LFM 40B is a 40-billion-parameter Liquid Foundation Model developed by Cerebras and running on Cerebras wafer-scale silicon, which enables inference speeds far exceeding what GPU-based systems typically deliver at this parameter scale. Liquid Foundation Models use a recurrent architecture — inspired by liquid neural networks — rather than standard Transformer attention, which gives them different computational characteristics and memory efficiency.
On Cerebras hardware, LFM 40B achieves very high token throughput, making it practical for latency-sensitive applications that usually require smaller models. It targets developers who want strong capability from a 40B-class model without the response-time penalty typical of that scale.
Key Features
Liquid Foundation Model architecture with recurrent state instead of full attention
Extremely fast inference on Cerebras wafer-scale silicon
40B parameter scale for strong general language understanding
High token throughput suitable for real-time applications
Memory-efficient generation compared to Transformer models of similar size
General-purpose text generation, analysis, and instruction following
Ideal Use Cases
Latency-sensitive chat applications requiring large-model quality
High-throughput document processing and summarization
Developer experimentation with non-Transformer architectures
Real-time analytics and automated report generation
Applications where LFM recurrent characteristics suit sequential data
Example Prompts for LFM 40B
Technical Specifications
| Provider | Cerebras |
| Category | Text |
| Modality | Text -> Text |
Frequently Asked Questions
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