gpustack/gpustack

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.

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