Systems16 articles

Systems

Articles

PagedAttention: Mathematical Foundations, Virtual Memory Block Allocation, Non-Contiguous KV-Cache Management, and Prefix Sharing Dynamics
PagedAttention is an attention algorithm and memory management architecture designed to eliminate memory waste and fragmentation in autoregressive Large Language Model (LLM) serving. Introduced by researchers at UC Berkeley and published at SOSP 2023 alongside the vLLM serving engine, PagedAttention adapts the classic operating system principles of virtual memory, paging, and copy-on-write to the management of dynamic Key-Value (KV) caches in GPU High Bandwidth Memory (HBM). During autoregressi
August 28, 20261 min
PagedAttention and Virtual Memory Management: Mathematical Foundations of Non-Contiguous KV-Cache Allocation, Dynamic Block Translation, and Copy-on-Write Forking Mechanics
PagedAttention and Virtual Memory Management: Mathematical Foundations of Non-Contiguous KV-Cache Allocation, Dynamic Block Translation, and Copy-on-Write Forking Mechanics In autoregressive transformer serving, the generation phase is fundamentally constrained by GPU memory capacity and memory bandwidth rather than raw compute throughput. While the initial prefill phase processes the prompt in parallel with high arithmetic intensity, the subsequent token-by-token decoding phase computes attent
August 27, 20261 min
Hierarchical KV Cache Offloading in Production LLM Serving: Host RAM, Local NVMe, Remote Storage, and LMCache Architecture
Hierarchical KV Cache Offloading in Production LLM Serving: Host RAM, Local NVMe, Remote Storage, and LMCache Architecture High-concurrency large language model (LLM) serving faces an acute memory capacity bottleneck. While modern GPUs deliver high floating-point compute throughput, High-Bandwidth Memory (HBM) capacity remains severely constrained. In workloads involving multi-turn conversations, agentic coding loops, and long-document retrieval-augmented generation (RAG), Key-Value (KV) cache
August 26, 20261 min
PagedAttention: Mathematical Foundations, Virtual Memory Block Allocation, and Zero-Waste KV Cache Serving
PagedAttention: Mathematical Foundations, Virtual Memory Block Allocation, and Zero-Waste KV Cache Serving In autoregressive transformer inference, serving throughput is bounded primarily by high-bandwidth memory (HBM) capacity and memory bandwidth rather than raw floating-point operations. As large language models generate tokens sequentially, each newly sampled token requires caching the key and value projection vectors across all preceding tokens in the sequence. In legacy serving engines, t
August 25, 20261 min
Test-Time Compute and Inference Search in Production: Comparing Best-of-N, Monte Carlo Tree Search, PRM-Guided Beam Search, and Dynamic Budget Allocation Architecture, Token Economics, and Serving Latency
In standard production Large Language Model (LLM) deployments, generation compute is fixed: a prompt is processed during prefill, and the model generates an answer via greedy autoregressive decoding or low-temperature sampling. Under this paradigm, accuracy on complex reasoning, mathematical derivation, theorem proving, and code synthesis is bounded by the model parameter count and training data distributions. Recent empirical work demonstrates that scaling inference-time compute can achieve pe
August 25, 20261 min
Multi-LoRA Serving in Production: Comparing S-LoRA, Punica, LoRAX, and vLLM Multi-LoRA Architecture, Batched SGMV Kernels, Paged Adapter Memory, and Co-Location Economics
Fine-tuning large language models on domain-specific corpora, proprietary workflows, and per-tenant datasets has become a standard enterprise practice. Deploying hundreds or thousands of distinct task-specific models as full weight replicas creates unsustainable infrastructure costs. A 70-billion-parameter base model in 16-bit precision requires approximately 140 GB of high-bandwidth memory (HBM) across two to four high-end GPUs. Serving 500 specialized models as isolated instances would require
August 25, 20261 min
Speculative Decoding in Production Serving: Comparing Small Draft Models, Medusa, EAGLE-2, and Lookahead Decoding Architecture, Verification Tree Overhead, and Throughput Economics
Large language model inference during autoregressive decoding is structurally memory-bandwidth bound. During generation, each forward pass loads the model weight matrices (tens to hundreds of gigabytes) from High-Bandwidth Memory (HBM) into on-chip SRAM to produce a single token. Because the arithmetic intensity is close to zero, modern accelerators like the NVIDIA H100 and B200 spend the vast majority of their compute cycles stalled on memory bus transfers rather than executing matrix multiplic
August 25, 20261 min
Continuous LLM Performance Profiling in Production: Roofline Models, Model FLOPs Utilization, Model Bandwidth Utilization, and Hardware Bottleneck Diagnostics
Evaluating the runtime performance of large language model serving infrastructures requires looking beyond raw GPU metrics. Standard operating system utilities such as nvidia-smi report high GPU utilization percentages whenever compute cores or memory controllers are active, masking critical inefficiencies in memory access, communication, and kernel scheduling. A serving node running single-stream autoregressive decoding can report 100% GPU utilization while operating at less than 2% of the hard
