Can LFM2 24B run on RTX A5000 24GB?

YES — Tight Fit

A84Great

Estimated from fit model

LFM2 24B needs ~20.7 GB VRAM. RTX A5000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~40 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 20.7 GB, 39.5 tok/s, Tight fit

20.7 GB required24.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

39.5 tok/s

TTFT

4904 ms

Safe context

38K

Memory

20.7 GB / 24.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsLFM2 24B on RTX A5000 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: 39.5 tok/s decode · 4.9s TTFT (warm) · 99 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	39.5 tok/s	2675 ms	38K
Coding	A	Tight fit	39.5 tok/s	4904 ms	38K
Agentic Coding	A	Runs with offload	39.5 tok/s	7134 ms	38K
Reasoning	A	Tight fit	39.5 tok/s	5796 ms	38K
RAG	A	Runs with offload	39.5 tok/s	8917 ms	38K

Inference speed

LFM2 24B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for LFM2 24B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~88 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	88.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	56.3	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	50.8	Tight
RTX 3090 24GB	24 GB	Q4_K_M	48.1	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	40.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	36.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	36.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	34.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	32.3	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.2	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	21.3	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	17.6	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	16.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	7.5	Too big
RTX 3060 12GB	12 GB	Q4_K_M	4.7	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.2	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How LFM2 24B (24B params) fits at each quantization level on RTX A5000 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 RTX A5000 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	81.3 tok/s
Qwen 3.5 27B	27B	S	35.3 tok/s
Qwen 3.6 27B	27B	S	35.4 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	84.1 tok/s
Qwen 3.5 35B A3B	35B	A	45.5 tok/s

Frequently asked questions

Can RTX A5000 24GB run LFM2 24B?

Yes, RTX A5000 24GB can run LFM2 24B with a A grade (Tight fit). Expected decode speed: 39.5 tok/s.

How much VRAM does LFM2 24B need?

LFM2 24B (24B parameters) requires approximately 20.7 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 A5000 24GB?

On RTX A5000 24GB, LFM2 24B achieves approximately 39.5 tokens per second decode speed with a time-to-first-token of 4904ms using Q4_K_M quantization.

Can RTX A5000 24GB run LFM2 24B for coding?

For coding workloads, LFM2 24B on RTX A5000 24GB receives a A grade with 39.5 tok/s and 38K context.

What context window can LFM2 24B use on RTX A5000 24GB?

On RTX A5000 24GB, LFM2 24B can safely use up to 38K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX A5000 24GB See all hardware for LFM2 24B

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