Can Gemma 4 E4B run on Intel Arc A750 8GB?

YES — With Offload

A78Great

Estimated from fit model

Gemma 4 E4B needs ~7.9 GB VRAM. Intel Arc A750 8GB has 8.0 GB. With Q4_K_M quantization, expect ~37 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: 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) — 7.9 GB, 36.8 tok/s, Runs with offload

7.9 GB required8.0 GB available

99% VRAM used

Fit status

Runs with offload

Decode

36.8 tok/s

TTFT

5267 ms

Safe context

18K

Memory

7.9 GB / 8.0 GB

Memory breakdown

Weights4.9 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsGemma 4 E4B on Intel Arc A750 8GB
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: 36.8 tok/s decode · 5.3s TTFT (warm) · 92 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	36.8 tok/s	2873 ms	18K
Coding	A	Runs with offload	36.8 tok/s	5267 ms	18K
Agentic Coding	B	Very compromised (needs ~0.6 GB host RAM)	20.8 tok/s	13533 ms	18K
Reasoning	A	Runs with offload	36.8 tok/s	6225 ms	18K
RAG	B	Very compromised (needs ~0.6 GB host RAM)	20.8 tok/s	16916 ms	18K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on Intel Arc A750 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A81
Q3_K_S	3	3.9 GB	Low	A81
NVFP4	4	4.5 GB	Medium	A80
Q4_K_MBest for your GPU	4	4.9 GB	Medium	A80
Q5_K_M	5	5.8 GB	High	F0
Q6_K	6	6.6 GB	High	F0
Q8_0	8	8.6 GB	Very High	F0
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run Gemma 4 E4B on your machine.

Run

ollama run gemma4:e4b

Your hardware

More models your Intel Arc A750 8GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	A	23.1 tok/s
CodeGeeX 4 9B	9B	A	43.9 tok/s

Frequently asked questions

Can Intel Arc A750 8GB run Gemma 4 E4B?

Yes, Intel Arc A750 8GB can run Gemma 4 E4B with a A grade (Runs with offload). Expected decode speed: 36.8 tok/s.

How much VRAM does Gemma 4 E4B need?

Gemma 4 E4B (8B parameters) requires approximately 7.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 E4B?

The recommended quantization for Gemma 4 E4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 E4B run at on Intel Arc A750 8GB?

On Intel Arc A750 8GB, Gemma 4 E4B achieves approximately 36.8 tokens per second decode speed with a time-to-first-token of 5267ms using Q4_K_M quantization.

Can Intel Arc A750 8GB run Gemma 4 E4B for coding?

For coding workloads, Gemma 4 E4B on Intel Arc A750 8GB receives a A grade with 36.8 tok/s and 18K context.

What context window can Gemma 4 E4B use on Intel Arc A750 8GB?

On Intel Arc A750 8GB, Gemma 4 E4B can safely use up to 18K 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 4 E4B feels slow on Intel Arc A750 8GB?

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.

Would CUDA be a better path than Intel Arc A750 8GB for Gemma 4 E4B?

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 A750 8GB See all hardware for Gemma 4 E4B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/gemma-4-e4b-on-arc-a750-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

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	36.8 tok/s	2873 ms	18K
Coding	A	Runs with offload	36.8 tok/s	5267 ms	18K
Agentic Coding	B	Very compromised (needs ~0.6 GB host RAM)	20.8 tok/s	13533 ms	18K
Reasoning	A	Runs with offload	36.8 tok/s	6225 ms	18K
RAG	B	Very compromised (needs ~0.6 GB host RAM)	20.8 tok/s	16916 ms	18K

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.1 GB

Low

A81

Q3_K_S

3.9 GB

Low

A81

NVFP4

4.5 GB

Medium

A80

Q4_K_MBest for your GPU

4.9 GB

Medium

A80

Q5_K_M

5.8 GB

High

Q6_K

6.6 GB

High

Q8_0

8.6 GB

Very High

F16

16.4 GB

Maximum

Model

Params

Grade