LFM2.5 24B A2B FP8

Name: LFM2.5 24B A2B FP8
Author: Liquid AI

by Liquid AILFM2.5

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released October 2026131K context≈ 98,304 words

A mid-sized model from LiquidAI's LFM2.5 family running in FP8 precision, which reduces memory footprint while preserving most of the full-precision capability. It handles long contexts well, supporting up to 131K tokens, making it suited for tasks involving lengthy documents. The FP8 quantization means slightly lower numerical precision than a full BF16 variant, a trade-off for deployment efficiency.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Reasoning & Logic

Strong

Factual Knowledge

LFM2.5 24B A2B FP8

by Liquid AILFM2.5

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released October 2026131K context≈ 98,304 words

A mid-sized model from LiquidAI's LFM2.5 family running in FP8 precision, which reduces memory footprint while preserving most of the full-precision capability. It handles long contexts well, supporting up to 131K tokens, making it suited for tasks involving lengthy documents. The FP8 quantization means slightly lower numerical precision than a full BF16 variant, a trade-off for deployment efficiency.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Reasoning & Logic

Strong

Factual Knowledge

Glossary

BF16A 16-bit floating-point format that balances precision and memory efficiency, commonly used for training and deploying large language models.FP8 PrecisionA data format that stores numbers using 8 bits instead of the standard 32 bits, significantly reducing memory requirements with minimal quality loss.FP8 QuantizationA compression technique that reduces model size by representing weights using 8-bit floating-point numbers instead of higher precision, making it faster and more memory-efficient.Full-PrecisionA model using standard 32-bit floating-point numbers to represent weights, providing maximum accuracy but requiring more memory.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.QuantizationReducing a model's numerical precision (e.g., from 16-bit to 4-bit) to shrink memory usage and speed up inference.TokensThe basic units of text that a language model processes, typically representing words or word fragments.

Capabilities

Capabilities

Use Case Fit

Glossary