Will It Run AI

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 heavy7.0 tok/s27746 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
2.9 GB
MediumF0
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

Get started

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

Run

ollama run gemma4:e2b

アップグレードオプション

Gemma 4 E2Bを快適に動かすハードウェア

IntelIntel Arc A380 6GBコスパ重視
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デコード

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

IntelIntel Arc A580 8GBコスパ最良
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デコード

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

IntelIntel Arc B570 10GBIntelアップグレード
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デコード

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

Can Intel Arc A370M 4GB run Gemma 4 E2B?

Yes, Intel Arc A370M 4GB can run Gemma 4 E2B at NVFP4 quantization (Very compromised (needs ~0.4 GB host RAM)). The recommended Q4_K_M requires 4.9 GB which exceeds available memory, but at NVFP4 it needs only 4.7 GB. Expected decode speed: 8.9 tok/s.

How much VRAM does Gemma 4 E2B need?

Gemma 4 E2B (5.099999904632568B parameters) requires approximately 4.9 GB at Q4_K_M quantization. On Intel Arc A370M 4GB, it fits at NVFP4 using 4.7 GB.

What is the best quantization for Gemma 4 E2B?

The recommended quantization is Q4_K_M, but on Intel Arc A370M 4GB the best fitting quantization is NVFP4, which uses 4.7 GB.

What speed will Gemma 4 E2B run at on Intel Arc A370M 4GB?

On Intel Arc A370M 4GB, Gemma 4 E2B achieves approximately 8.9 tokens per second decode speed with a time-to-first-token of 21701ms using NVFP4 quantization.

Can Intel Arc A370M 4GB run Gemma 4 E2B for coding?

For coding workloads, Gemma 4 E2B on Intel Arc A370M 4GB receives a F grade with 7.0 tok/s and 4K context.

What context window can Gemma 4 E2B use on Intel Arc A370M 4GB?

On Intel Arc A370M 4GB, Gemma 4 E2B can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 4 E2B feels slow on Intel Arc A370M 4GB?

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.

Would CUDA be a better path than Intel Arc A370M 4GB for Gemma 4 E2B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Intel Arc A370M 4GBSee all hardware for Gemma 4 E2B
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