Can Granite 4.1 8B run on Intel Arc A750 8GB?

BARELY — Tight on Memory

B64Good

Estimated from fit model

Granite 4.1 8B needs ~9.0 GB VRAM. Intel Arc A750 8GB has 8.0 GB. With Q4_K_M quantization, expect ~28 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: 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) — 9.0 GB, 28.2 tok/s, Very compromised (needs ~0.6 GB host RAM)

9.0 GB required8.0 GB available

113% VRAM needed

1.0 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.6 GB host RAM)

Decode

28.2 tok/s

TTFT

6854 ms

Safe context

Memory

9.0 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights4.9 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsGranite 4.1 8B 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: 28.2 tok/s decode · 6.9s TTFT (warm) · 71 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload	48.5 tok/s	2177 ms	9K
Coding	B	Very compromised (needs ~0.6 GB host RAM)	28.2 tok/s	6854 ms	9K
Agentic Coding	F	Too heavy	17.1 tok/s	16506 ms	9K
Reasoning	B	Very compromised (needs ~0.6 GB host RAM)	28.2 tok/s	8101 ms	9K
RAG	F	Too heavy	17.1 tok/s	20633 ms	9K

Quantization options

How Granite 4.1 8B (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	A78
Q3_K_S	3	3.9 GB	Low	A78
NVFP4	4	4.5 GB	Medium	A77
Q4_K_MBest for your GPU	4	4.9 GB	Medium	A77
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 Granite 4.1 8B on your machine.

Run

ollama run granite4.1:8b

Opções de upgrade

Hardware que roda bem Granite 4.1 8B

Intel Arc B570 10GBOpção econômica

10 GB VRAM (+2)

Removes host-memory offload, which is usually the single biggest latency and throughput win.45.2 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 60%.

~$219 MSRP

Intel Arc B580 12GBMelhor custo-benefício

12 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.48.2 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 71%.

~$249 MSRP

Intel Arc A770 16GBUpgrade Intel

16 GB VRAM (+8)560 GB/s (+48)

Removes host-memory offload, which is usually the single biggest latency and throughput win.55.5 tok/s decodificação

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 97%.

~$349 MSRP

Frequently asked questions

Can Intel Arc A750 8GB run Granite 4.1 8B?

Yes, Intel Arc A750 8GB can run Granite 4.1 8B with a B grade (Very compromised (needs ~0.6 GB host RAM)). Expected decode speed: 28.2 tok/s.

How much VRAM does Granite 4.1 8B need?

Granite 4.1 8B (8B parameters) requires approximately 9.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite 4.1 8B?

The recommended quantization for Granite 4.1 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite 4.1 8B run at on Intel Arc A750 8GB?

On Intel Arc A750 8GB, Granite 4.1 8B achieves approximately 28.2 tokens per second decode speed with a time-to-first-token of 6854ms using Q4_K_M quantization.

Can Intel Arc A750 8GB run Granite 4.1 8B for coding?

For coding workloads, Granite 4.1 8B on Intel Arc A750 8GB receives a B grade with 28.2 tok/s and 9K context.

What context window can Granite 4.1 8B use on Intel Arc A750 8GB?

On Intel Arc A750 8GB, Granite 4.1 8B can safely use up to 9K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Granite 4.1 8B feels slow on Intel Arc A750 8GB?

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 A750 8GB for Granite 4.1 8B?

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 Granite 4.1 8B

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