thu-pacman/chitu

Features

• LLM Serving Architectures - Provides a high-performance engineering architecture for deploying and serving large language models at scale across clusters.
• Cross-Hardware Model Inference - Enables the execution of large language models across diverse hardware, including NVIDIA GPUs, regional chipsets, and legacy systems.
• Distributed Model Execution - Distributes model inference workloads across multiple compute devices to increase processing speed and resource utilization.
• Heterogeneous Hardware Scaling - Increases computing capacity by running inference across mixed hardware clusters and non-NVIDIA chipsets.
• Inference Scaling - Scales model inference capacity across configurations ranging from single devices to large-scale production clusters.
• Inference Deployment Orchestrators - Orchestrates the deployment and scaling of large language models across heterogeneous hardware clusters.
• Heterogeneous Orchestrators - Coordinates the distribution of LLM workloads across varying CPU and GPU architectures to optimize resource utilization.
• Hardware-Agnostic Deployment - Provides a runtime that abstracts chip-specific instructions to allow models to run across GPUs, CPUs, and regional accelerators.
• Distributed Inference Orchestrators - Implements a system for distributing model weights and computation tasks across multiple devices and network nodes to scale processing.
• High-Throughput Model Serving - Employs architectures designed to handle large volumes of concurrent LLM inference requests with stable performance.
• Hardware Kernel Switching - Switches between hardware-specific kernels based on the target compute device to maintain optimal inference performance.
• Model Inference Deployment - Deploys large language models into production environments across diverse hardware including GPUs and CPUs.
• Traffic Management - Controls request throughput and connection concurrency to ensure stable operation in high-demand production environments.
• Model Tensor Mapping - Provides specialized memory mapping to allocate model tensors across diverse hardware architectures for optimized inference performance.
• Cluster Load Balancing - Balances computational loads across available cluster nodes to ensure maximum hardware utilization during model inference.
• Concurrent Request Limits - Manages simultaneous inference requests through a structured buffer to maintain system stability during traffic spikes.

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