Can LFM2 24B run on Quadro RTX 6000 24GB?

YES — Tight Fit

A84Great

○Estimated from fit model

LFM2 24B needs ~20.7 GB VRAM. Quadro RTX 6000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~34 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, 34.0 tok/s, Tight fit

20.7 GB required24.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

34.0 tok/s

TTFT

5686 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 Quadro RTX 6000 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: 34.0 tok/s decode · 5.7s TTFT (warm) · 85 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	34.0 tok/s	3102 ms	38K
Coding	A	Tight fit	34.0 tok/s	5686 ms	38K
Agentic Coding	A	Runs with offload	34.0 tok/s	8271 ms	38K
Reasoning	A	Tight fit	34.0 tok/s	6720 ms	38K
RAG	A	Runs with offload	34.0 tok/s	10338 ms	38K

Quantization options

How LFM2 24B (24B params) fits at each quantization level on Quadro RTX 6000 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 Quadro RTX 6000 24GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	70.1 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	30.4 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	30.5 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	72.5 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	A	37.9 tok/s

Frequently asked questions

See all results for Quadro RTX 6000 24GB See all hardware for LFM2 24B

URL: https://willitrunai.com/can-run/lfm2-24b-on-quadro-rtx-6000-24gb

⇱ LFM2 24B on Quadro RTX 6000 24GB? TIGHT FIT