AllocateLFM2.5-8B-A1B GPU requirements
What it takes to run LFM2.5-8B-A1B locally: memory by quantization, the smallest GPU that fits, and the managed alternative.
LFM2.5-8B-A1B serves up to 32,768 tokens of context; the KV cache grows linearly toward that ceiling, so the slider below shows exactly what longer context costs in memory.
Smallest single device that fits: RTX 4090 (24 GB)
How it works
Common questions
How much VRAM does LFM2.5-8B-A1B need?
At 8K context: roughly 19 GB at FP16, 9 GB at 8-bit, and 5 GB at 4-bit, including KV cache and runtime overhead. Longer context adds memory linearly.
Can a single GPU run LFM2.5-8B-A1B?
At 4-bit, yes: a RTX 4090 (24 GB) or larger handles it at 8K context. At FP16 you need a RTX 4090 (24 GB) or larger.
Why does LFM2.5-8B-A1B need so much memory as a MoE model?
Only some experts activate per token, which sets speed, but all 8 billion parameters must sit in memory. MoE trades memory for throughput.
What license is LFM2.5-8B-A1B under?
Custom license. Check the license terms for your use case before deployment.
Is there an alternative to buying GPUs for LFM2.5-8B-A1B?
Yes: managed inference. Allocate serves LFM2.5-8B-A1B token-metered from $0.03 per million input tokens at provider list price, plus the 7% transaction fee. Idle time costs nothing.
More free tools
Allocate serves open-weight models like LFM2.5-8B-A1B token-metered inside your own boundary. No hardware to buy, and if you fine-tune on your data, the weights are yours.