Local AI Advisor

Can Desktop PC · GeForce RTX 5070 Ti · 32 GB RAM run LFM2.5 24B-A2B?

16 GB VRAM + 32 GB RAM · 896 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: 340–460 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_M340–460 tok/sunder 1 s8KDetails
Coding questionswith ContinueBorderlineQ4_K_M320–440 tok/sunder 1 s16KDetails
Coding in a repositorywith AiderBorderlineQ4_K_M240–330 tok/s≈2 s32KDetails
Agentic codingwith OpenCodeBorderlineQ4_K_M230–310 tok/s≈2 s32KDetails
Long documents (64K)with Open WebUIBorderlineQ4_K_M100–140 tok/s≈9 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.