro A40 6GB?

YES — With Offload

A71Great

○Estimated from fit model

Gemma 3 4B needs ~6.0 GB VRAM. Intel Arc Pro A40 6GB has 6.0 GB. With Q4_K_M quantization, expect ~22 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 6.0 GB, 22.9 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

22.9 tok/s

TTFT

8458 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 Intel Arc Pro A40 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: 22.9 tok/s decode · 8.5s TTFT (warm) · 57 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

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.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade 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	29.2 tok/s	3614 ms	16K
Coding	A	Runs with offload	21.8 tok/s	8881 ms	16K
Agentic Coding	F	Too heavy	11.7 tok/s	24107 ms	16K
Reasoning	A	Runs with offload	21.8 tok/s	10496 ms	16K
RAG	F	Too heavy	11.7 tok/s	30134 ms	16K

Quantization options

How Gemma 3 4B (4B params) fits at each quantization level on Intel Arc Pro A40 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

Get started

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

Run

ollama run gemma3:4b

Your hardware

More models your Intel Arc Pro A40 6GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 2.5 VL 7B	7B	B	14.6 tok/s
👁 Alibaba Qwen 2.5 7B	7B	B	14.6 tok/s

Frequently asked questions

See all results for Intel Arc Pro A40 6GB See all hardware for Gemma 3 4B

URL: https://willitrunai.com/can-run/gemma-3-4b-on-arc-pro-a40-6gb

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