Can Desktop PC · Ryzen 9 9950X (no GPU) · 64 GB DDR5 run LFM2.5 24B-A2B?

64 GB RAM, no GPU · 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 llama.cpp (llama-server). Generation: 20–35 tok/s, leaving about 38 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	20–35 tok/s	≈2 s	8K	Details
Coding questionswith Continue	Excellent	Q4_K_M	20–35 tok/s	≈10 s	16K	Details
Coding in a repositorywith Aider	Acceptable	Q8_0	11–18 tok/s	≈35 s	32K	Details
Agentic codingwith OpenCode	Acceptable	Q4_K_M	17–30 tok/s	≈45 s	32K	Details
Long documents (64K)with Open WebUI	Acceptable	Q8_0	9–14 tok/s	≈2 min	64K	Details

Speeds are bandwidth-based estimates; no direct benchmark exists for this pair. “First reply” is the time to read the workload’s opening prompt. How we calculate

Other models for Desktop PC · Ryzen 9 9950X (no GPU) · 64 GB DDR5

The best-rated models for chat on this machine.

Everything Desktop PC · Ryzen 9 9950X (no GPU) · 64 GB DDR5 can run

Cheapest machines for LFM2.5 24B-A2B

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

Different tool, context size or workload?

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

Check my exact setup