Building Next Generation
AI Infrastructure
Introducing Litespark:
Ultra Fast LLM Pretraining Framework
Today's AI infrastructure performance is limited by compute capabilities and is further hindered by algorithmic inefficiencies. This makes production of larger models prohibitively expensive while inhibiting the implementation of advanced AI technologies.
Litespark is a language model framework which utilizes advanced algorithms to speedup training and inference workloads for generative AI applications.
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Reduce Pre-Training Time From
Months To Days
H100:
PyTorch:
PyTorch with Litespark:
0
10000
20000
30000
40000
50000
70000
Throughput (Tokens/sec/GPU)
A100:
PyTorch:
PyTorch with Litespark:
0
10000
20000
30000
40000
Throughput (Tokens/sec/GPU)
Experience Blazing Fast Performance
with Zero Code Changes
Leverage accelerated compute on existing hardware without changing codebase or model framework
Compatible with Industry Standard
ML Frameworks
Cost and Energy Savings
Mindbeam's Litespark framework saves up to 81% on energy consumption when training a 30B model on 32 H200 GPUs. This lowers the carbon footprint of generative AI models that are trained using Litespark.
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