Liquid AI
LFM 2.5 2.6B
Liquid AI model
Model Summary
Family
LFM 2.5
Version
2.5
Parameters
2.7B (est.)
Parameter counts for closed models are estimates; vendors rarely publish exact sizes.
VRAM Requirements by Quantization
Memory needed to serve LFM 2.5 2.6B for inference, including a 20% overhead for activations and KV cache.
| Precision | VRAM needed | Smallest single GPU that fits |
|---|---|---|
| INT4 (4-bit) | 1.51 GB | NVIDIA P100 SXM2 (16 GB) |
| INT8 (8-bit) | 3.02 GB | NVIDIA P100 SXM2 (16 GB) |
| FP16 (16-bit) | 6.03 GB | NVIDIA P100 SXM2 (16 GB) |
| FP32 (32-bit) | 12.07 GB | NVIDIA P100 SXM2 (16 GB) |
Recommended GPU Configurations
Cheapest on-demand configurations to serve LFM 2.5 2.6B at 8-bit (3 GB VRAM).
1x NVIDIA T4
16 GB total VRAM · Turing
~$0.50/h
1x NVIDIA P100 SXM2
16 GB total VRAM · Pascal
~$0.60/h
1x NVIDIA L4
24 GB total VRAM · Ada Lovelace
~$1.00/h
Quick GPU Planning
Use the calculator pre-filled with this exact version to estimate memory, speed, and compute requirements in a few clicks.
Access Pre-filled CalculatorFrequently Asked Questions
How much VRAM do you need to run LFM 2.5 2.6B?
With an estimated 2.7B parameters, LFM 2.5 2.6B needs roughly 3 GB of VRAM in 8-bit (INT8), 2 GB in 4-bit, and 6 GB in FP16, including a 20% overhead for activations and KV cache.
Which GPUs can run LFM 2.5 2.6B?
At 8-bit quantization, the most cost-effective option is 1x NVIDIA T4 (16 GB combined VRAM, around $0.50/hour on-demand). Higher-end cards like the NVIDIA B200 or AMD MI355X reduce the GPU count needed.
Can LFM 2.5 2.6B run on a single GPU?
Yes. In 8-bit, a single NVIDIA P100 SXM2 (16 GB) fits the model.
How much does it cost to serve LFM 2.5 2.6B in the cloud?
Renting 1x T4 costs on the order of $0.50/hour, i.e. about $365/month running 24/7. Actual prices vary by provider and commitment; spot and reserved capacity can be significantly cheaper.
Deploy on a GPU cloud
Rent 1x T4 by the hour instead of buying hardware.
