llm-d/llm-d

Features

• Disaggregated Inference Orchestration - Provides a distributed architecture that separates prefill and decode phases across specialized worker pools to maximize throughput.
• Disaggregated Inference - Implements a disaggregated architecture that separates prefill and decode phases across specialized hardware nodes to maximize throughput.
• Disaggregated Throughput Optimizations - Provides a disaggregated prefill and decode topology specifically designed to maximize throughput for batch-intensive LLM workloads.
• Hardware-Agnostic Accelerators - Utilizes a hardware-agnostic control plane to manage various accelerators and enable low-latency inter-chip communication.
• Inference Gateways - Functions as an OpenAI-compatible API gateway for request routing and traffic distribution.
• KV Cache Management - Optimizes prefix reuse through cache-aware routing and tiered offloading of memory blocks.
• KV-Cache-Aware Request Routing - Employs a weighted scoring system to route requests to replicas holding the necessary KV cache context.
• Prefix Cache Reuse - Maximizes cache hits by tracking state across servers and offloading excess data to secondary storage.
• Inference Deployment Orchestrators - Provides a control plane for deploying model replicas and managing hardware accelerators with SLO-aware scaling.
• Agnostic Control Planes - Provides a hardware-agnostic control plane to manage diverse accelerators and ensure low-latency communication across different chips.
• OpenAI-Compatible Model Servers - Provides an OpenAI-compatible API gateway for drop-in integration of large language model inference services.
• Prefix-Aware Routing - Routes incoming traffic to specific model replicas based on cached prompt prefixes to minimize redundant computation.
• LLM KV Cache Stores - Optimizes memory use and prefix reuse by offloading KV caches to CPU or shared storage and routing via affinity.
• Inference Batching - Manages large volumes of offline inference requests through queuing and flow control to maximize hardware utilization.
• LLM Replica Autoscaling - Automatically adjusts the number of model replicas based on queue depth and memory pressure to maintain latency targets.
• Model Deployments - Deploys and manages multiple instances of model replicas on hardware accelerators for scalable request processing.
• Traffic Load Balancers - Provides a proxy-based distribution system to balance incoming model traffic across multiple server replicas.
• Cache-Aware Load Balancing - Distributes inference requests across server pools using real-time metrics, predicted latency, and cache locality.
• Memory Offloading Frameworks - Implements tiered cache offloading by moving memory blocks between GPU memory, host RAM, and shared storage for long-context workloads.
• Latency Reduction Techniques - Implements a performance suite using speculative decoding and fused kernels to minimize token generation latency.
• Activation and KV Cache Offloaders - Swaps memory blocks to host RAM to prevent context drops during long-context workloads.
• Shared Storage Offloading - Moves memory blocks to a shared file system to decouple cache capacity from local GPU memory.
• Cache-Aware Schedulers - Implements a scheduling system that leverages shared context and prefix caching to optimize throughput for multi-tenant environments.
• KV-Cache Transport Optimizations - Reduces tail latency during context migration using adaptive congestion control and specialized libraries.
• Asynchronous Batching Execution - Executes latency-tolerant requests from message queues to maximize hardware utilization alongside interactive traffic.
• Disaggregated Phase Scaling - Scales large models by separating prefill and decode stages using expert parallelism.
• Adapter-Aware Routing - Routes traffic to specific nodes based on the location of loaded LoRA adapters to avoid redundant execution.
• Offline Batch Inference - Provides a mechanism to run large-scale asynchronous inference via compatible APIs to maximize total hardware utilization.
• SLO-Driven Predictive Routing - Scores endpoints by predicting time-to-first-token and inter-token latency to ensure requests meet defined performance targets.
• Disaggregated Serving - Utilizes a disaggregated architecture to split prefill and decode stages across fast accelerator interconnects.
• Saturation-Based Scaling - Reactive optimizer that adjusts the number of replicas by monitoring queue length to prevent request overflow.
• Asynchronous Gating - Implements flow-control gating to execute offline batch requests using spare hardware capacity.
• Pressure-Based Scaling - Adjusts the number of active replicas by monitoring cache saturation and queue length to maintain system stability.
• Capacity Scaling - Adjusts compute resources using native metrics and traffic routing to minimize costs while meeting latency targets.
• Queue-Based Scaling Triggers - Handles traffic spikes using intelligent queuing and autoscales capacity based on real-time load metrics.
• Scheduler High Availability - Ensures redundancy and accurate cache tracking through a high-availability scheduler that discovers server pods.
• SLO-Aware Scaling - Implements production stability through SLO-aware autoscaling and flow control for multi-tenant environments.
• Inference Job Management - Provides capabilities to queue offline requests and dispatch them using flow-control gating for high-volume workloads.
• Pod Autoscaling - Autoscales container replicas by monitoring queue depth and memory saturation to maintain latency targets.
• LLM Production Infrastructure - Sets up and manages high-availability infrastructure specifically designed for production LLM serving.
• Cold-Start Load Balancing - Provides a specialized distribution strategy for cache-cold requests to prevent hotspots and oscillations in the request queue.
• Load Balancing Metrics - Distributes requests across replicas using real-time queue depth and memory saturation metrics.
• Predictive Latency Routing - Implements a routing system that predicts time-to-first-token and inter-token latency to enforce performance targets.
• SLO-Based Server Placement - Ships a request packing system that compares predicted latency against defined SLO targets to optimize server placement.
• Inference Batching Schedulers - Provides a request queuing system that balances interactive traffic with asynchronous offline workloads.

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