Lepton AI
Build and deploy high-performance AI applications at scale with zero infrastructure management.
The world's most performant AI execution engine and platform for heterogeneous compute.
Modular MAX (Modular Accelerated Xecution) is a revolutionary AI infrastructure platform designed to solve the fragmentation of the AI hardware and software stack. At its core, MAX provides a unified graph compiler and execution engine that enables developers to deploy AI models across CPUs, GPUs, and NPUs from diverse vendors (Intel, NVIDIA, AMD, Apple, ARM) with near-native performance. Integrated seamlessly with the Mojo programming language, MAX allows for the creation of custom high-performance kernels without the complexity of CUDA or C++. Its architecture leverages advanced graph optimizations, automatic quantization, and kernel fusion to significantly reduce latency and operational costs. For 2026, MAX is positioned as the primary competitor to hardware-locked SDKs like NVIDIA's TensorRT, offering a 'write once, run anywhere' paradigm that is critical for enterprise multi-cloud and edge strategies. It bridges the gap between the ease of Python and the performance of hardware-level systems, making it the infrastructure of choice for large-scale LLM deployments and real-time edge intelligence.
Dynamically partitions and executes model graphs across different hardware backends (CPU/GPU) in a single pipeline.
Build and deploy high-performance AI applications at scale with zero infrastructure management.
The search foundation for multimodal AI and RAG applications.
Accelerating the journey from frontier AI research to hardware-optimized production scale.
The Enterprise-Grade RAG Pipeline for Seamless Unstructured Data Synchronization.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Allows for the fusion of custom Mojo code directly into the inference graph at the compiler level.
Seamlessly imports and utilizes existing Python libraries like NumPy within the high-performance MAX environment.
Automated Mixed Precision logic that converts FP32 weights to FP16, INT8, or FP8 without significant accuracy loss.
Optimized implementations of FlashAttention-2 and 3 natively built in Mojo for LLM workloads.
Handles variable input dimensions without requiring graph recompilation for every new input size.
A customized memory allocator that minimizes fragmentation and maximizes cache hits for large model weights.
Python-based LLM serving is often too slow and expensive for real-time chat.
Registry Updated:2/7/2026
Running complex YOLO models on ARM CPUs with high latency.
High cloud bills due to GPU dependency for simple models.