LFM2.5-24B-A2B runs at 184 tok/s on an M5 Max, a published figure reported by huggingface.co (llama.cpp, Q4_K_M, 4K-token input). It needs about 17 GB of unified memory. It is Liquid AI's 24B mixture-of-experts model (2.45B active), released under the LFM Open licence.
Want to run LFM2.5-24B-A2B faster — or step up to bigger models? Find your Mac →This is a published figure: a real run, reported by its source and linked below. The index records it as given and never adjusts it.
| Metric | Value |
|---|---|
| Tokens per second | sourced184 tok/s |
| Provenance | published figure · huggingface.co |
| Quantization | Q4_K_M |
| Engine | llama.cpp |
| Context | 4K-token input |
| Figure dated | 2026-10-13 |
| Minimum RAM | 17 GB |
| Parameters | 24B MoE (2.45B active) |
| Chip bandwidth | 614 GB/s |
Each row as the index holds it, with its source. “Mac RAM” is the memory of the Mac a run used, not the model’s requirement (17 GB). The headline figure follows the reference rule: the lower-median measured plain-decode run at 5 bits or fewer (a run whose quant was not stated counts only when no stated-quant run qualifies), else the estimate; a run marked “not used to anchor estimates” is listed here but never becomes the headline.
| Speed | Quant | Engine | Mac RAM | Context | Source | Note |
|---|---|---|---|---|---|---|
| sourced184 tok/s | Q4_K_M | llama.cpp | — | 4K-token input | huggingface.co | Vendor model card: Liquid AI's Pipette device-benchmarking infrastructure on llama.cpp (build not stated). |
Ollama — install it (brew install ollama or the app from ollama.com), then pull the model by its tag, checked on ollama.com on 2026-10-17:
MLX — pick a build from Hugging Face’s MLX listings, then run it with its repo id:
Downloads: Ollama page → · model card →
The 184 tok/s figure above came from llama.cpp at Q4_K_M; other runtimes and quants run at different speeds.
Prefer a GUI? LM Studio downloads GGUF and MLX builds in one click. Step by step: install Ollama on a Mac.
Liquid AI's 24B MoE with 2.45B active (13 Oct 2026), a reasoning model built for on-device agents, 128K context. Liquid's card reports 184 tok/s on an M5 Max (llama.cpp Q4_K_M, 4K prompt) with 15 GB in use, so it is comfortable on a 24 GB Mac and tight on 16 GB. LFM Open License: free commercial use under $10M revenue; MLX needs mlx-lm 0.32.0+.
LFM Open licence · 24B MoE · 128K context · LLMCheck capability 35/50 · see its leaderboard rank →
LFM2.5-24B-A2B needs about 17 GB of unified memory. These current Apple Silicon Macs have the headroom to run it comfortably:
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