Can Desktop PC · GeForce RTX 3090 · 32 GB RAM run LFM2.5 24B-A2B?

24 GB VRAM + 32 GB RAM · 936 GB/s memory bandwidth · LFM2.5 24B-A2B has 24B parameters (2B active per token).

Short answer

Yes, excellent for chat and acceptable or better for every workload we test.

Best quantization for chat: Q4_K_M (14 GB of weights) on Ollama. Generation: 300–410 tok/s, leaving about 8.4 GB free for your other apps.

How it rates for each workload

Same model and machine, different jobs. Agents and long documents send far bigger prompts than chat, so they need much more speed and memory.

Workload	Verdict	Quant	Generation	First reply	Context
Chatwith Open WebUI	Excellent	Q4_K_M	300–410 tok/s	under 1 s	8K	Details
Coding questionswith Continue	Excellent	Q4_K_M	290–390 tok/s	under 1 s	16K	Details
Coding in a repositorywith Aider	Acceptable	Q4_K_M	250–340 tok/s	≈2 s	32K	Details
Agentic codingwith OpenCode	Acceptable	Q4_K_M	230–320 tok/s	≈2 s	32K	Details
Long documents (64K)with Open WebUI	Excellent	Q4_K_M	170–230 tok/s	≈7 s	64K	Details

Speeds are estimates calibrated against measured benchmarks on similar hardware. “First reply” is the time to read the workload’s opening prompt. How we calculate

Other models for Desktop PC · GeForce RTX 3090 · 32 GB RAM

The best-rated models for chat on this machine.

Everything Desktop PC · GeForce RTX 3090 · 32 GB RAM can run

Cheapest machines for LFM2.5 24B-A2B

Hardware rated comfortable or better for chat, lowest price first.

Same chip, other memory: 64 GB

Different tool, context size or workload?

These pages use typical settings. The full check models your exact setup.

Check my exact setup