LiquidAI: LFM2-24B-A2B
Fast MoE Inference Engine
Scale Agents Efficiently
Hybrid MoE
24B Params 2.3B Active
Activates 2.3B params per token in 40-layer A2B architecture with 30 conv blocks.
Low Memory
Fits 32GB RAM
Deploys on laptops, edge devices, and H100s for LiquidAI: LFM2-24B-A2B API workflows.
High Throughput
26K Tokens Second
Handles 1024 concurrent requests at 32K context for liquidai lfm2 24b a2b pipelines.
Examples
See what LiquidAI: LFM2-24B-A2B can create
Copy any prompt below and try it yourself in the playground.
Math Proof
“Prove the Pythagorean theorem step-by-step using geometric arguments and formal logic. Include diagrams in ASCII art and verify with coordinates.”
Code Debugger
“Analyze this Python function for bugs: def factorial(n): if n == 0: return 1 else: return n * factorial(n-1). Fix recursion depth issues and optimize for large n.”
Agent Workflow
“Plan a multi-step research task: query database for sales data, analyze trends with stats, generate report in JSON, and suggest actions.”
RAG Summary
“Summarize key insights from these documents on climate models, extract trends, and output structured JSON with citations for 32K context.”
For Developers
A few lines of code.
Agents. Two Lines.
ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.
- Serverless: scales to zero, scales to millions
- Pay per token, no minimums
- Python and JavaScript SDKs, plus REST API
import requestsresponse = requests.post("https://modelslab.com/api/v7/llm/chat/completions",json={"key": "YOUR_API_KEY","prompt": "","model_id": ""})print(response.json())
Ready to create?
Start generating with LiquidAI: LFM2-24B-A2B on ModelsLab.