Local AI Advisor

Can Laptop · GeForce RTX 4060 Laptop · 32 GB RAM run LFM2.5 24B-A2B?

8 GB VRAM + 32 GB RAM · 256 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: 30–45 tok/s, leaving about 0.0 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_M30–45 tok/s≈1 s8KDetails
Coding questionswith ContinueBorderlineQ4_K_M25–45 tok/s≈5 s16KDetails
Coding in a repositorywith AiderBorderlineQ4_K_M20–35 tok/s≈15 s32KDetails
Agentic codingwith OpenCodeBorderlineQ4_K_M20–35 tok/s≈20 s32KDetails
Long documents (64K)with Open WebUIBorderlineQ8_09–15 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.