Can granite 8b code instruct 4k run on Intel Arc Pro B60 24GB?

YES — Runs Great

C48Usable

Estimated from fit model

granite 8b code instruct 4k needs ~9.1 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~51 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 9.1 GB, 50.5 tok/s, Runs well

9.1 GB required24.0 GB available

38% VRAM used

Fit status

Runs well

Decode

50.5 tok/s

TTFT

3837 ms

Safe context

270K

Memory

9.1 GB / 24.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsgranite 8b code instruct 4k on Intel Arc Pro B60 24GB
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: 50.5 tok/s decode · 3.8s TTFT (warm) · 126 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	50.5 tok/s	2093 ms	270K
Coding	C	Runs well	50.5 tok/s	3837 ms	270K
Agentic Coding	C	Runs well	50.5 tok/s	5581 ms	270K
Reasoning	C	Runs well	50.5 tok/s	4534 ms	270K
RAG	C	Runs well	50.5 tok/s	6976 ms	270K

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C44
Q3_K_S	3	3.9 GB	Low	C45
NVFP4	4	4.5 GB	Medium	C45
Q4_K_M	4	4.9 GB	Medium	C45
Q5_K_M	5	5.8 GB	High	C46
Q6_K	6	6.6 GB	High	C46
Q8_0	8	8.6 GB	Very High	C48
F16Best for your GPU	16	16.4 GB	Maximum	C50

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 5090 32GBGünstige Wahl

32 GB VRAM (+8)1792 GB/s (+1336)

Raises estimated decode speed by about 201%.152 tok/s Dekodierung

Raises estimated decode speed by about 201%.

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

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

ca. $1,999 MSRP

MacBook Pro M4 Max 36GBBestes Preis-Leistungs-Verhältnis

36 GB Unified (+12)

This setup is broadly balanced for this model.57.7 tok/s Dekodierung

ca. $2,499 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run granite 8b code instruct 4k?

Yes, Intel Arc Pro B60 24GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 50.5 tok/s.

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

granite 8b code instruct 4k (8B parameters) requires approximately 9.1 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 Pro B60 24GB?

On Intel Arc Pro B60 24GB, granite 8b code instruct 4k achieves approximately 50.5 tokens per second decode speed with a time-to-first-token of 3837ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on Intel Arc Pro B60 24GB receives a C grade with 50.5 tok/s and 270K context.

What context window can granite 8b code instruct 4k use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, granite 8b code instruct 4k can safely use up to 270K 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 Pro B60 24GB?

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 Pro B60 24GB 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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