August 24, 20261 min
Cross-Model KV Cache Transfer in Production: Architecture, Closed-Form Ridge Projections, and Cascaded Serving Economics
Modern enterprise LLM serving architectures frequently rely on multi-model pipelines to balance inference cost, generation latency, and output quality. In model routing cascades, lightweight 8B models triage incoming queries and escalate complex reasoning tasks to 70B or MoE models. In speculative decoding pipelines, smaller draft models propose token sequences verified by larger target models. In long-horizon AI agent swarms, sub-agents frequently switch between specialized models across multi-
August 24, 20261 min
Predictive KV Cache Warming in Production LLM Serving: Architecture, Session Prefetching, and TTFT Latency Shaving
Predictive KV Cache Warming in Production LLM Serving: Architecture, Session Prefetching, and TTFT Latency Shaving In long-context large language model (LLM) deployments, Time-to-First-Token (TTFT) represents the primary latency bottleneck. As input prompts scale from 8,000 tokens to 128,000 tokens in multi-turn coding agents, legal document analysis, and enterprise retrieval pipelines, prefill execution consumes between 85% and 95% of total GPU compute time per request. Standard optimization
August 23, 20261 min
Zero-Downtime Model Updates in Production LLM Serving: In-Place Weight Transfer, CUDA IPC vs. NCCL Syncing, and Traffic Draining Architectures
Zero-Downtime Model Updates in Production LLM Serving: In-Place Weight Transfer, CUDA IPC vs. NCCL Syncing, and Traffic Draining Architectures In high-throughput LLM serving infrastructure, updating model checkpoints presents a severe operational dilemma. Traditional microservice deployment patterns such as blue-green deployments or rolling pod restarts fail to scale economically when applied to multi-GPU LLM clusters. Tearing down a serving instance to load a newly fine-tuned checkpoint or pol
August 22, 20261 min
GPU Memory Profiling in Production LLM Serving: CUDA Allocator Internals, PyTorch Snapshots, and VRAM Optimization
In high-throughput large language model serving, memory is the primary constraint governing latency, batch concurrency, and context length. While model parameter footprints are static and easily calculated, runtime GPU memory (VRAM) dynamics are governed by low-level caching allocators, dynamic key-value (KV) cache allocation pools, transient activation spikes, and memory fragmentation. When an inference worker crashes with torch.cuda.OutOfMemoryError, default system diagnostics such as nvidia-
August 22, 20261 min
Speculative Tool Execution and Parallel Action Graphs in Production AI Agents: DAG Scheduling, Optimistic Concurrency, and Side-Effect Rollback
Autonomous AI agents operating in complex environments (such as software engineering, scientific discovery, and automated workflow orchestration) face an operational bottleneck: execution latency. While foundation models have become faster at token generation, the standard agent interaction loop remains strictly serial. A model reasons, outputs a single tool call, halts generation, waits for the external environment to return a result, appends the output to context, and resumes reasoning. In mu
August 20, 20261 min
Custom LLM Kernel Optimization in Production: Triton vs. CUDA C++ vs. torch.compile vs. CUTLASS
Serving large language models at scale requires extracting maximum performance from modern GPU architectures like NVIDIA Ampere, Hopper, and Blackwell. While early production deployments relied on standard PyTorch eager execution and standard cuBLAS calls, high-throughput serving systems such as vLLM, SGLang, and TensorRT-LLM depend on specialized fused GPU kernels to eliminate memory bandwidth bottlenecks and saturate Tensor Cores. Engineering teams face four primary paradigms for kernel devel
August 20, 20261 min
Dynamic KV Cache Eviction in Production: Architecture, Sparsity Policies, and Serving Trade-Offs
In long-context large language model serving, the key-value (KV) cache is the primary hardware bottleneck limiting concurrency and throughput. While model weights remain static during inference, KV cache memory scales linearly with sequence length, batch size, and layer count. For modern 70B parameter models utilizing Grouped-Query Attention (GQA), serving a 128,000-token context across a modest batch size of 4 requires over 80 GB of VRAM solely for KV states in 16-bit precision, exceeding the m
August 20, 20261 min
Speculative Decoding in Production: Architecture, Economics, and Serving Trade-Offs
Standard autoregressive large language model (LLM) generation produces one token per forward pass. Because modern inference architectures must read tens of gigabytes of model weights from high-bandwidth memory (HBM) into SRAM to process each solitary token at low batch sizes, generation is strictly memory-bandwidth bound rather than compute bound. Speculative decoding alters this hardware equation. By pairing a fast draft mechanism with parallel verification by the primary target model, inferen
August 17, 20261 min