Can GPT-OSS 20B run on Intel Arc Pro B60 24GB?

YES — Runs Great

S93Excellent

Estimated from fit model

GPT-OSS 20B needs ~18.6 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~44 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) — 18.6 GB, 47.3 tok/s, Runs well

18.6 GB required24.0 GB available

78% VRAM used

Fit status

Runs well

Decode

47.3 tok/s

TTFT

4096 ms

Safe context

52K

Memory

18.6 GB / 24.0 GB

Memory breakdown

Weights12.8 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsGPT-OSS 20B 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: 47.3 tok/s decode · 4.1s TTFT (warm) · 118 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	S	Runs well	47.3 tok/s	2234 ms	52K
Coding	S	Runs well	44.0 tok/s	4403 ms	52K
Agentic Coding	S	Tight fit	47.3 tok/s	5957 ms	52K
Reasoning	S	Runs well	47.3 tok/s	4840 ms	52K
RAG	S	Tight fit	47.3 tok/s	7446 ms	52K

Quantization options

How GPT-OSS 20B (21B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	S86
Q3_K_S	3	10.3 GB	Low	S88
NVFP4	4	11.8 GB	Medium	S89
Q4_K_M	4	12.8 GB	Medium	S89
Q5_K_M	5	15.1 GB	High	S88
Q6_KBest for your GPU	6	17.2 GB	High	S88
Q8_0	8	22.5 GB	Very High	F0
F16	16	43.1 GB	Maximum	F0

Get started

Copy-paste commands to run GPT-OSS 20B on your machine.

Run

ollama run gpt-oss

Your hardware

More models your Intel Arc Pro B60 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	37.2 tok/s
Qwen 3.5 27B	27B	S	16.1 tok/s
Qwen 3.6 27B	27B	S	12.3 tok/s
Qwen 3.6 35B A3B	35B	A	16.6 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	38.5 tok/s

Frequently asked questions

Can Intel Arc Pro B60 24GB run GPT-OSS 20B?

Yes, Intel Arc Pro B60 24GB can run GPT-OSS 20B with a S grade (Runs well). Expected decode speed: 44.0 tok/s.

How much VRAM does GPT-OSS 20B need?

GPT-OSS 20B (21B parameters) requires approximately 18.6 GB of memory with Q4_K_M quantization.

What is the best quantization for GPT-OSS 20B?

The recommended quantization for GPT-OSS 20B is Q4_K_M, which balances quality and memory efficiency.

What speed will GPT-OSS 20B run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, GPT-OSS 20B achieves approximately 44.0 tokens per second decode speed with a time-to-first-token of 4403ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run GPT-OSS 20B for coding?

For coding workloads, GPT-OSS 20B on Intel Arc Pro B60 24GB receives a S grade with 44.0 tok/s and 52K context.

What context window can GPT-OSS 20B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, GPT-OSS 20B can safely use up to 52K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if GPT-OSS 20B 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 GPT-OSS 20B?

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 Pro B60 24GB See all hardware for GPT-OSS 20B

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