Can LFM2 24B run on RTX 4000 Ada 20GB?

YES — With Offload

A82Great

○Estimated from fit model

LFM2 24B needs ~20.0 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: 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) — 20.0 GB, 20.6 tok/s, Runs with offload

20.0 GB required20.0 GB available

100% VRAM used

Fit status

Runs with offload

Decode

20.6 tok/s

TTFT

9389 ms

Safe context

16K

Memory

20.0 GB / 20.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsLFM2 24B on RTX 4000 Ada 20GB
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: 20.6 tok/s decode · 9.4s TTFT (warm) · 52 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	20.6 tok/s	5122 ms	16K
Coding	A	Runs with offload	20.6 tok/s	9389 ms	16K
Agentic Coding	A	Very compromised (needs ~1.6 GB host RAM)	12.2 tok/s	23165 ms	16K
Reasoning	A	Runs with offload	20.6 tok/s	11097 ms	16K
RAG	A	Very compromised (needs ~1.6 GB host RAM)	12.2 tok/s	28957 ms	16K

Quantization options

How LFM2 24B (24B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A84
Q3_K_S	3	11.8 GB	Low	A84
NVFP4	4	13.4 GB	Medium	A83
Q4_K_MBest for your GPU	4	14.6 GB	Medium	A83
Q5_K_M	5	17.3 GB	High	F0
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 4000 Ada 20GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	A	23.8 tok/s
👁 Alibaba Qwen 3.5 27B	27B	A	10.7 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	10.1 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	A	25.3 tok/s
👁 Alibaba Qwen 3 30B A3B	30.5B	A	23.8 tok/s

Frequently asked questions

See all results for RTX 4000 Ada 20GB See all hardware for LFM2 24B

URL: https://willitrunai.com/can-run/lfm2-24b-on-rtx-4000-ada-20gb