Would CUDA be a better path than Intel Arc B570 10GB for Granite 3.1 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.

Can Granite 3.1 8B run on Intel Arc B570 10GB?

Q: What should I upgrade first if Granite 3.1 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.

YES — Tight Fit

B57Good

Estimated from fit model

Granite 3.1 8B needs ~8.7 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With Q4_K_M quantization, expect ~52 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) — 8.7 GB, 52.0 tok/s, Tight fit

8.7 GB required10.0 GB available

87% VRAM used

Fit status

Tight fit

Decode

52.0 tok/s

TTFT

3724 ms

Safe context

26K

Memory

8.7 GB / 10.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsGranite 3.1 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: 52.0 tok/s decode · 3.7s TTFT (warm) · 130 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	B	Runs well	52.0 tok/s	2031 ms	26K
Coding	B	Tight fit	52.0 tok/s	3724 ms	26K
Agentic Coding	C	Runs with offload (needs ~0.3 GB host RAM)	34.7 tok/s	8111 ms	26K
Reasoning	B	Tight fit	52.0 tok/s	4401 ms	26K
RAG	C	Runs with offload (needs ~0.3 GB host RAM)	34.7 tok/s	10139 ms

Inference speed

Granite 3.1 8B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Granite 3.1 8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	112.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	112.0

Quantization options

How Granite 3.1 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	B56
Q3_K_S	3	3.9 GB	Low	B57
NVFP4	4

Get started

Copy-paste commands to run Granite 3.1 8B on your machine.

Run

ollama run granite3.1-dense

Upgrade options

Hardware that runs Granite 3.1 8B well

Intel Arc B580 12GBBudget pick

12 GB VRAM (+2)456 GB/s (+76)

The raw memory story may look fine, but the software ecosystem is still a constraint here.55.4 tok/s decode

~$249 MSRP

Intel Arc A770 16GBBest value

16 GB VRAM (+6)560 GB/s (+180)

Adds memory headroom for longer context windows and future model growth.63.8 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$349 MSRP

Intel Arc Pro A60 12GBIntel upgrade

12 GB VRAM (+2)384 GB/s (+4)

The raw memory story may look fine, but the software ecosystem is still a constraint here.47.7 tok/s decode

~$499 MSRP

Frequently asked questions

Can Intel Arc B570 10GB run Granite 3.1 8B?

Yes, Intel Arc B570 10GB can run Granite 3.1 8B with a B grade (Tight fit). Expected decode speed: 52.0 tok/s.

How much VRAM does Granite 3.1 8B need?

Granite 3.1 8B (8B parameters) requires approximately 8.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite 3.1 8B?

The recommended quantization for Granite 3.1 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite 3.1 8B run at on Intel Arc B570 10GB?

On Intel Arc B570 10GB, Granite 3.1 8B achieves approximately 52.0 tokens per second decode speed with a time-to-first-token of 3724ms using Q4_K_M quantization.

Can Intel Arc B570 10GB run Granite 3.1 8B for coding?

For coding workloads, Granite 3.1 8B on Intel Arc B570 10GB receives a B grade with 52.0 tok/s and 26K context.

What context window can Granite 3.1 8B use on Intel Arc B570 10GB?

On Intel Arc B570 10GB, Granite 3.1 8B can safely use up to 26K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Granite 3.1 8B feels slow on Intel Arc B570 10GB?

See all results for Intel Arc B570 10GB See all hardware for Granite 3.1 8B

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