Can LFM2 24B run on RTX A6000 48GB?

YES — Runs Great

A83Great

Estimated from fit model

LFM2 24B needs ~23.1 GB VRAM. RTX A6000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~43 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 23.1 GB, 42.9 tok/s, Runs well

23.1 GB required48.0 GB available

48% VRAM used

Fit status

Runs well

Decode

42.9 tok/s

TTFT

4517 ms

Safe context

131K

Memory

23.1 GB / 48.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsLFM2 24B on RTX A6000 48GB
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: 42.9 tok/s decode · 4.5s TTFT (warm) · 107 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	A	Runs well	42.9 tok/s	2464 ms	131K
Coding	A	Runs well	42.9 tok/s	4517 ms	131K
Agentic Coding	A	Runs well	42.9 tok/s	6570 ms	131K
Reasoning	A	Runs well	42.9 tok/s	5338 ms	131K
RAG	A	Runs well	42.9 tok/s	8213 ms	131K

Quantization options

How LFM2 24B (24B params) fits at each quantization level on RTX A6000 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A76
Q3_K_S	3	11.8 GB	Low	A77
NVFP4	4	13.4 GB	Medium	A77
Q4_K_M	4	14.6 GB	Medium	A78
Q5_K_M	5	17.3 GB	High	A78
Q6_K	6	19.7 GB	High	A79
Q8_0Best for your GPU	8	25.7 GB	Very High	A81
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 RTX A6000 48GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	88.3 tok/s
Qwen 3.5 27B	27B	S	38.3 tok/s
Qwen 3.6 27B	27B	S	38.4 tok/s
Qwen 3.6 35B A3B	35B	S	74.2 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	91.3 tok/s

Frequently asked questions

Can RTX A6000 48GB run LFM2 24B?

Yes, RTX A6000 48GB can run LFM2 24B with a A grade (Runs well). Expected decode speed: 42.9 tok/s.

How much VRAM does LFM2 24B need?

LFM2 24B (24B parameters) requires approximately 23.1 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 RTX A6000 48GB?

On RTX A6000 48GB, LFM2 24B achieves approximately 42.9 tokens per second decode speed with a time-to-first-token of 4517ms using Q4_K_M quantization.

Can RTX A6000 48GB run LFM2 24B for coding?

For coding workloads, LFM2 24B on RTX A6000 48GB receives a A grade with 42.9 tok/s and 131K context.

What context window can LFM2 24B use on RTX A6000 48GB?

On RTX A6000 48GB, LFM2 24B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX A6000 48GB 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-a6000-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

42.9 tok/s

2464 ms

131K

Coding

Runs well

42.9 tok/s

4517 ms

131K

Agentic Coding

Runs well

42.9 tok/s

6570 ms

131K

Reasoning

Runs well

42.9 tok/s

5338 ms

131K

RAG

Runs well

42.9 tok/s

8213 ms

131K

Quantization options

How LFM2 24B (24B params) fits at each quantization level on RTX A6000 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A76
Q3_K_S	3	11.8 GB	Low	A77
NVFP4	4	13.4 GB	Medium	A77
Q4_K_M	4	14.6 GB	Medium	A78
Q5_K_M	5	17.3 GB	High	A78
Q6_K	6	19.7 GB	High	A79
Q8_0Best for your GPU	8	25.7 GB	Very High	A81
F16	16	49.2 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

88.3 tok/s

Qwen 3.5 27B

27B

38.3 tok/s

Qwen 3.6 27B

27B

38.4 tok/s

Qwen 3.6 35B A3B

35B

74.2 tok/s

Qwen3-VL 30B A3B Instruct

30B

91.3 tok/s

Frequently asked questions

Can RTX A6000 48GB run LFM2 24B?

Yes, RTX A6000 48GB can run LFM2 24B with a A grade (Runs well). Expected decode speed: 42.9 tok/s.

How much VRAM does LFM2 24B need?

LFM2 24B (24B parameters) requires approximately 23.1 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 RTX A6000 48GB?

On RTX A6000 48GB, LFM2 24B achieves approximately 42.9 tokens per second decode speed with a time-to-first-token of 4517ms using Q4_K_M quantization.

Can RTX A6000 48GB run LFM2 24B for coding?

For coding workloads, LFM2 24B on RTX A6000 48GB receives a A grade with 42.9 tok/s and 131K context.

What context window can LFM2 24B use on RTX A6000 48GB?

On RTX A6000 48GB, LFM2 24B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.