Can Granite 4.1 8B run on Intel Arc B570 10GB?

YES — Tight Fit

A76Great

Estimated from fit model

Granite 4.1 8B needs ~9.2 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With Q4_K_M quantization, expect ~45 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: 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) — 9.2 GB, 45.2 tok/s, Tight fit

9.2 GB required10.0 GB available

92% VRAM used

Fit status

Tight fit

Decode

45.2 tok/s

TTFT

4283 ms

Safe context

21K

Memory

9.2 GB / 10.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsGranite 4.1 8B on Intel Arc B570 10GB
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: 45.2 tok/s decode · 4.3s TTFT (warm) · 113 tok/s prefill

What limits this setup

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

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.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	45.2 tok/s	2336 ms	21K
Coding	A	Tight fit	45.2 tok/s	4283 ms	21K
Agentic Coding	B	Very compromised (needs ~0.7 GB host RAM)	25.3 tok/s	11149 ms	21K
Reasoning	A	Tight fit	45.2 tok/s	5062 ms	21K
RAG	B	Very compromised (needs ~0.7 GB host RAM)	25.3 tok/s	13937 ms	21K

Quantization options

How Granite 4.1 8B (8B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A76
Q3_K_S	3	3.9 GB	Low	A77
NVFP4	4	4.5 GB	Medium	A77
Q4_K_M	4	4.9 GB	Medium	A77
Q5_K_M	5	5.8 GB	High	A77
Q6_KBest for your GPU	6	6.6 GB	High	A76
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

Your hardware

More models your Intel Arc B570 10GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	40.2 tok/s
Nemotron Nano 9B v2	9B	A	40.2 tok/s
CodeGeeX 4 9B	9B	A	40.9 tok/s

Frequently asked questions

Can Intel Arc B570 10GB run Granite 4.1 8B?

Yes, Intel Arc B570 10GB can run Granite 4.1 8B with a A grade (Tight fit). Expected decode speed: 45.2 tok/s.

How much VRAM does Granite 4.1 8B need?

Granite 4.1 8B (8B parameters) requires approximately 9.2 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 B570 10GB?

On Intel Arc B570 10GB, Granite 4.1 8B achieves approximately 45.2 tokens per second decode speed with a time-to-first-token of 4283ms using Q4_K_M quantization.

Can Intel Arc B570 10GB run Granite 4.1 8B for coding?

For coding workloads, Granite 4.1 8B on Intel Arc B570 10GB receives a A grade with 45.2 tok/s and 21K context.

What context window can Granite 4.1 8B use on Intel Arc B570 10GB?

On Intel Arc B570 10GB, Granite 4.1 8B can safely use up to 21K 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 B570 10GB?

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 B570 10GB 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 B570 10GB See all hardware for Granite 4.1 8B

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