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Can I run LFM2.5 24B-A2B (MoE) locally?

Liquid AI · 24B parameters (2B active) · chat, reasoning, coding.

Ollama weight files: Q4_K_M 15 GB (lfm2.5:24b); Q8_0 25 GB (lfm2.5:24b-a2b-q8_0).

Yes on 52 machines in this list. The smallest is RTX 3060 Ti 8GB: Q4_K_M, about 16.2 GB, roughly 26 tokens per second, via Ollama. ollama run lfm2.5:24b

Setup on DGX Spark 128GB (NVIDIA, ASUS, Dell, Lenovo, HP, MSI, Acer, Gigabyte)

Open LFM2.5 24B-A2B (MoE) in the checker

Hardware that fits

HardwareRuntimeQuantMemoryFitSpeedCommand
RTX 3060 Ti 8GBOllamaQ4_K_M16.2 GBpartial offload (slower)~26 tok/sollama run lfm2.5:24b
RTX 3070 8GBOllamaQ4_K_M16.2 GBpartial offload (slower)~26 tok/sollama run lfm2.5:24b
RTX 4060 8GBOllamaQ4_K_M16.2 GBpartial offload (slower)~25 tok/sollama run lfm2.5:24b
RX 7600 8GBOllamaQ4_K_M16.2 GBpartial offload (slower)~25 tok/sollama run lfm2.5:24b
RTX 3080 10GBOllamaQ4_K_M16.2 GBpartial offload (slower)~34 tok/sollama run lfm2.5:24b
RTX 3060 12GBOllamaQ4_K_M16.2 GBpartial offload (slower)~38 tok/sollama run lfm2.5:24b
RTX 4070 12GBOllamaQ4_K_M16.2 GBpartial offload (slower)~42 tok/sollama run lfm2.5:24b
RTX 5070 12GBOllamaQ4_K_M16.2 GBpartial offload (slower)~44 tok/sollama run lfm2.5:24b
RTX 4060 Ti 16GBOllamaQ4_K_M16.2 GBpartial offload (slower)~60 tok/sollama run lfm2.5:24b
RTX 4070 Ti Super 16GBOllamaQ4_K_M16.2 GBpartial offload (slower)~116 tok/sollama run lfm2.5:24b
RTX 4080 16GBOllamaQ4_K_M16.2 GBpartial offload (slower)~121 tok/sollama run lfm2.5:24b
RTX 5080 16GBOllamaQ4_K_M16.2 GBpartial offload (slower)~146 tok/sollama run lfm2.5:24b
RX 7800 XT 16GBOllamaQ4_K_M16.2 GBpartial offload (slower)~110 tok/sollama run lfm2.5:24b
RX 9070 XT 16GBOllamaQ4_K_M16.2 GBpartial offload (slower)~112 tok/sollama run lfm2.5:24b
RTX 3090 24GBOllamaQ4_K_M16.2 GBfits in fast memory~225 tok/sollama run lfm2.5:24b
RTX 4090 24GBOllamaQ4_K_M16.2 GBfits in fast memory~242 tok/sollama run lfm2.5:24b
RX 7900 XTX 24GBOllamaQ4_K_M16.2 GBfits in fast memory~230 tok/sollama run lfm2.5:24b
M1 Pro 32GBOllamaQ4_K_M16.2 GBfits in fast memory~64 tok/sollama run lfm2.5:24b
M2 Pro 32GBOllamaQ4_K_M16.2 GBfits in fast memory~64 tok/sollama run lfm2.5:24b
M4 32GBOllamaQ4_K_M16.2 GBfits in fast memory~38 tok/sollama run lfm2.5:24b
M3 Pro 36GBOllamaQ8_026.2 GBfits in fast memory~29 tok/sollama run lfm2.5:24b-a2b-q8_0
Mac Studio M4 Max (32c GPU) 36GBOllamaQ8_026.2 GBfits in fast memory~79 tok/sollama run lfm2.5:24b-a2b-q8_0
Mac Studio M5 Max (32c GPU) 36GBOllamaQ8_026.2 GBfits in fast memory~88 tok/sollama run lfm2.5:24b-a2b-q8_0
RTX 5090 32GBOllamaQ8_026.2 GBfits in fast memory~258 tok/sollama run lfm2.5:24b-a2b-q8_0
M4 Pro 48GBOllamaQ8_026.2 GBfits in fast memory~52 tok/sollama run lfm2.5:24b-a2b-q8_0
Mac Studio M4 Max (40c GPU) 48GBOllamaQ8_026.2 GBfits in fast memory~105 tok/sollama run lfm2.5:24b-a2b-q8_0
Mac Studio M5 Max (40c GPU) 48GBOllamaQ8_026.2 GBfits in fast memory~118 tok/sollama run lfm2.5:24b-a2b-q8_0
M1 Max 64GBOllamaQ8_026.2 GBfits in fast memory~77 tok/sollama run lfm2.5:24b-a2b-q8_0
M2 Max 64GBOllamaQ8_026.2 GBfits in fast memory~77 tok/sollama run lfm2.5:24b-a2b-q8_0
M3 Max 64GBOllamaQ8_026.2 GBfits in fast memory~77 tok/sollama run lfm2.5:24b-a2b-q8_0
