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URL: https://willitrunai.com/can-run/gemma-4-e2b-on-arc-a370m-4gb

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Can Gemma 4 E2B run on Intel Arc A370M 4GB?

YES — With NVFP4

B59Good
○Estimated from fit model

Gemma 4 E2B needs ~4.7 GB VRAM. Intel Arc A370M 4GB has 4.0 GB. With NVFP4 quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload
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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.

Gemma 4 E2B at Q4_K_M needs 4.9 GB — too much for Intel Arc A370M 4GB (4.0 GB). Runs at NVFP4 (4.7 GB) with medium quality. 3 quantization levels fit.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 4.9 GB, exceeds 4.0 GB available
4.9 GB required4.0 GB available
123% VRAM needed

0.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

7.0 tok/s

TTFT

27746 ms

Safe context

4K

Memory

4.9 GB / 4.0 GB

Offload

20%

Memory breakdown

Weights3.1 GB
KV Cache0.5 GB
Runtime0.9 GB
Headroom0.4 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsGemma 4 E2B on Intel Arc A370M 4GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort: def quicksort(arr): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) # Example usage numbers = [3, 6, 8, 10, 1, 2, 1] print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10] The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]: """Sort a list using the quicksort algorithm. Args: arr: List of integers to sort. Returns: A new sorted list. """ if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 7.0 tok/s decode · 27.7s TTFT (warm) · 17 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 10% 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.

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

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.

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

WorkloadGradeFitDecodeTTFTContext
ChatBVery compromised (needs ~0.5 GB host RAM)7.8 tok/s13465 ms4K
CodingFToo heavy6.4 tok/s30174 ms4K
Agentic CodingFToo heavy5.6 tok/s50083 ms4K
ReasoningFToo heavy7.0 tok/s32791 ms4K
RAGFToo heavy5.6 tok/s62604 ms4K

Quantization options

How Gemma 4 E2B (5.099999904632568B params) fits at each quantization level on Intel Arc A370M 4GB (4.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.0 GB
LowF0
Q3_K_S
3
2.5 GB
LowF0
NVFP4
4

Get started

Copy-paste commands to run Gemma 4 E2B on your machine.

Run

ollama run gemma4:e2b

Upgrade options

Hardware that runs Gemma 4 E2B well

👁 Intel
Intel Arc A380 6GBBudget pick
6 GB VRAM (+2)186 GB/s (+74)
A
Makes the model fit on the accelerator instead of staying completely out of reach.24.1 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.

~$139 MSRP

👁 Intel
Intel Arc A580 8GBBest value
8 GB VRAM (+4)512 GB/s (+400)
A
Makes the model fit on the accelerator instead of staying completely out of reach.66.5 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.

~$179 MSRP

👁 Intel
Intel Arc B570 10GBIntel upgrade
10 GB VRAM (+6)380 GB/s (+268)
A
Makes the model fit on the accelerator instead of staying completely out of reach.54.4 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.

~$219 MSRP

Frequently asked questions

See all results for Intel Arc A370M 4GBSee all hardware for Gemma 4 E2B
2.9 GB
Medium
F0
Q4_K_M
4
3.1 GB
MediumF0
Q5_K_M
5
3.7 GB
HighF0
Q6_K
6
4.2 GB
HighF0
Q8_0
8
5.5 GB
Very HighF0
F16
16
10.5 GB
MaximumF0

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.