Can LFM2 24B run on AMD Instinct MI60 32GB?

YES — Runs Great

S86Excellent

Estimated from fit model

LFM2 24B needs ~21.2 GB VRAM. AMD Instinct MI60 32GB has 32.0 GB. With Q4_K_M quantization, expect ~37 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 21.2 GB, 36.8 tok/s, Runs well

21.2 GB required32.0 GB available

66% VRAM used

Fit status

Runs well

Decode

36.8 tok/s

TTFT

5255 ms

Safe context

87K

Memory

21.2 GB / 32.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsLFM2 24B on AMD Instinct MI60 32GB
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: 36.8 tok/s decode · 5.3s TTFT (warm) · 92 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	36.8 tok/s	2866 ms	87K
Coding	S	Runs well	36.8 tok/s	5255 ms	87K
Agentic Coding	S	Runs well	36.8 tok/s	7643 ms	87K
Reasoning	S	Runs well	36.8 tok/s	6210 ms	87K
RAG	S	Runs well	36.8 tok/s	9554 ms	87K

Quantization options

How LFM2 24B (24B params) fits at each quantization level on AMD Instinct MI60 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A79
Q3_K_S	3	11.8 GB	Low	A80
NVFP4	4

Get started

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

Run

ollama run lfm2

Your hardware

More models your AMD Instinct MI60 32GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	75.9 tok/s
Qwen 3.5 27B	27B	S	32.9 tok/s

Frequently asked questions

Can AMD Instinct MI60 32GB run LFM2 24B?

Yes, AMD Instinct MI60 32GB can run LFM2 24B with a S grade (Runs well). Expected decode speed: 36.8 tok/s.

How much VRAM does LFM2 24B need?

LFM2 24B (24B parameters) requires approximately 21.2 GB of memory with Q4_K_M quantization.

What is the best quantization for LFM2 24B?

The recommended quantization for LFM2 24B is Q4_K_M, which balances quality and memory efficiency.

What speed will LFM2 24B run at on AMD Instinct MI60 32GB?

On AMD Instinct MI60 32GB, LFM2 24B achieves approximately 36.8 tokens per second decode speed with a time-to-first-token of 5255ms using Q4_K_M quantization.

Can AMD Instinct MI60 32GB run LFM2 24B for coding?

For coding workloads, LFM2 24B on AMD Instinct MI60 32GB receives a S grade with 36.8 tok/s and 87K context.

What context window can LFM2 24B use on AMD Instinct MI60 32GB?

On AMD Instinct MI60 32GB, LFM2 24B can safely use up to 87K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for AMD Instinct MI60 32GB See all hardware for LFM2 24B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/lfm2-24b-on-instinct-mi60-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: