Local AI Advisor

Can Desktop PC · GeForce RTX 5060 Ti 16GB · 32 GB RAM run LFM2.5 24B-A2B?

16 GB VRAM + 32 GB RAM · 448 GB/s memory bandwidth · LFM2.5 24B-A2B has 24B parameters (2B active per token).

Short answer

It loads, but it's borderline even for chat.

Best quantization for chat: Q4_K_M (14 GB of weights) on Ollama. Generation: 180–240 tok/s, leaving about 0.4 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 WebUIBorderlineQ4_K_M180–240 tok/sunder 1 s8KDetails
Coding questionswith ContinueBorderlineQ4_K_M170–230 tok/s≈1 s16KDetails
Coding in a repositorywith AiderBorderlineQ4_K_M130–180 tok/s≈3 s32KDetails
Agentic codingwith OpenCodeBorderlineQ4_K_M130–170 tok/s≈4 s32KDetails
Long documents (64K)with Open WebUIBorderlineQ4_K_M70–95 tok/s≈15 s64KDetails

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

Different tool, context size or workload?

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