Systems-Level Inference Optimization #

Overview #

While Sections 1 and 2 focused on architectural foundations and algorithmic optimizations, this section addresses systems-level optimizations that orchestrate computation, manage resources, and coordinate execution across the entire inference pipeline. These techniques operate at the level of runtime systems, schedulers, memory managers, and execution engines, bridging the gap between high-level model architectures and low-level hardware execution.

The challenge of systems-level optimization lies in managing the inherent tensions and tradeoffs that emerge when serving large language models at scale. Real-world deployment must handle highly variable workloads: requests arrive with different prompt lengths, generate outputs of unpredictable sizes, and have varying latency requirements. The system must efficiently utilize expensive hardware resources while maintaining responsiveness and fairness across concurrent users. It must balance competing objectives: maximizing throughput to reduce cost per request, minimizing latency for interactive applications, and ensuring predictable performance under diverse load patterns.

Traditional serving systems designed for stateless, fixed-size workloads break down when faced with the unique characteristics of autoregressive generation. Standard batching strategies assume uniform input sizes and synchronous execution, but LLM requests vary dramatically in both input length and generation length. Memory allocation schemes that preallocate fixed buffers waste resources when sequences are short and fail when sequences exceed expectations. Execution models that treat each request as an atomic unit cannot interleave work effectively, leading to poor resource utilization and unpredictable latency. And the fundamental tension between compute-intensive prefill phases and memory-bandwidth-bound decoding phases creates scheduling challenges that simple round-robin or priority-based approaches cannot resolve.

Systems-level optimizations address these challenges by rethinking how computation is organized, how memory is managed, and how work is scheduled. Rather than forcing LLM workloads to fit into existing serving paradigms, these techniques adapt system design to match the mathematical and computational structure of transformer inference. They exploit opportunities for parallelism and reuse that emerge from the causal, sequential nature of autoregressive generation. They manage memory hierarchies explicitly, recognizing that the bottleneck often lies in data movement rather than computation. And they make scheduling decisions at fine-grained temporal scales, enabling dynamic resource allocation that adapts to workload characteristics in real-time.

The techniques covered in this section span multiple layers of the system stack. Some focus on request scheduling and batching, enabling efficient coordination of multiple concurrent requests with varying characteristics. Others target memory management, organizing storage hierarchies to minimize fragmentation and maximize utilization of limited device memory. Still others optimize execution patterns, reducing overhead through graph compilation, kernel fusion, and persistent execution models. What unifies these approaches is their systems perspective: they improve performance by changing how work is organized and executed, rather than changing what computation is performed.

Together, these systems-level optimizations form the critical infrastructure layer that makes algorithmic and architectural improvements practically deployable. Understanding these techniques is essential for practitioners building production inference systems, as they often determine whether theoretical performance gains translate into real-world improvements in latency, throughput, and cost efficiency.

Contents of this section #

3.1 Continuous Batching #

Introduces continuous batching as a dynamic scheduling technique that rebuilds batches at every decoding step, allowing requests to join and leave asynchronously. Explains how this addresses the limitations of static batching for variable-length LLM requests, improving GPU utilization and reducing latency for shorter requests.

3.2 Paged Attention #

Describes Paged Attention, a memory management technique that organizes KV cache into fixed-size blocks allocated on demand, similar to OS paging. Covers how this reduces fragmentation, enables higher concurrency, and unlocks the full potential of continuous batching by making memory usage scale with actual sequence length rather than maximum length.

3.3 Chunked Prefill #

Explains chunked prefill, which breaks long prefill operations into smaller, interleavable chunks that can be scheduled alongside decoding requests. Analyzes the tradeoffs between prefill efficiency and decode responsiveness, and how chunking improves system stability and fairness under variable workload patterns.

3.4 Disaggregated Inference #

Explores disaggregated inference architectures that separate prefill and decode execution onto different devices or hardware tiers, allowing each phase to be optimized independently and mitigating the fundamental scheduling tension between compute-bound and memory-bound operations.

3.5 Multi-LoRA Serving #

Presents multi-LoRA serving as a technique for efficiently serving multiple fine-tuned model variants by sharing base model weights and dynamically loading lightweight adapter weights. Covers static and dynamic serving strategies, memory management for adapters, and framework support for multi-adapter deployments.

3.6 Compute Graph Optimization #

Introduces compute graph optimization techniques including piecewise CUDA graphs and persistent kernels. Explains how these approaches reduce kernel launch overhead, enable better compiler optimizations, and improve execution efficiency through graph capture and megakernel execution models.

3.7 Kernel Fusion #

Explores kernel fusion as a critical optimization for memory-bound operations, combining multiple operations into single kernels to minimize memory traffic. Covers operator fusion, tiling strategies, and mathematical fusion techniques like Flash Attention that enable efficient attention computation through online softmax algorithms.