01What it can do
- 2B actifs: très rapide, même en partie sur CPU
- ~14 GB VRAM en Q4: tient sur une carte 16 GB
- Multilingue, dont le français
- 128k context
- —Licence LFM restreinte: vérifier les conditions commerciales
- —Pas de tag Ollama officiel (installation via Hugging Face)
- —No public score in our sources: performance must be verified on your use cases
04Install
Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.
02Required memory
Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.
What hardware do you need for LFM2.5 24B-A2B?
To run LFM2.5 24B-A2B locally with Q4 quantization, you need about 14 GB of VRAM. An option to compare: GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395) — leave some headroom for the system and context; check engine compatibility with the GPU.
Why this choice? Our complete guide on GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395) →
Affiliate links — commission possible at no extra cost to you; independent recommendation. As an Amazon Associate, BestLLMfor earns from qualifying purchases.
On the go: LFM2.5 24B-A2B also runs on a RTX laptop PC (16 GB of VRAM) →
Your private ChatGPT, free, on your own machine in an hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.
- Lifetime online access
- PDF + files
- Refunded within 30 days
03Expected speed
Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.