Can Desktop PC · GeForce RTX 4060 Ti 16GB · 32 GB RAM run LFM2.5 24B-A2B?

Q: Can Desktop PC · GeForce RTX 4060 Ti 16GB · 32 GB RAM run LFM2.5 24B-A2B?

It loads, but it's borderline even for chat. Rated by Local AI Advisor's engine at Q4_K_M, about 85–140 tok/s for chat.

16 GB VRAM + 32 GB RAM · 288 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: 85–140 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.

Workload	Verdict	Quant	Generation	First reply	Context
Chatwith Open WebUI	Borderline	Q4_K_M	85–140 tok/s	under 1 s	8K	Details
Coding questionswith Continue	Borderline	Q4_K_M	80–130 tok/s	≈1 s	16K	Details
Coding in a repositorywith Aider	Borderline	Q4_K_M	65–110 tok/s	≈4 s	32K	Details
Agentic codingwith OpenCode	Borderline	Q4_K_M	60–100 tok/s	≈5 s	32K	Details
Long documents (64K)with Open WebUI	Borderline	Q4_K_M	40–65 tok/s	≈20 s	64K	Details

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

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Different tool, context size or workload?

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

Check my exact setup