Litebite provides AI-driven solutions for optimizing digital content delivery and performance. The platform focuses on intelligent compression and resource management to enhance website speed and user experience. This results in lower bandwidth costs and improved SEO rankings for web properties.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises deploying large language models (LLMs) face high computational overhead, elevated latency, and substantial cloud‑compute costs, which hinder the scalability and responsiveness of generative AI applications.
Solution
Litebite AI offers a suite of machine‑learning optimization models that streamline LLM inference pipelines. By applying techniques such as weight quantization, layer‑wise pruning, and hardware‑aware compilation, the platform cuts the number of FLOPs required per token and reduces memory bandwidth demands. The resulting inference engine delivers lower latency and higher throughput without sacrificing model accuracy. Litebite’s solution integrates via a lightweight SDK and REST API, allowing developers to replace existing inference back‑ends with minimal code changes. The platform also provides real‑time performance analytics, enabling operators to monitor cost savings and latency improvements across cloud or on‑premise deployments.
Target Audience
Primary customers are enterprise AI teams, SaaS providers, and cloud platform operators that run large‑scale generative AI workloads and need to reduce inference cost and latency.
Features
- Model‑level quantization and mixed‑precision support for major LLM architectures (e.g., GPT‑3, LLaMA, Claude)
- Automated layer pruning and knowledge‑distillation pipelines that preserve output quality
- Hardware‑aware compilation targeting GPUs, TPUs, and CPU accelerators with vendor‑specific kernels
- Plug‑and‑play SDK and RESTful API for seamless integration into existing inference services
- Real‑time profiling dashboard showing FLOP reduction, latency, and cost metrics per deployment
- Batch‑size adaptive scheduler that maximizes throughput under variable traffic loads
- Compatibility layer for popular serving frameworks such as TensorRT, ONNX Runtime, and vLLM