The Next Wave of AI Infrastructure: Distributed Inference, Multi-Cloud, and Ray as the Unifying Layer
Published: November 11, 2025
AI infrastructure is undergoing a fundamental shift. For years, the dominant workload was pre-training massive models—a compute-intensive but relatively predictable batch process. Today, that model is being upended. Inference, post-training, and agentic loops now drive the majority of infrastructure demand, forcing a rethinking of how GPUs are scheduled, how models are served, and how clouds are selected.
At Ray Summit 2025, engineers from leading AI labs and production deployments converged on a shared narrative: the era of single-GPU serving is ending; post-training reinforcement learning has become a continuous production workload; and multi-cloud GPU sourcing is no longer a contingency plan but a core strategy. This article examines five key trends reshaping AI infrastructure and the economic logic behind them.
[IMAGE: Diagram showing evolution from single-GPU training to multi-GPU distributed inference with agentic loops]
---
The New AI Infrastructure Stack: From Training to Continuous Inference
The hidden economic logic behind this shift is simple: as model size and complexity increase, efficiency in inference and scheduling becomes the key cost lever. Pre-training costs, while still significant, are concentrated and amortized over months. Inference costs, by contrast, scale with every user query, every agent loop, and every fine-tuning iteration. When models like GPT-4-class architectures reach hundreds of billions of parameters, serving a single user request can consume multiple GPU-seconds. Multiply that by millions of requests and the total compute spend dwarfs training.
This has prompted adoption of distributed inference strategies—splitting model layers across GPUs, routing requests to expert shards in mixture-of-experts (MoE) models, and carefully managing key-value cache transfers. It has also driven multi-provider sourcing: no single cloud region today has enough top-tier GPU capacity to handle the demand spikes from major AI companies. CoreWeave, AWS, GCP, and Azure are all being used simultaneously by the same organization, with workload routing determined by spot pricing, availability, and latency requirements.
Ray Summit 2025 provided the latest observations on these patterns. The framework that began as a research project at UC Berkeley has become the de facto compute substrate for AI workflows, appearing in the top 1% of PyPI downloads (source: ClickPy) and powering nearly every major open-source post-training framework.
---
Distributed Inference and the Death of Single-GPU Serving
The notion that a single GPU can serve a state-of-the-art language model is becoming obsolete. For models above 70 billion parameters, or any MoE architecture with dozens of expert networks, one GPU simply cannot hold the full model in memory while also handling the attention computations and key-value cache required for long-context inference.
Distributed inference solves this through a technique called prefill/decode splitting. The prefill phase—processing the user's input tokens in parallel—is compute-bound and benefits from large batch sizes across many GPUs. The decode phase—generating tokens one at a time—is memory-bound and requires fast access to the model parameters and KV cache. By routing prefill to a dedicated set of GPU clusters and decode to separate, lower-latency nodes, organizations can optimize utilization for each phase independently.
For MoE models, the challenge is even greater. Each token activates only a subset of expert networks, so a token may need to be routed from GPU A to GPU B to access its relevant expert. This requires dynamic request routing and careful management of expert placement to minimize cross-node communication. The vLLM community, in collaboration with Ray, has developed libraries that handle this routing transparently, treating a pool of GPUs as a single logical inference engine.
The implication is profound: infrastructure must now support dynamic, multi-GPU coordination at inference time. This changes not only software requirements but hardware procurement decisions. Companies are moving away from purchasing isolated GPU servers toward building unified clusters where any GPU can serve any part of any model, enabled by a scheduling layer like Ray.
[IMAGE: Architecture diagram of distributed inference with prefill and decode stages routed across multiple GPUs]
---
Post-Training and Reinforcement Learning Become Core Workloads
Post-training—the family of techniques including fine-tuning, RLHF, and reinforcement learning—was historically treated as a one-off step performed after pre-training. No longer. As AI systems are deployed in production, teams continuously update their models based on user feedback, new data, and evolving objectives.
At Ray Summit 2025, it was evident that nearly every major open-source post-training framework is built on Ray. This includes TRL (Transformer Reinforcement Learning), OpenRLHF, and several proprietary stacks from leading labs. The reason is architectural: post-training workflows involve tight feedback loops between data collection, model evaluation, and weight updates. A typical RLHF pipeline requires running the model on a batch of prompts, collecting human or AI-generated preferences, training a reward model, updating the policy, and then evaluating the new policy against a holdout set—all while maintaining version control and provenance.
Two real-world examples stand out. Cursor, the AI-powered code editor, uses reinforcement learning to refine its code generation models. By training on actual user interactions—whether a suggested code snippet was accepted, edited, or rejected—Cursor continuously improves its suggestions. This requires Ray to orchestrate the training, inference, and evaluation across a heterogeneous pool of GPUs.
Physical Intelligence, a robotics startup, applies reinforcement learning to develop generalist policies for physical robots. Their models must learn from simulation, real-world data, and human teleoperation simultaneously. The compute demands are multimodal: they need GPU clusters for training, lower-latency GPUs for real-time robot control, and CPUs for data preprocessing. Ray provides the unified compute fabric that manages these resources across different clouds and on-premise clusters.
These workloads demand tight integration with data pipelines, evaluation loops, and deployment infrastructure. The market is responding with turnkey solutions like Anyscale (Ray's commercial offering) and managed services from cloud providers that embed Ray natively.
