Would CUDA be a better path than Intel Arc A770 16GB for LFM2 24B?

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 LFM2 24B run on Intel Arc A770 16GB?

Q: What should I upgrade first if LFM2 24B feels slow on Intel Arc A770 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With NVFP4

A71Great

Estimated from fit model

LFM2 24B needs ~18.4 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With NVFP4 quantization, expect ~12 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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.

LFM2 24B at Q4_K_M needs 19.6 GB — too much for Intel Arc A770 16GB (16.0 GB). Runs at NVFP4 (18.4 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 19.6 GB, exceeds 16.0 GB available

19.6 GB required16.0 GB available

123% VRAM needed

3.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

9.1 tok/s

TTFT

21342 ms

Safe context

Memory

19.6 GB / 16.0 GB

Offload

20%

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLFM2 24B on Intel Arc A770 16GB
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: 9.1 tok/s decode · 21.3s TTFT (warm) · 23 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

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.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Very compromised (needs ~1.9 GB host RAM)	10.4 tok/s	10166 ms	4K
Coding	F	Too heavy	9.1 tok/s	21342 ms	4K
Agentic Coding	F	Too heavy	7.1 tok/s	39754 ms	4K
Reasoning	F	Too heavy	9.1 tok/s	25222 ms	4K
RAG	F	Too heavy	7.1 tok/s	49692 ms	4K

Quantization options

How LFM2 24B (24B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A84
Q3_K_SBest for your GPU	3	11.8 GB	Low	A84

Get started

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

Run

ollama run lfm2

Upgrade options

Hardware that runs LFM2 24B well

Intel Arc Pro B60 24GBBudget pick

24 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.18.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$599 MSRP

RTX 5090 32GBBiggest leap

32 GB VRAM (+16)1792 GB/s (+1232)

Makes the model fit on the accelerator instead of staying completely out of reach.70.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,999 MSRP

Intel Data Center GPU Max 1550 128GBBest value

128 GB VRAM (+112)3200 GB/s (+2640)

Makes the model fit on the accelerator instead of staying completely out of reach.148 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$15,000 MSRP

Gaudi 3 128GBIntel upgrade

128 GB VRAM (+112)3700 GB/s (+3140)

Makes the model fit on the accelerator instead of staying completely out of reach.190.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$15,000 MSRP

Frequently asked questions

Can Intel Arc A770 16GB run LFM2 24B?

Yes, Intel Arc A770 16GB can run LFM2 24B at NVFP4 quantization (Very compromised (needs ~1.7 GB host RAM)). The recommended Q4_K_M requires 19.6 GB which exceeds available memory, but at NVFP4 it needs only 18.4 GB. Expected decode speed: 11.9 tok/s.

How much VRAM does LFM2 24B need?

LFM2 24B (24B parameters) requires approximately 19.6 GB at Q4_K_M quantization. On Intel Arc A770 16GB, it fits at NVFP4 using 18.4 GB.

What is the best quantization for LFM2 24B?

The recommended quantization is Q4_K_M, but on Intel Arc A770 16GB the best fitting quantization is NVFP4, which uses 18.4 GB.

What speed will LFM2 24B run at on Intel Arc A770 16GB?

On Intel Arc A770 16GB, LFM2 24B achieves approximately 11.9 tokens per second decode speed with a time-to-first-token of 16334ms using NVFP4 quantization.

Can Intel Arc A770 16GB run LFM2 24B for coding?

For coding workloads, LFM2 24B on Intel Arc A770 16GB receives a F grade with 9.1 tok/s and 4K context.

What context window can LFM2 24B use on Intel Arc A770 16GB?

On Intel Arc A770 16GB, LFM2 24B can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Intel Arc A770 16GB See all hardware for LFM2 24B

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