Posts Tagged ‘Optimization’
[VoxxedDaysBucharest2026] Optimizing LLM Inference on Kubernetes: Abdel Sghiouar on Practical Techniques for the Rest of Us
Lecturer
Abdel Sghiouar is a Developer Advocate at Google Cloud with deep expertise in cloud-native technologies, Kubernetes orchestration, and AI/ML workload optimization. Drawing from a robust background in infrastructure engineering and open source contributions, Abdel helps organizations design, deploy, and tune complex AI applications for production environments across diverse infrastructures.
Abstract
While major cloud providers and hyperscalers leverage virtually unlimited computational resources, the majority of organizations face significant constraints when operationalizing Large Language Models. Abdel Sghiouar presents a comprehensive set of practical strategies for optimizing LLM inference workloads on Kubernetes. The session systematically addresses container and model optimization techniques, accelerator management, data persistence and storage considerations, networking and intelligent load balancing, and advanced observability practices. Emphasis is placed on open-source tools and architectural patterns that deliver meaningful cost-performance improvements adaptable to on-premises, hybrid, and public cloud deployments.
Understanding LLM Inference Characteristics and Challenges
Large Language Models continue their rapid evolution in both scale and sophistication. Architectural innovations such as mixture-of-experts (MoE) enable dynamic activation of specialized sub-networks, while multi-modal capabilities process diverse inputs including text, images, audio, and video. Expanded context windows support richer interactions but demand substantial memory resources.
Inference execution comprises two primary phases with contrasting characteristics: the prefill stage (encoding input tokens, predominantly compute-bound) and the decode stage (token generation, typically memory-bound). KV (key-value) caching optimizes conversational flows by preserving intermediate states, avoiding redundant prefill computations for subsequent messages.
Deployment topologies vary considerably. Single-host single-accelerator setups predominate for local development and experimentation (e.g., using Ollama). Single-host multi-accelerator configurations require model sharding across GPUs within one machine. Multi-host distributed deployments introduce complex requirements for high-bandwidth, low-latency interconnects to maintain coherent context across nodes. Each topology presents distinct challenges regarding scalability, fault tolerance, and operational complexity.
Container, Model, and Storage Optimizations
Inference serving runtimes and model artifacts generate exceptionally large container images, frequently exceeding several gigabytes prior to incorporating weights. Conventional optimization strategies like multi-stage builds or native compilation (e.g., GraalVM) prove inadequate for these workloads.
Distributed caching solutions such as Spiegel provide cluster-wide image and model artifact caching, substantially reducing repeated pulls from external registries. Kubernetes-native features enabling containers as volumes allow separate packaging of models, which can then be mounted efficiently onto serving runtimes. When combined with caching layers, these approaches dramatically accelerate cold starts.
Quantization techniques offer another lever, reducing numerical precision (e.g., FP16 to INT8 or lower) to decrease memory footprints while preserving sufficient accuracy for many applications. Careful selection of quantization levels based on task sensitivity balances performance and quality.
Accelerator Management and Dynamic Resource Allocation
Kubernetes has supported GPU scheduling through device plugins for several years. However, static device configurations struggle with real-world constraints including accelerator scarcity and heterogeneous hardware fleets.
Dynamic Resource Allocation, matured in recent Kubernetes versions, introduces flexible resource claiming based on abstract characteristics rather than rigid device specifications (e.g., requesting “NVIDIA GPU with minimum 30GB memory and specific core count”). This enables more efficient scheduling across mixed clusters and better utilization rates.
Integration with cluster autoscalers allows on-demand provisioning, addressing both availability gaps and cost optimization by scaling resources precisely to workload demands. Platform operators describe device inventories; application teams specify requirements, with the scheduler performing intelligent matching.
Networking, Load Balancing, and Observability Considerations
LLM traffic profiles differ markedly from conventional web workloads. Requests exhibit high variability in size and computational intensity (simple text queries versus multi-modal inputs), while responses frequently involve streaming token generation. Standard round-robin load balancing produces inefficient distributions, with certain backends becoming overloaded while others remain underutilized.
The Kubernetes Gateway API, augmented with custom endpoint selection logic, supports sophisticated routing decisions based on request attributes extracted from bodies (model identifier, input modality, streaming requirements) combined with real-time backend telemetry. This facilitates intelligent traffic steering, prioritization of business-critical workloads, and maintenance of sticky sessions necessary for coherent streaming interactions.
Comprehensive observability must encompass prefill and decode phase latencies, KV cache hit rates, token generation throughput, GPU utilization, and end-to-end request metrics. Integration with Prometheus, Grafana, and specialized LLM monitoring solutions provides actionable insights for capacity planning and bottleneck identification.
