Elevate Your LLM Inference: Autoscaling with Ray, ROCm 7.0.0, and SkyPilot

2026-02-13 · Source: AMD ROCm Blogs · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Software Development & Engineering · Depth: Intermediate, long

Summary

This blog post, published on February 13, 2026, details how to autoscale large language model (LLM) inference workloads using Ray Serve with a vLLM backend on AMD Instinct GPUs, leveraging AMD's ROCm 7.0.0 software platform. It demonstrates scaling from single-GPU to multi-cluster deployments, including multi-node and multi-cloud configurations via SkyPilot. The article provides a step-by-step guide for setting up a Docker container with ROCm 7.0.0, installing vLLM 0.10.2 from source, and configuring Ray Serve 2.52.1 for autoscaling on a single node with 8 MI300X GPUs. It further illustrates multi-node and multi-cluster autoscaling using SkyPilot across two Kubernetes clusters, each with a single AMD Instinct MI300X GPU, showcasing how these tools manage variable request loads to optimize performance and control infrastructure costs.

Key takeaway

For AI Engineers and MLOps teams deploying LLM inference, integrating Ray Serve with ROCm 7.0.0 on AMD Instinct GPUs, augmented by SkyPilot for multi-cloud orchestration, offers a robust solution for managing fluctuating traffic. You should configure autoscaling policies carefully to balance performance and cost efficiency, ensuring your infrastructure dynamically adapts to demand without manual intervention. This setup allows for seamless scaling from single-node to complex multi-cluster environments.

Key insights

Ray Serve, ROCm 7.0.0, and SkyPilot enable scalable, cost-efficient LLM inference across diverse cloud and cluster environments.

Principles

• Autoscaling optimizes resource use for variable LLM inference loads.
• Unified platforms simplify scaling from single-GPU to multi-cloud.
• Monitoring metrics drives dynamic replica adjustments.

Method

Configure Ray Serve with vLLM on ROCm-enabled AMD GPUs, setting autoscaling policies like `max_ongoing_requests` and `target_ongoing_requests`. For multi-cluster, use SkyPilot with a YAML configuration to deploy and manage Ray Serve replicas across Kubernetes clusters.

In practice

• Use `rocm/vllm` Docker images for vLLM inference on ROCm 7.0.0.
• Employ Locust for client-side load testing to trigger autoscaling.
• Monitor Ray Dashboard and logs to verify autoscaling behavior.

Topics

• LLM Inference Autoscaling
• Ray Serve
• AMD ROCm 7.0.0
• SkyPilot
• Multi-Cloud Deployment

Code references

• vllm-project/vllm
• ROCm/rocm-blogs
• ROCm/rocm-blogs

Best for: MLOps Engineer, AI Engineer, Deep Learning Engineer

Related on AIssential

• NVIDIA Dynamo on AKS - Autoscaling LLM Inference — Effective LLM inference autoscaling requires specialized tools like NVIDIA Dynamo on AKS to manage variable loads and GPU constraints.
• Exploring Use Cases for Scalable AI: Implementing Ray with ROCm 7 Support for Efficient ML Workflows — Ray and ROCm 7.0.0 enable scalable, efficient AI/ML workflows on AMD GPUs, particularly for LLMs.
• Reliable LLM Inference at Scale — Reliable LLM inference at scale requires sophisticated capacity management, cost-based routing, and robust runtime failure detection.
• Automating Inference Optimizations with NVIDIA TensorRT LLM AutoDeploy — AutoDeploy automates PyTorch LLM optimization for TensorRT LLM, significantly reducing manual inference engineering.
• Running AI on mixed hardware for speed and affordability — llm-d optimizes LLM inference on mixed GPU clusters by intelligently routing requests based on KV cache state, significantly boosting throughput and reducing costs.
• Lodestar: An Online-Learning LLM Inference Router — Lodestar uses online learning to dynamically route LLM inference requests, significantly reducing latency by adapting to real-time cluster conditions.

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Editorial summary, takeaway, and curation by AIssential. Original article published by AMD ROCm Blogs.