Mindbeam
Litespark is a high-performance LLM framework that accelerates pre-training and inference while improving GPU and CPU efficiency. It delivers up to 6x faster training and reduces energy consumption by up to 86%, integrating seamlessly with PyTorch and AWS. The framework also includes Litespark-Inference, which enables efficient execution of large models on standard CPUs without specialized accelerators.
- Artificial Intelligence
- AI Agents
- Developer Tools
- Software Only
Funding
Founders
Product
Problem
Large language model pre-training and inference are computationally intensive, requiring extensive GPU clusters that drive up infrastructure costs and energy consumption. Many organizations face prohibitive expenses and environmental impacts when scaling model training, while also struggling with latency and resource constraints during inference on standard hardware.
Solution
Litespark provides a high-performance LLM framework that accelerates training and inference while improving GPU efficiency. The framework integrates seamlessly with existing PyTorch and NVIDIA ecosystems, requiring zero code changes, and delivers benchmark-proven performance gains including up to 6x faster training and up to 86% energy reduction. Litespark-Inference extends optimization to CPU platforms, enabling efficient execution of large models like a 2-billion parameter BitNet model in system RAM with dramatically higher throughput and near-instant response times. The solution fits directly into existing ML pipelines, supporting both training and serving workloads without disruption.
Target Audience
Primary customers are AI research teams, machine learning engineers, and enterprises that train or deploy large language models and need to reduce infrastructure costs, energy usage, and inference latency while maintaining high performance.
Features
- Up to 6x faster LLM training throughput compared to baseline frameworks
- Up to 86% reduction in energy consumption per training run
- Seamless integration with PyTorch and native NVIDIA support requiring zero code changes
- Litespark-Inference enables high-performance execution on standard CPUs, including 2x faster performance on Apple Silicon M5, AVX-512 platforms, and Intel Core Ultra
- Optimized for 256–512 GPU clusters, reducing MWh consumption and CO₂ emissions
- Available through AWS Marketplace for straightforward deployment