Can granite 8b code instruct 4k run on Intel Arc B570 10GB?

YES — Runs Great

C54Usable

Estimated from fit model

granite 8b code instruct 4k needs ~7.7 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With Q4_K_M quantization, expect ~42 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 7.7 GB, 42.0 tok/s, Runs well

7.7 GB required10.0 GB available

77% VRAM used

Fit status

Runs well

Decode

42.0 tok/s

TTFT

4604 ms

Safe context

55K

Memory

7.7 GB / 10.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsgranite 8b code instruct 4k 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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 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	C	Runs well	42.0 tok/s	2511 ms	55K
Coding	C	Runs well	42.0 tok/s	4604 ms	55K
Agentic Coding	C	Tight fit	42.0 tok/s	6697 ms	55K
Reasoning	C	Runs well	42.0 tok/s	5441 ms	55K
RAG	C	Tight fit	42.0 tok/s	8371 ms	55K

Quantization options

How granite 8b code instruct 4k (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	C51
Q3_K_S	3	3.9 GB	Low	C53
NVFP4	4	4.5 GB	Medium	C53
Q4_K_M	4	4.9 GB	Medium	C53
Q5_K_M	5	5.8 GB	High	C52
Q6_KBest for your GPU	6	6.6 GB	High	C52
Q8_0	8	8.6 GB	Very High	F0
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run granite 8b code instruct 4k on your machine.

Run

lms load hf-ibm-granite--granite-8b-code-instruct-4k-gguf && lms server start

Upgrade-Optionen

Hardware, die granite 8b code instruct 4k gut ausführt

RTX 5070 12GBGünstige Wahl

12 GB VRAM (+2)672 GB/s (+292)

Raises estimated decode speed by about 107%.86.8 tok/s Dekodierung

Raises estimated decode speed by about 107%.

Moves you onto CUDA, which still has the broadest local-AI runtime coverage.

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

ca. $549 MSRP

RTX 4070 Super 12GBBestes Preis-Leistungs-Verhältnis

12 GB VRAM (+2)504 GB/s (+124)

Raises estimated decode speed by about 74%.73.2 tok/s Dekodierung

Raises estimated decode speed by about 74%.

Moves you onto CUDA, which still has the broadest local-AI runtime coverage.

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

ca. $599 MSRP

Frequently asked questions

Can Intel Arc B570 10GB run granite 8b code instruct 4k?

Yes, Intel Arc B570 10GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 42.0 tok/s.

How much VRAM does granite 8b code instruct 4k need?

granite 8b code instruct 4k (8B parameters) requires approximately 7.7 GB of memory with Q4_K_M quantization.

What is the best quantization for granite 8b code instruct 4k?

The recommended quantization for granite 8b code instruct 4k is Q4_K_M, which balances quality and memory efficiency.

What speed will granite 8b code instruct 4k run at on Intel Arc B570 10GB?

On Intel Arc B570 10GB, granite 8b code instruct 4k achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4604ms using Q4_K_M quantization.

Can Intel Arc B570 10GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on Intel Arc B570 10GB receives a C grade with 42.0 tok/s and 55K context.

What context window can granite 8b code instruct 4k use on Intel Arc B570 10GB?

On Intel Arc B570 10GB, granite 8b code instruct 4k can safely use up to 55K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if granite 8b code instruct 4k 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 8b code instruct 4k?

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.

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