Can LFM2 24B run on NVIDIA V100 32GB?

YES — Runs Great

S87Excellent

Estimated from fit model

LFM2 24B needs ~21.5 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~41 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 21.5 GB, 44.3 tok/s, Runs well

21.5 GB required32.0 GB available

67% VRAM used

Fit status

Runs well

Decode

44.3 tok/s

TTFT

4372 ms

Safe context

85K

Memory

21.5 GB / 32.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsLFM2 24B on NVIDIA V100 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: 44.3 tok/s decode · 4.4s TTFT (warm) · 111 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	44.3 tok/s	2385 ms	85K
Coding	S	Runs well	41.2 tok/s	4700 ms	85K
Agentic Coding	S	Runs well	44.3 tok/s	6360 ms	85K
Reasoning	S	Runs well	44.3 tok/s	5167 ms	85K
RAG	S	Runs well	44.3 tok/s	7950 ms	85K

Quantization options

How LFM2 24B (24B params) fits at each quantization level on NVIDIA V100 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	13.4 GB	Medium	A81
Q4_K_M	4	14.6 GB	Medium	A82
Q5_K_M	5	17.3 GB	High	A83
Q6_K	6	19.7 GB	High	A82
Q8_0Best for your GPU	8	25.7 GB	Very High	A82
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 NVIDIA V100 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	91.2 tok/s
Qwen 3.5 27B	27B	S	39.5 tok/s
Qwen 3.6 27B	27B	S	39.7 tok/s
Qwen 3.6 35B A3B	35B	S	76.6 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	94.3 tok/s

Frequently asked questions

Can NVIDIA V100 32GB run LFM2 24B?

Yes, NVIDIA V100 32GB can run LFM2 24B with a S grade (Runs well). Expected decode speed: 41.2 tok/s.

How much VRAM does LFM2 24B need?

LFM2 24B (24B parameters) requires approximately 21.5 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 NVIDIA V100 32GB?

On NVIDIA V100 32GB, LFM2 24B achieves approximately 41.2 tokens per second decode speed with a time-to-first-token of 4700ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run LFM2 24B for coding?

For coding workloads, LFM2 24B on NVIDIA V100 32GB receives a S grade with 41.2 tok/s and 85K context.

What context window can LFM2 24B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, LFM2 24B can safely use up to 85K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See all hardware for LFM2 24B

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