Local AI Advisor

Can Laptop · Ryzen AI 9 HX 370 (Radeon 890M) · 32 GB run LFM2.5 24B-A2B?

32 GB unified · 120 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 Lemonade. Generation: 35–60 tok/s, leaving about 8.9 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.

WorkloadVerdictQuantGenerationFirst replyContext
Chatwith Open WebUIExcellentQ4_K_M35–60 tok/s≈2 s8KDetails
Coding questionswith ContinueExcellentQ4_K_M35–60 tok/s≈9 s16KDetails
Coding in a repositorywith AiderAcceptableQ4_K_M30–50 tok/s≈25 s32KDetails
Agentic codingwith OpenCodeAcceptableQ4_K_M30–45 tok/s≈35 s32KDetails
Long documents (64K)with Open WebUIComfortableQ4_K_M20–35 tok/s≈2 min64KDetails

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

Different tool, context size or workload?

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