Local AI Advisor

Can Laptop · GeForce RTX 5090 Laptop · 32 GB RAM run LFM2.5 24B-A2B?

24 GB VRAM + 32 GB RAM · 896 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: 260–430 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.

WorkloadVerdictQuantGenerationFirst replyContext
Chatwith Open WebUIExcellentQ4_K_M260–430 tok/sunder 1 s8KDetails
Coding questionswith ContinueExcellentQ4_K_M250–410 tok/sunder 1 s16KDetails
Coding in a repositorywith AiderAcceptableQ4_K_M210–360 tok/s≈2 s32KDetails
Agentic codingwith OpenCodeAcceptableQ4_K_M200–330 tok/s≈2 s32KDetails
Long documents (64K)with Open WebUIExcellentQ4_K_M140–240 tok/s≈5 s64KDetails

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.