M4 Max 64GBOllamaQ8_026.2 GBfits in fast memory~105 tok/sollama run lfm2.5:24b-a2b-q8_0
Mac Studio M4 Max (40c GPU) 64GBOllamaQ8_026.2 GBfits in fast memory~105 tok/sollama run lfm2.5:24b-a2b-q8_0
Mac Studio M5 Max (40c GPU) 64GBOllamaQ8_026.2 GBfits in fast memory~118 tok/sollama run lfm2.5:24b-a2b-q8_0
DGX Spark 64GB (ASUS, Dell, HP, MSI, Acer, Gigabyte)OllamaQ8_026.2 GBfits in fast memory~75 tok/sollama run lfm2.5:24b-a2b-q8_0
Mac Studio M3 Ultra 96GBOllamaQ8_026.2 GBfits in fast memory~157 tok/sollama run lfm2.5:24b-a2b-q8_0
Mac Studio M5 Ultra 96GBOllamaQ8_026.2 GBfits in fast memory~230 tok/sollama run lfm2.5:24b-a2b-q8_0
M3 Max 128GBOllamaQ8_026.2 GBfits in fast memory~77 tok/sollama run lfm2.5:24b-a2b-q8_0
M4 Max 128GBOllamaQ8_026.2 GBfits in fast memory~105 tok/sollama run lfm2.5:24b-a2b-q8_0
Mac Studio M4 Max (40c GPU) 128GBOllamaQ8_026.2 GBfits in fast memory~105 tok/sollama run lfm2.5:24b-a2b-q8_0
Mac Studio M5 Max (40c GPU) 128GBOllamaQ8_026.2 GBfits in fast memory~118 tok/sollama run lfm2.5:24b-a2b-q8_0
DGX Spark 128GB (NVIDIA, ASUS, Dell, Lenovo, HP, MSI, Acer, Gigabyte)OllamaQ8_026.2 GBfits in fast memory~75 tok/sollama run lfm2.5:24b-a2b-q8_0
M2 Ultra 192GBOllamaQ8_026.2 GBfits in fast memory~154 tok/sollama run lfm2.5:24b-a2b-q8_0
2x Mac Studio M5 Max (40c GPU) 128GBllama.cppQ8_0 GGUF26.7 GBfits in fast memory~98 tok/sllama-server -hf unsloth/LFM2.5-24B-A2B-GGUF:Q8_0 --rpc node2:50052 -c 8192 # run rpc-server on each other node
Mac Studio M3 Ultra 256GBOllamaQ8_026.2 GBfits in fast memory~157 tok/sollama run lfm2.5:24b-a2b-q8_0
Mac Studio M5 Ultra (64c GPU) 256GBOllamaQ8_026.2 GBfits in fast memory~230 tok/sollama run lfm2.5:24b-a2b-q8_0
Mac Studio M5 Ultra (80c GPU) 256GBOllamaQ8_026.2 GBfits in fast memory~230 tok/sollama run lfm2.5:24b-a2b-q8_0
2x DGX Spark 128GBllama.cppQ8_0 GGUF26.7 GBfits in fast memory~63 tok/sllama-server -hf unsloth/LFM2.5-24B-A2B-GGUF:Q8_0 --rpc node2:50052 -c 8192 # run rpc-server on each other node
4x Mac Studio M5 Max (40c GPU) 128GBllama.cppQ8_0 GGUF26.7 GBfits in fast memory~87 tok/sllama-server -hf unsloth/LFM2.5-24B-A2B-GGUF:Q8_0 --rpc node2:50052,node3:50052,node4:50052 -c 8192 # run rpc-server on each other node
2x Mac Studio M5 Ultra (80c GPU) 256GBllama.cppQ8_0 GGUF26.7 GBfits in fast memory~192 tok/sllama-server -hf unsloth/LFM2.5-24B-A2B-GGUF:Q8_0 --rpc node2:50052 -c 8192 # run rpc-server on each other node
Mac Studio M3 Ultra (80c GPU) 512GBOllamaQ8_026.2 GBfits in fast memory~157 tok/sollama run lfm2.5:24b-a2b-q8_0
4x DGX Spark 128GBllama.cppQ8_0 GGUF26.7 GBfits in fast memory~55 tok/sllama-server -hf unsloth/LFM2.5-24B-A2B-GGUF:Q8_0 --rpc node2:50052,node3:50052,node4:50052 -c 8192 # run rpc-server on each other node
4x Mac Studio M5 Ultra (80c GPU) 256GBllama.cppQ8_0 GGUF26.7 GBfits in fast memory~169 tok/sllama-server -hf unsloth/LFM2.5-24B-A2B-GGUF:Q8_0 --rpc node2:50052,node3:50052,node4:50052 -c 8192 # run rpc-server on each other node

Estimates, not benchmarks. Model sizes are ollama.com download sizes; memory needed adds headroom for ~8k tokens of context; speeds are rough figures from memory bandwidth. Data updated 2026-10-18.

A project by OnDevice Labs · llms.txt