Can LFM2 24B run on RTX 6000 Ada Laptop 16GB?

Q: What should I upgrade first if LFM2 24B feels slow on RTX 6000 Ada Laptop 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With NVFP4

A72Great

Estimated from fit model

LFM2 24B needs ~18.7 GB VRAM. RTX 6000 Ada Laptop 16GB has 16.0 GB. With NVFP4 quantization, expect ~19 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload

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.

LFM2 24B at Q4_K_M needs 19.9 GB — too much for RTX 6000 Ada Laptop 16GB (16.0 GB). Runs at NVFP4 (18.7 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 19.9 GB, exceeds 16.0 GB available

19.9 GB required16.0 GB available

124% VRAM needed

3.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

14.7 tok/s

TTFT

13207 ms

Safe context

Memory

19.9 GB / 16.0 GB

Offload

20%

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLFM2 24B on RTX 6000 Ada Laptop 16GB
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: 14.7 tok/s decode · 13.2s TTFT (warm) · 37 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 1.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Very compromised (needs ~2.1 GB host RAM)	16.8 tok/s	6304 ms	4K
Coding	F	Too heavy	14.7 tok/s	13207 ms	4K
Agentic Coding	F	Too heavy	11.5 tok/s	24513 ms	4K
Reasoning	F	Too heavy	14.7 tok/s	15608 ms	4K
RAG	F	Too heavy	11.5 tok/s	30642 ms	4K

Quantization options

How LFM2 24B (24B params) fits at each quantization level on RTX 6000 Ada Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A84
Q3_K_SBest for your GPU	3	11.8 GB	Low	A84

Get started

Copy-paste commands to run LFM2 24B on your machine.

Run

ollama run lfm2

Upgrade options

Hardware that runs LFM2 24B well

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+4)

Makes the model fit on the accelerator instead of staying completely out of reach.15 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,250 MSRP

RTX 3090 24GBBest value

24 GB VRAM (+8)936 GB/s (+360)

Makes the model fit on the accelerator instead of staying completely out of reach.48.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,499 MSRP

RTX 4090 24GBNVIDIA upgrade

24 GB VRAM (+8)1008 GB/s (+432)

Makes the model fit on the accelerator instead of staying completely out of reach.56.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,599 MSRP

Frequently asked questions

Can RTX 6000 Ada Laptop 16GB run LFM2 24B?

Yes, RTX 6000 Ada Laptop 16GB can run LFM2 24B at NVFP4 quantization (Very compromised (needs ~1.9 GB host RAM)). The recommended Q4_K_M requires 19.9 GB which exceeds available memory, but at NVFP4 it needs only 18.7 GB. Expected decode speed: 19.1 tok/s.

How much VRAM does LFM2 24B need?

LFM2 24B (24B parameters) requires approximately 19.9 GB at Q4_K_M quantization. On RTX 6000 Ada Laptop 16GB, it fits at NVFP4 using 18.7 GB.

What is the best quantization for LFM2 24B?

The recommended quantization is Q4_K_M, but on RTX 6000 Ada Laptop 16GB the best fitting quantization is NVFP4, which uses 18.7 GB.

What speed will LFM2 24B run at on RTX 6000 Ada Laptop 16GB?

On RTX 6000 Ada Laptop 16GB, LFM2 24B achieves approximately 19.1 tokens per second decode speed with a time-to-first-token of 10128ms using NVFP4 quantization.

Can RTX 6000 Ada Laptop 16GB run LFM2 24B for coding?

For coding workloads, LFM2 24B on RTX 6000 Ada Laptop 16GB receives a F grade with 14.7 tok/s and 4K context.

What context window can LFM2 24B use on RTX 6000 Ada Laptop 16GB?

On RTX 6000 Ada Laptop 16GB, LFM2 24B can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 6000 Ada Laptop 16GB See all hardware for LFM2 24B

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