Practical Patterns and the LLM-D Project
The LLM-D initiative, hosted under the Linux Foundation with contributions from Google, IBM, NVIDIA, and additional partners, aggregates architectural patterns, performance benchmarks, and reference implementations for production-grade inference. Key elements include optimized prefill/decode separation, advanced routing logic often leveraging engines like vLLM, and comprehensive guidance for multi-node deployments.
A holistic, layered optimization strategy proves most effective: infrastructure-level improvements (caching, persistent volumes), platform capabilities (dynamic scheduling, intelligent networking), and application-level choices (model quantization, serving engine selection). Organizations without hyperscale resources can still achieve competitive efficiency and scalability through disciplined application of these patterns.
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[VoxxedDaysBucharest2026] Mastering Performance Optimization in Java: Roberto Cortez on Writing Efficient Code
Lecturer
Roberto Cortez is a Senior Software Engineer at Red Hat and a prominent contributor to the Quarkus project, with particular expertise in configuration systems, startup performance, and runtime efficiency optimizations. With years of experience in Java development and cloud-native technologies, Roberto focuses on making Java applications faster, more resource-efficient, and better suited for modern deployment environments.
Abstract
In many development projects, functional delivery takes precedence while performance considerations are deferred until bottlenecks become apparent. Roberto Cortez challenges this approach through a detailed examination of efficient Java coding practices. Using real-world examples from Quarkus development, he demonstrates essential tools including Async Profiler for visualization, JMH for benchmarking, and Java Flight Recorder. Through iterative optimization of concrete code examples, he illustrates the importance of measurement, analysis, and continuous refinement.
The Perils of Assumption and the Imperative of Measurement
Roberto draws from his extensive work on Quarkus configuration loading to highlight how seemingly minor implementation details can have outsized performance impacts. He gently critiques the common misinterpretation of Donald Knuth’s famous quote about premature optimization, clarifying that while not every piece of code requires micro-optimization, developers must remain vigilant about critical execution paths that significantly affect user experience or resource consumption.
A central example involves a simple string prefixing operation implemented using Java Streams. While the code appears clean and idiomatic, profiling reveals substantial hidden costs in object allocations and temporary structures. This serves as a powerful reminder that intuition alone is insufficient — empirical measurement must guide optimization decisions.
Profiling with Async Profiler and Flame Graphs
Async Profiler emerges as a key tool due to its low overhead and rich visualization capabilities. When attached to a running Quarkus endpoint responsible for generating lists of names, the resulting flame graphs clearly highlight hotspots in StringBuilder usage and intermediate object creation. These visualizations prove invaluable for understanding complex runtime behavior where application code often represents only a small fraction of total execution time due to framework, JVM, and library interactions.
Roberto demonstrates practical usage patterns and interpretation techniques that enable developers to quickly identify and address performance bottlenecks.
Benchmarking with JMH for Rigorous Comparison
For precise, statistically sound measurements, Roberto turns to the Java Microbenchmark Harness (JMH). He presents detailed benchmarks comparing multiple implementations of the prefixing task: traditional Streams, parallel Streams, manual for-loops, and optimized versions reusing StringBuilder instances. Results across different Java versions (17, 21, and experimental 25) reveal how JVM improvements can render certain hand-optimizations obsolete or even counterproductive.
Additional demonstrations focus on environment variable resolution in Quarkus, where iterative refinements including custom equals and hashCode implementations yield substantial gains in both startup time and memory consumption.
Sustained Vigilance, Real-World Impact, and Lessons Learned
Performance optimization is portrayed as an ongoing discipline rather than a one-time activity. Roberto shares how optimizations introduced in Quarkus 3.5 required revisiting and partial reversion in version 3.6 due to upstream changes. The famous “One Billion Row Challenge” serves as an inspiring example of extreme creativity and technical depth in pursuit of performance.
Key takeaways include focusing optimization efforts on high-impact areas, balancing readability and maintainability concerns, and maintaining rigorous measurement practices throughout the development lifecycle. Developers are encouraged to cultivate a performance-aware mindset while avoiding premature or counterproductive optimizations.
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[DotJs2024] Becoming the Multi-armed Bandit
In the intricate ballet of software stewardship, where intuition waltzes with empiricism, resides the multi-armed bandit—a probabilistic oracle guiding choices amid uncertainty. Ben Halpern, co-founder of Forem and dev.to’s visionary steward, dissected this gem at dotJS 2024. A full-stack polymath blending code with community curation, Ben recounted its infusions across his odyssey—from parody O’Reilly covers viralizing memes to mutton-busting triumphs—framing bandits as bridges between artistic whimsy and scientific rigor, aligning devs with stakeholders in pursuit of optimal paths.
Ben’s prologue evoked dev.to’s genesis: Twitter-era jests birthing a creative agora, bandit logic A/B-testing post formats for engagement zeniths. The archetype—casino levers, pulls maximizing payouts—mirrors dev dilemmas: UI variants, feature rollouts, content cadences. Exploration probes unknowns; exploitation harvests proven yields. Ben advocated epsilon-greedy: baseline exploitation (1-ε pulls best arm), exploratory ventures (ε samples alternatives), ε tuning via Thompson sampling for contextual nuance.
