Can LFM2 24B run on Quadro RTX 8000 48GB?

YES — Runs Great

A82Great

○Estimated from fit model

LFM2 24B needs ~23.1 GB VRAM. Quadro RTX 8000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~34 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, 34.0 tok/s, Runs well

23.1 GB required48.0 GB available

48% VRAM used

Fit status

Runs well

Decode

34.0 tok/s

TTFT

5686 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 Quadro RTX 8000 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: 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	A	Runs well	34.0 tok/s	3102 ms	131K
Coding	A	Runs well	34.0 tok/s	5686 ms	131K
Agentic Coding	A	Runs well	34.0 tok/s	8271 ms	131K
Reasoning	A	Runs well	34.0 tok/s	6720 ms	131K
RAG	A	Runs well	34.0 tok/s	10338 ms	131K

Quantization options

How LFM2 24B (24B params) fits at each quantization level on Quadro RTX 8000 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 Quadro RTX 8000 48GB 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 Qwen 3.6 35B A3B	35B	S	58.9 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	72.5 tok/s

Frequently asked questions

See all results for Quadro RTX 8000 48GB See all hardware for LFM2 24B

URL: https://willitrunai.com/can-run/lfm2-24b-on-quadro-rtx-8000-48gb