text model · LFM · Windows

Can I run LFM2 24B-A2B on Nvidia GeForce RTX 5090 (32GB)?

Compatibility verdict VRAM threshold engine

Yes, it runs GPU accelerated

Yes. LFM2 24B-A2B runs on Nvidia GeForce RTX 5090 (32GB) at Q4_K_M (~15.4 GB of ~31 GB usable).

Needs ~15.4 GB Device usable ~31 GB

Runs at Q4_K_M using ~15.4 GB of ~31 GB usable. You have room for Q8_0 for higher quality.

That figure is at a 4k context and moves about ±15% as context length changes. Nvidia GeForce RTX 5090 (32GB) leaves ~15.6 GB of headroom, room to step up to Q8_0 for higher quality.

Q4_K_M needed: ~15.4 GB
Usable on device: ~31 GB
Device memory: 32 GB
Best quant: Q4_K_M

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Which quant fits

Quant ladder vs ~31 GB usable

Q2_K

~12.1 GB

Q3_K_M

~13.7 GB

Q4_K_M

~15.4 GB

Q5_K_M

~19.1 GB

Q6_K

~21.7 GB

Q8_0

~25.6 GB

FP16

~46.4 GB

The line marks Nvidia GeForce RTX 5090 (32GB)'s ~31 GB budget; rungs past it are too large.

Run it

Install commands Windows

Pick your tool. All 3 load the same Q4_K_M weights.

Ollama

$ ollama run lfm2:24b

llama.cpp

$ llama-cli -hf LiquidAI/LFM2-24B-A2B-GGUF:Q4_K_M

LM Studio

$ lms get LiquidAI/LFM2-24B-A2B-GGUF

Model LFM

Parameters: 24B (MoE, 2.3B active)
Q4_K_M size: 13.43 GB
Q8_0 size: 23.61 GB
Context: 32k
Ollama tag: lfm2:24b

Full LFM2 24B-A2B requirements →

Device Windows

Memory: 32 GB vram
Usable for weights: ~31 GB
Power draw: ~575 W
Best runtime: vLLM (Linux) / Ollama (CUDA)

Best models for Nvidia GeForce RTX 5090 (32GB) →

You could also run

Run LFM2 24B-A2B on other hardware

Apple M4 (24GB)Apple M4 Pro (24GB)Nvidia GeForce RTX 4090 (24GB)Nvidia GeForce RTX 3090 (24GB)AMD Radeon RX 7900 XTX (24GB)32GB RAM Laptop (CPU/iGPU only)

FAQ

Can Nvidia GeForce RTX 5090 (32GB) run LFM2 24B-A2B?

Yes. LFM2 24B-A2B runs on Nvidia GeForce RTX 5090 (32GB) at Q4_K_M (~15.4 GB of ~31 GB usable).

How much memory does LFM2 24B-A2B need?

Nvidia GeForce RTX 5090 (32GB) has room to spare. At Q4_K_M the weights are ~13.43 GB; with KV cache and runtime overhead, budget ~15.4 GB at a 4k context. It is a Mixture-of-Experts model (24B total / 2.3B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run LFM2 24B-A2B on Windows?

LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.

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Markdown

[![LFM2 24B-A2B on Nvidia GeForce RTX 5090 (32GB)](https://localmodel.run/badge/lfm2-24b-a2b/nvidia-rtx-5090-32gb.svg)](https://localmodel.run/can-i-run/lfm2-24b-a2b/nvidia-rtx-5090-32gb)

Sources

Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.