Can LFM2 24B run on RTX PRO 4000 Blackwell 24GB?

YES — Tight Fit

A85Great

Estimated from fit model

LFM2 24B needs ~20.7 GB VRAM. RTX PRO 4000 Blackwell 24GB has 24.0 GB. With Q4_K_M quantization, expect ~41 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, 41.4 tok/s, Tight fit

20.7 GB required24.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

41.4 tok/s

TTFT

4671 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 PRO 4000 Blackwell 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: 41.4 tok/s decode · 4.7s TTFT (warm) · 104 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	41.4 tok/s	2548 ms	38K
Coding	A	Tight fit	41.4 tok/s	4671 ms	38K
Agentic Coding	A	Runs with offload	41.4 tok/s	6794 ms	38K
Reasoning	A	Tight fit	41.4 tok/s	5520 ms	38K
RAG	A	Runs with offload	41.4 tok/s	8492 ms	38K

Quantization options

How LFM2 24B (24B params) fits at each quantization level on RTX PRO 4000 Blackwell 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 PRO 4000 Blackwell 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	85.4 tok/s
Qwen 3.5 27B	27B	S	37 tok/s
Qwen 3.6 27B	27B	S	37.1 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	88.3 tok/s
Qwen 3.5 35B A3B	35B	A	49.1 tok/s

Frequently asked questions

Can RTX PRO 4000 Blackwell 24GB run LFM2 24B?

Yes, RTX PRO 4000 Blackwell 24GB can run LFM2 24B with a A grade (Tight fit). Expected decode speed: 41.4 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 PRO 4000 Blackwell 24GB?

On RTX PRO 4000 Blackwell 24GB, LFM2 24B achieves approximately 41.4 tokens per second decode speed with a time-to-first-token of 4671ms using Q4_K_M quantization.

Can RTX PRO 4000 Blackwell 24GB run LFM2 24B for coding?

For coding workloads, LFM2 24B on RTX PRO 4000 Blackwell 24GB receives a A grade with 41.4 tok/s and 38K context.

What context window can LFM2 24B use on RTX PRO 4000 Blackwell 24GB?

On RTX PRO 4000 Blackwell 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 PRO 4000 Blackwell 24GB See all hardware for LFM2 24B

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