gpustack/gpustack
Gpustack
gpustack este o platformă de gestionare a clusterelor GPU și un orchestrator de inferență LLM. Acesta funcționează ca un sistem centralizat pentru pooling-ul și orchestrarea unităților de procesare grafică pe servere locale și medii cloud, servind drept manager de calcul eterogen pentru diverse configurații hardware și software.
Sistemul oferă un gateway securizat de implementare a modelelor AI care servește modelele ca servicii scalabile folosind autentificarea bazată pe chei. Include un scheduler de resurse GPU care echilibrează sarcinile de lucru pe acceleratoare și coordonează mai multe motoare de inferență pentru a mapa modele AI specifice pe hardware compatibil.
Platforma acoperă orchestrarea cuprinzătoare a clusterelor, inclusiv recuperarea automată în caz de eșec, monitorizarea resurselor în timp real și scalarea inferenței distribuite. Încorporează optimizarea performanței prin cuantizare și decodare speculativă pentru a maximiza throughput-ul și a reduce latența.
Configurațiile sistemului și starea clusterului sunt menținute prin persistența stării într-o bază de date relațională externă.
Features
- Cluster Orchestrators - Provides a centralized management plane for orchestrating distributed workloads and resource allocation across multi-node GPU clusters.
- GPU Cluster Management Platforms - Provides a centralized platform for pooling and orchestrating GPUs across local servers and cloud environments.
- Distributed Inference Scaling - Spreads large AI model workloads across multiple hardware nodes to increase throughput and memory capacity.
- Multi-GPU Parallelism Strategies - Splits large AI workloads across multiple GPUs using tensor or data parallelism strategies.
- Inference Backends - Provides a hardware-agnostic abstraction layer for executing AI models across diverse computing environments and engines.
- Custom Resource GPU Scheduling - Implements a custom resource scheduler to distribute inference tasks across hardware accelerators for optimized throughput.
- Multi-Backend Inference Orchestration - Coordinates request routing and mapping across multiple different inference engine backends within a single system.
- GPU Resource Orchestrators - Dynamically manages and allocates GPU hardware across shared AI workloads in a centralized cluster.
- Model Workload Balancers - Distributes incoming requests across available hardware resources to maximize efficiency and prevent device overloading.
- Unified Control Planes - Provides a unified control plane for provisioning and synchronizing GPU compute resources across diverse environments and cloud providers.
- Shared Compute Pools - Aggregates GPUs from local and cloud sources into a shared compute pool for flexible execution.
- Heterogeneous Compute Coordination - Provides a control plane to coordinate diverse GPU hardware and software configurations within a shared environment.
- LLM Inference Orchestration - Coordinates and deploys inference engines like vLLM and SGLang to serve AI models.
- GPU Resource Pooling - Aggregates GPUs from local servers and cloud providers into a single shared execution environment.
- AI Gateways - Provides a unified interface and secure gateway for managing and routing AI model requests.
- Latency Reduction Techniques - Utilizes speculative decoding and specialized kernels to minimize the time required for model response generation.
- High Throughput Inference - Applies quantization techniques to model weights and activations to maximize the number of requests processed per second.
- Throughput Optimizers - Optimizes token throughput and reduces latency through quantization, speculative decoding, and batch size adjustments.
- Inference Performance Optimization - Optimizes latency and throughput using pre-tuned engine settings, speculative decoding, and extended cache systems.
- Model Serving APIs - Exposes local AI models as network-accessible services via secure, scalable API endpoints.
- AI Model Production Deployment - Downloads models from catalogs and maps them to the most efficient hardware accelerators and engines for production.
- Model Inference Deployment - Maps specific inference engines to models and hardware requirements to maximize execution speed.
- High Availability Clustering - Coordinates server nodes and automates recovery to ensure high availability across all hardware resources.
- Inference Workload Balancing - Distributes incoming API requests across available GPUs to prevent device overloading and maximize throughput.
- Inference Engine Tunings - Adjusts batch sizes, scheduling policies, and cache configurations based on request patterns to improve output speed.
- Granular Access Controls - Applies granular permissions to secure model endpoints and sensitive system configurations.
- Identity and Access Management - Authenticates users and enforces access policies to protect GPU infrastructure from unauthorized access.
- Backend Failure Recoveries - Automatically detects and recovers from hardware or software faults to maintain continuous service availability.
- Cluster Health Monitoring - Tracks resource utilization and performance metrics via dashboards to ensure cluster stability.
- GPU Resource Monitoring - Tracks real-time GPU utilization and memory usage to ensure efficient hardware consumption.
- Application Development - GPU cluster manager for running LLMs.
- Inference Engines - Scalable deployment solution for AI models on GPU clusters.
- Computation and Optimization - GPU cluster manager for running AI models.