[IMAGE: Flowchart showing reinforcement learning cycle: data collection, training, deployment, evaluation, feedback]
---
Multimodal and Agentic Workflows Demand Heterogeneous Compute
The rise of multimodal models—those that process text, images, video, audio, and even sensor data simultaneously—introduces a new layer of complexity. Such models require heterogeneous compute: CPUs for data preprocessing and decompression, GPUs for training and inference, and specialized hardware for audio encoding or video decoding.
Consider a typical agentic workflow: a user asks an AI assistant to summarize a video and generate a chart from the transcript. The system must ingest and transcode the video (CPU-heavy), run a vision model to extract frames (GPU), run a speech-to-text model (GPU), feed the text into a large language model (GPU), generate a chart image (GPU), and then format the response. Each step may run on different hardware types, and the overall latency budget is tight.
Ray's ability to abstract these different compute resources behind a single API is becoming essential. With its task and actor model, developers can specify resource requirements per step—a given task needs 2 CPUs and 1 GPU with 16GB memory, another needs 4 CPUs and no GPU—and Ray handles placement, queuing, and fault tolerance.
Multimodal workflows also drive demand for flexible data pipelines. The data engineering required to prepare video datasets, align audio with text, and generate synthetic images is itself a compute-intensive process. Organizations are increasingly using Ray Data to build these pipelines, pipelining preprocessing directly into training to avoid writing intermediate files to disk.
[IMAGE: Diagram showing heterogeneous compute nodes (CPU, GPU, TPU) connected via Ray, with multimodal data flowing through preprocessing, training, and inference stages]
---
Global GPU Scheduling: Policy-Driven Preemption and Spot Instances
One of the most underappreciated aspects of modern AI infrastructure is scheduling. As organizations scale to hundreds or thousands of GPUs across multiple clouds, the challenge shifts from "get any GPU" to "get the right GPU at the right price under the right policy."
Global GPU schedulers, built on Ray, now support policy-driven preemption. For example, a team running a long post-training job may accept being preempted if a higher-priority inference-serving job needs those GPUs—but only if the preemption saves at least 30% in cost. Conversely, a batch inference job can opportunistically use spot instances, pausing and resuming as spot prices fluctuate.
This is more than just a technical feature; it's an economic necessity. GPU spot instance prices on major clouds can vary by 5x over a day. By building a scheduler that understands both workload priority and cost awareness, companies can dramatically reduce their AI operations spend. Cursor, for instance, reports saving over 40% on inference costs by routing non-latency-sensitive workloads to spot and preemptible instances on CoreWeave while keeping critical user-facing inference on reserved GCP capacity.
Ray's built-in placement group and scheduling strategies enable this. Users can define "gang scheduling" for distributed training (all GPUs must be allocated simultaneously) alongside "spread scheduling" for inference (GPUs should be distributed across availability zones for fault tolerance). The scheduler optimizes for both, even as nodes are dynamically added and removed from the cluster.
[IMAGE: Screenshot-style mockup of a Ray dashboard showing global GPU utilization, spot price curves, and preemption events across AWS, GCP, Azure, and CoreWeave]
---
Multi-Cloud GPU Sourcing: Combatting Scarcity Through Diversity
The final trend is perhaps the most operational: multi-cloud is no longer a theoretical architecture; it is a survival tactic. Global GPU shortages, particularly for NVIDIA's H100 and B200 chips, mean that no single provider can guarantee the capacity needed for large-scale AI workloads. Companies are signing contracts with up to five providers simultaneously, using Ray to abstract the differences.
The practical challenges are significant. Each cloud provider has different networking (NVIDIA InfiniBand vs. Elastic Fabric Adapter vs. Google's custom interconnects), different VM shapes, different billing models, and different data transfer costs. Ray's ability to present a unified cluster across these environments—with automatic node discovery, health checking, and workload migration—is the key enabler.
During Ray Summit 2025, one attendee from a major AI lab described their architecture: a Ray cluster spanning AWS us-east-1, GCP us-central1, CoreWeave us-west-2, and on-premise GPUs at their headquarters, all managed through a single Ray dashboard. When spot prices spike on one cloud, the scheduler automatically migrates workloads to cheaper regions, respecting data locality constraints.
The economic logic is clear: by diversifying GPU sources, organizations reduce their exposure to single-provider outages and price volatility. They also gain negotiating leverage. But without a unifying layer like Ray, managing such a distributed setup would require custom scripting and manual intervention, making it impractical at scale.
---
Conclusion: The Unifying Layer
These five trends—distributed inference, continuous post-training, heterogeneous compute, global scheduling, and multi-cloud sourcing—coalesce around a single conclusion: modern AI infrastructure needs a unified compute substrate that can abstract away the complexity of distributed resources. Ray has emerged as that layer, not by accident but by design.
Its open-source ecosystem, with deep integrations into vLLM, PyTorch, and every major post-training framework, makes it the natural choice for teams building production AI systems. And its commercial backing from Anyscale ensures enterprise-grade reliability and support.
As we look ahead to 2026, the pressures that drove these trends will only intensify. Model sizes will continue to grow; agentic workflows will become more complex; and GPU supply will remain constrained. Organizations that invest in a flexible, multi-cloud, distributed infrastructure layer today will be best positioned to ride the next wave of AI innovation.
Keywords: AI infrastructure trends, distributed inference, Ray framework, multi-cloud GPU, post-training reinforcement learning, agentic workflows, GPU scheduling, heterogeneous compute