Ray Summit 2026 Attendees

Event snapshot

• Dates: August 24–26, 2026
• Venue: San Francisco, CA — venue details via anyscale.com/ray-summit/2026
• Attendees: Hundreds of AI builders, ML infrastructure leaders, and applied AI researchers
• Format: Three days — Day 1: full-day code-first training workshops; Day 2: keynotes, technical sessions, breakouts; Day 3: customer highlights, lightning talks, closing keynotes
• Speakers: Ray maintainers, ML infrastructure leaders, and applied AI researchers from leading AI labs and production engineering teams
• Topics: Distributed computing, LLM workflows, high-throughput inference, vLLM, reinforcement learning at scale, multimodal pipelines, foundation model training, Ray Core, Anyscale platform
• Registration: Live now at anyscale.com/ray-summit/2026 — early bird pricing available
• Hosted by: Anyscale — the company founded to develop Ray and build products and services around it

Why a Ray Summit registration is the most infrastructure-specific buying signal in AI

Most AI conferences attract a mix of practitioners, executives, and observers. Ray Summit attracts almost exclusively practitioners — and not practitioners who are learning about distributed AI systems, but practitioners who are running them. The self-selection mechanism is the event itself: Ray Summit is explicitly built around a specific open-source framework. The only reason to attend is that you are actively building with Ray or actively evaluating it for a production deployment. The sessions are code-first. The speakers are the engineers who built the tools. The attendees are the engineers running them.

Every Ray Summit registrant is inside a specific, identifiable, and high-value infrastructure buying window. Here is what they are doing in the 60 days surrounding the event:

• Evaluating Anyscale and Ray-adjacent platforms for production deployment — attendees have almost always already validated Ray as the right framework for their workload. The conference is where they evaluate the enterprise support, managed platform, and tooling ecosystem that makes Ray production-grade at their organization's scale.
• Benchmarking GPU and compute providers against real throughput requirements — attendees arrive with specific compute bottlenecks and leave with a vendor shortlist. GPU cloud providers, bare metal compute vendors, and specialized inference hardware companies are all evaluated in the weeks surrounding the event.
• Selecting vLLM and inference optimization tooling — vLLM integration with Ray is a confirmed session topic. Attendees building high-throughput, low-latency inference services are actively benchmarking vLLM against competing inference frameworks.
• Choosing an MLOps and experiment tracking platform — tooling for experiment tracking, model versioning, pipeline orchestration, and deployment automation is purchased in parallel with Ray infrastructure decisions and accelerated by the conference.
• Evaluating reinforcement learning and model adaptation tooling — Ray Summit 2026 explicitly features talks on advanced model adaptation and RL using Ray. Attendees in this cohort are evaluating RL training infrastructure, reward modeling tooling, and RLHF platforms.
• Getting peer validation from engineers running similar systems — the peer conversations at this event are the highest-signal reference checks in the ML infrastructure market — an endorsement from an engineer running Ray at OpenAI or Uber carries more weight than any vendor case study.

Who attends Ray Summit 2026

Ray Summit draws a precisely defined cohort of distributed systems and ML infrastructure practitioners. The event's technical depth acts as a natural filter — the code-first workshops, the production case studies, and the Ray-specific framing mean that only practitioners with active distributed AI workloads attend. Every role at Ray Summit represents a high-value infrastructure buyer.

The three-day format — and what each day signals

Ray Summit's three-day structure is not a standard conference format. Each day serves a different practitioner's needs and concentrates a different buyer profile.

• Day 1 — Code-first training workshops — Full day of immersive, expert-led workshops taught by the engineers who build and maintain Ray. Covers distributed computing fundamentals, model training at scale, and GenAI infrastructure — in code, not slides. Day 1 training attendees are the highest-intent infrastructure buyers in the dataset.
• Day 2 — Main conference: keynotes & technical sessions — First full conference day: keynotes, technical talks, and breakouts on LLM workflows from data processing to serving, high-throughput inference with vLLM, model adaptation and RL strategies, Ray Core updates and the 2026 roadmap preview, and production case studies from leading AI teams.
• Day 3 — Customer highlights, lightning talks & closing keynotes — Concentrates on real-world production deployments — customer case studies, practitioner lightning talks, and closing keynotes. Where Ray Summit most directly surfaces the 'what's working and what's breaking' peer intelligence the conference is known for.

What Ray Summit 2026 attendees are actively evaluating

• Anyscale managed platform and Ray enterprise support — every Ray Summit attendee is a potential Anyscale customer evaluating the managed platform against the cost and control tradeoffs of running Ray on their own infrastructure.
• GPU cloud providers and compute infrastructure — CoreWeave, Lambda Labs, Together AI, Modal, and the major hyperscalers (AWS, GCP, Azure) benchmarked on cost and performance against production workload requirements.
• vLLM and high-throughput inference serving infrastructure — vLLM is a confirmed session topic at Ray Summit 2026. Attendees benchmark vLLM deployment infrastructure and serving optimization tooling.
• MLOps and experiment tracking platforms — MLflow, Weights & Biases, DVC, and competing experiment tracking and model registry platforms are evaluated by every ML engineer managing production Ray workloads.
• Distributed storage and data pipeline infrastructure — high-throughput storage, data preprocessing pipelines, and feature stores that can feed Ray's distributed compute without creating data bottlenecks.
• RL training infrastructure and RLHF tooling — reinforcement learning at scale is a confirmed topic at Ray Summit 2026. RL training frameworks, reward modeling platforms, and human feedback tooling that integrates with Ray.
• Kubernetes and cluster management platforms — Ray clusters run on Kubernetes in most production deployments. Cluster management platforms, autoscaling tooling, and the cloud-native infrastructure layer are all in active evaluation.
• AI observability and distributed tracing for ML systems — ML-specific observability platforms that provide tracing, profiling, and debugging across Ray's distributed execution graph.

Agent Jesse vs. the database tools

The gap between "ML Engineer at an AI company in San Francisco" and "ML Engineer registered for Ray Summit Day 1 training, team running Ray Train on self-managed Kubernetes, GPU utilization bottleneck referenced in two open job descriptions, no managed Ray platform in current stack" is the entire difference between a cold email and a warm account.

Ray Summit is where production ML infrastructure decisions get made. Agent Jesse catches the signal before the first workshop starts.

Frequently Asked Questions