Can LFM2 24B run on Radeon PRO W7700 16GB?

YES — With NVFP4

A72Great

Estimated from fit model

LFM2 24B needs ~18.4 GB VRAM. Radeon PRO W7700 16GB has 16.0 GB. With NVFP4 quantization, expect ~16 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: 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 19.6 GB — too much for Radeon PRO W7700 16GB (16.0 GB). Runs at NVFP4 (18.4 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 19.6 GB, exceeds 16.0 GB available

19.6 GB required16.0 GB available

123% VRAM needed

3.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

12.2 tok/s

TTFT

15826 ms

Safe context

Memory

19.6 GB / 16.0 GB

Offload

20%

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLFM2 24B on Radeon PRO W7700 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: 12.2 tok/s decode · 15.8s TTFT (warm) · 31 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 1.7 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 ~1.9 GB host RAM)	14.0 tok/s	7538 ms	4K
Coding	F	Too heavy	12.2 tok/s	15826 ms	4K
Agentic Coding	F	Too heavy	9.6 tok/s	29478 ms	4K
Reasoning	F	Too heavy	12.2 tok/s	18703 ms	4K
RAG	F	Too heavy	9.6 tok/s	36848 ms	4K

Quantization options

How LFM2 24B (24B params) fits at each quantization level on Radeon PRO W7700 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
NVFP4	4	13.4 GB	Medium	F0
Q4_K_M	4	14.6 GB	Medium	F0
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

Upgrade-Optionen

Hardware, die LFM2 24B gut ausführt

RX 7900 XT 20GBGünstige Wahl

20 GB VRAM (+4)800 GB/s (+224)

Makes the model fit on the accelerator instead of staying completely out of reach.35.2 tok/s Dekodierung

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.

ca. $899 MSRP

RX 7900 XTX 24GBBestes Preis-Leistungs-Verhältnis

24 GB VRAM (+8)960 GB/s (+384)

Makes the model fit on the accelerator instead of staying completely out of reach.50.8 tok/s Dekodierung

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.

ca. $999 MSRP

Radeon AI PRO R9700 32GBAMD-Upgrade

32 GB VRAM (+16)640 GB/s (+64)

Makes the model fit on the accelerator instead of staying completely out of reach.27.7 tok/s Dekodierung

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.

ca. $1,899 MSRP

Frequently asked questions

Can Radeon PRO W7700 16GB run LFM2 24B?

Yes, Radeon PRO W7700 16GB can run LFM2 24B at NVFP4 quantization (Very compromised (needs ~1.7 GB host RAM)). The recommended Q4_K_M requires 19.6 GB which exceeds available memory, but at NVFP4 it needs only 18.4 GB. Expected decode speed: 16.0 tok/s.

How much VRAM does LFM2 24B need?

LFM2 24B (24B parameters) requires approximately 19.6 GB at Q4_K_M quantization. On Radeon PRO W7700 16GB, it fits at NVFP4 using 18.4 GB.

What is the best quantization for LFM2 24B?

The recommended quantization is Q4_K_M, but on Radeon PRO W7700 16GB the best fitting quantization is NVFP4, which uses 18.4 GB.

What speed will LFM2 24B run at on Radeon PRO W7700 16GB?

On Radeon PRO W7700 16GB, LFM2 24B achieves approximately 16.0 tokens per second decode speed with a time-to-first-token of 12112ms using NVFP4 quantization.

Can Radeon PRO W7700 16GB run LFM2 24B for coding?

For coding workloads, LFM2 24B on Radeon PRO W7700 16GB receives a F grade with 12.2 tok/s and 4K context.

What context window can LFM2 24B use on Radeon PRO W7700 16GB?

On Radeon PRO W7700 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.

What should I upgrade first if LFM2 24B feels slow on Radeon PRO W7700 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.

See all results for Radeon PRO W7700 16GB See all hardware for LFM2 24B

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