Practical infusions abounded. Load balancing: bandit selects origins, favoring responsive backends. Feature flags: variants vie, metrics crown victors. Smoke tests: endpoint probes, failures demote. ML pipelines: hyperparameter hunts, models ascend via validation. Ben’s dev.to saga: title A/Bs, bandit-orchestrated, surfacing resonant headlines sans bias. Organizational strata: nascent projects revel in exploration—ideation fests yielding prototypes; maturity mandates exploitation—scaling victors, pruning pretenders. This lexicon fosters accord: explorers and scalers, once at odds, synchronize via phases, preempting pivots’ friction.
Caution tempered zeal: bandits thrive on voluminous outcomes, not trivial toggles; overzealous testing paralyzes. As AI cheapens variants—code gen’s bounty—feedback scaffolds intensify, bandits as arbiters ensuring quality amid abundance. Ben’s coda: wield judiciously, blending craft’s flair with datum’s discipline for endeavors audacious yet assured.
Algorithmic Essence and Variants
Ben unpacked epsilon-greedy’s equilibrium: 90% best-arm fealty, 10% novelty nudges; Thompson’s Bayesian ballet contextualizes. UCB (Upper Confidence Bound) optimism tempers regret, ideal for sparse signals—dev.to’s post tweaks, engagement echoes guiding refinements.
Embeddings in Dev Workflows
Balancing clusters bandit-route requests; flags unleash cohorts, telemetry triumphs. ML’s parameter quests, smoke’s sentinel sweeps—all bandit-bolstered. Ben’s ethos: binary pass-fails sideline; array assays exalt, infrastructure for insight paramount.
Strategic Alignment and Prudence
Projects arc: explore’s ideation inferno yields scale’s forge. Ben bridged divides—stakeholder symposia in bandit vernacular—averting misalignment. Overreach warns: grand stakes summon science; mundane mandates art’s alacrity, future’s variant deluge demanding deft discernment.
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[KotlinConf2018] Mathematical Modeling in Kotlin: Optimization, Machine Learning, and Data Science Applications
Lecturer
Thomas Nield is a Business Consultant at Southwest Airlines, balancing technology with operations research in airline scheduling and optimization. He is an author with O’Reilly Media, having written “Getting Started with SQL” and “Learning RxJava,” and contributes to open-source projects like RxJavaFX and RxKotlin. Relevant links: O’Reilly Profile (publications); LinkedIn Profile (professional page).
Abstract
This article explores mathematical modeling in Kotlin, addressing complex problems through discrete optimization, Bayesian techniques, and neural networks. It analyzes methodologies for scheduling, regression, and classification, contextualized in data science and operations research. Implications for production deployment, library selection, and problem-solving efficiency are discussed, emphasizing Kotlin’s refactorable features.
Introduction and Context
Mathematical modeling solves non-deterministic problems beyond brute force, such as scheduling 190 classes or optimizing train costs. Kotlin’s pragmatic features enable clear, evolvable models for production.
Context: Models underpin data science, machine learning, and operations research. Examples include constraint programming for puzzles (Sudoku) and real-world applications (airline schedules).
Methodological Approaches
Discrete optimization uses libraries like OjAlgo for linear programming (e.g., minimizing train costs with constraints). Bayesian classifiers (e.g., Naive Bayes) model probabilities for spam detection.
Neural networks: Custom implementations train on MNIST for digit recognition, using activation functions (sigmoid) and backpropagation. Kotlin’s extensions and lambdas facilitate intuitive expressions.
Graph optimization: Dijkstra’s algorithm for shortest paths, applicable to logistics.
Analysis of Techniques and Examples
Optimization: Linear models minimize objectives under constraints; graph models solve routing (e.g., traveling salesman via genetic algorithms).
Bayesian: Probabilistic inference for sentiment/email classification, leveraging word frequencies.
Neural networks: Multi-layer perceptrons for fuzzy problems (image recognition); Kotlin demystifies black boxes through custom builds.
Innovations: Kotlin’s type safety and conciseness aid refactoring; libraries like Deeplearning4j for production.
Implications and Consequences
Models enable efficient solutions; choose based on data/problem nature (optimization for constraints, networks for fuzzy data).
Consequences: Custom implementations build intuition but libraries optimize; Kotlin enhances maintainability for production.
Conclusion
Kotlin empowers mathematical modeling, bridging optimization and machine learning for practical problem-solving.
Links
- Lecture video: https://www.youtube.com/watch?v=-zTqtEcnM7A
- Lecturer’s X/Twitter: @thomasnield
- Lecturer’s LinkedIn: Thomas Nield
- Organization’s X/Twitter: @SouthwestAir
- Organization’s LinkedIn: Southwest Airlines