LFM2-24B is a 24-billion parameter Liquid Foundation Model (LFM) developed and served on Cerebras' wafer-scale chip hardware. Liquid Foundation Models use a recurrent-style architecture that differs from standard transformers, enabling efficient inference without the quadratic attention scaling cost — an especially good fit for Cerebras' unique silicon.
Running on Cerebras hardware, LFM2-24B achieves exceptionally high token throughput compared to GPU-hosted equivalents of similar size, making it appealing for latency-sensitive pipelines. It handles general instruction-following, summarization, and conversational tasks, and its architecture gives it advantages on long sequences without the memory overhead traditional attention requires.
Key Features
High-throughput inference powered by Cerebras wafer-scale hardware
Recurrent LFM architecture with efficient long-sequence handling
General instruction-following and multi-turn conversation
Summarization and document processing at speed
Low-latency responses suitable for real-time applications
Competitive performance-per-compute-unit for its parameter class
Ideal Use Cases
Real-time chat and assistant applications requiring low latency
Batch document summarization where throughput matters
Backend inference pipelines that need predictable token-per-second rates
Enterprises exploring alternative architectures beyond standard transformers
Research and development on non-transformer language model behavior
Example Prompts for LFM2-24B
Technical Specifications
| Provider | Cerebras |
| Category | Text |
| Modality | Text -> Text |
Frequently Asked Questions
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