Can LFM2 24B run on Intel Arc Pro B60 24GB?

YES — Tight Fit

A82Great

○Estimated from fit model

LFM2 24B needs ~20.4 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 20.4 GB, 18.1 tok/s, Tight fit

20.4 GB required24.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

18.1 tok/s

TTFT

10707 ms

Safe context

40K

Memory

20.4 GB / 24.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsLFM2 24B 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: 18.1 tok/s decode · 10.7s TTFT (warm) · 45 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	18.1 tok/s	5840 ms	40K
Coding	A	Tight fit	18.1 tok/s	10707 ms	40K
Agentic Coding	A	Runs with offload	18.1 tok/s	15574 ms	40K
Reasoning	A	Tight fit	18.1 tok/s	12654 ms	40K
RAG	A	Runs with offload	18.1 tok/s	19468 ms	40K

Quantization options

How LFM2 24B (24B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A82
Q3_K_S	3	11.8 GB	Low	A83
NVFP4	4	13.4 GB	Medium	A83
Q4_K_M	4	14.6 GB	Medium	A83
Q5_K_MBest for your GPU	5	17.3 GB	High	A83
Q6_K	6	19.7 GB	High	F0
Q8_0	8	25.7 GB	Very High	F0
F16	16	49.2 GB	Maximum	F0

Get started

Copy-paste commands to run LFM2 24B on your machine.

Run

ollama run lfm2

Your hardware

More models your Intel Arc Pro B60 24GB can run

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

Frequently asked questions

See all results for Intel Arc Pro B60 24GB See all hardware for LFM2 24B

URL: https://willitrunai.com/can-run/lfm2-24b-on-arc-pro-b60-24gb

⇱ LFM2 24B on Intel Arc Pro B60 24GB? TIGHT FIT