If you’re running a self-hosted inference stack for your AI agents, July 2026 brings a significant milestone: vLLM v0.25.0 is now available as a stable release, and it changes the game for how dense model inference works under the hood. The headline change? Model Runner V2 (MRv2) is now the default — and the old PagedAttention (V0) implementation is gone.

This guide walks through what changed, what it means for your agentic AI stack, and how to migrate confidently.

What Changed in vLLM v0.25.0

The v0.25.0 release consolidates 558 commits from 232 contributors. Here are the biggest technical changes for teams running inference for agentic workloads:

Model Runner V2 Is Now the Default

Model Runner V2 was previously opt-in for quantized models. In v0.25.0, it becomes the default execution path for all dense models. MRv2 brings significant improvements over the V1 path:

  • Early verification steps (EVS): The runner validates inputs before execution, catching mismatches earlier and reducing silent failures in production.
  • Real-time embeddings: Embedding generation is now supported natively within the unified runner path.
  • Prefix caching for Mamba hybrid models: Teams using Mamba-style hybrid architectures get prefix caching support, improving latency on repeated prefixes common in agentic prompt patterns.
  • Multimodal prefix bidirectional attention: Better support for multimodal inputs used in tool-calling agents.
  • Dynamic speculative decoding with full CUDA graphs: Speculative decoding now works alongside full CUDA graph acceleration, improving throughput without sacrificing latency on short outputs.

PagedAttention (V0) Is Gone

The legacy V0 attention backend — which was based on the original PagedAttention implementation that made vLLM famous — has been permanently removed. If your deployment used any environment variables or configuration flags that forced V0 behavior, those flags will now be ignored or cause errors.

This is not a feature deprecation with a fallback. The V0 code path is deleted. If you pinned to V0 for any reason (custom kernels, compatibility workarounds, benchmarking), you need to address that before upgrading.

Hardware Backend Updates

AMD ROCm

  • Moved to Torch 2.11 stable ABI, improving binary compatibility.
  • Added AITER FlashAttention MLA prefill backend.
  • Fused shared-expert optimizations for architectures like GLM and MiniMax-M3.
  • AITER MoE (Mixture of Experts) optimizations and custom all-reduce operations.
  • INT3 quantization support for AMD hardware.

Intel XPU

  • W8A8 FP8 linear kernel with multi-granularity quantization.
  • Pipeline-parallel accuracy fixes.
  • Uniform-batch CUDA graph for FA2 (Flash Attention 2).
  • Routing mm_prefix models to Triton attention backend.
  • C++ memory info improvements.

If your agent infrastructure uses AMD GPUs or Intel Gaudi/XPU accelerators, v0.25.0 brings meaningful production-readiness improvements — especially for MoE models and quantized inference.

How to Migrate

Important: The exact environment variables, config keys, and CLI flags for vLLM change across versions. The guidance below is conceptual and based on the nature of the changes described in the official v0.25.0 release notes. Always verify specific syntax against the official vLLM documentation and the v0.25.0 release notes before updating your production stack.

Step 1: Audit Your Current Configuration

Before upgrading, document your current vLLM startup configuration. Look for:

  • Any flags or environment variables that reference V0, paged_attention, or legacy attention backends.
  • Custom attention kernel overrides.
  • Speculative decoding configurations (these interact with the new CUDA graph support).
  • Hardware-specific flags for AMD or Intel backends, which may need updating for the new Torch/ABI versions.

Step 2: Update Your vLLM Installation

Update to v0.25.0 via pip or your preferred package manager. If you build from source, check the AMD or Intel build instructions in the repository — the ROCm Torch version requirement changed to 2.11.

# Example — verify exact package name and version for your environment
pip install vllm==0.25.0

Check the official installation guide for hardware-specific dependencies, especially for ROCm and XPU builds.

Step 3: Test with a Non-Production Workload

Before pointing production agent traffic at the upgraded instance:

  1. Run a smoke test with your most common prompt patterns — agentic prompts tend to be long and tool-call-heavy, which stresses prefix caching and KV management.
  2. Benchmark latency and throughput against your v0.24.x baseline. MRv2 should generally be faster, but the profile may differ for your specific model and hardware.
  3. Test speculative decoding if you use it — the CUDA graph integration changed in this release.
  4. Verify embedding generation if your pipeline uses vLLM for embeddings alongside generation.

Step 4: Update Monitoring and Observability

MRv2 may surface different internal metrics and log formats than V0. Review your Prometheus dashboards, alerting rules, and log parsers to ensure they still capture the right signals after the upgrade.

Step 5: Handle KV Cache Offloading

v0.25.0 adds KV cache offloading support, which can significantly extend effective context length by spilling KV caches to CPU memory. This is particularly valuable for long agentic conversation histories. Check the release notes for configuration details — this is an opt-in feature with specific memory management implications.

What This Means for Agentic AI Stacks

For teams running agents at scale, vLLM v0.25.0 is the most significant stability milestone in recent memory. The consolidation onto MRv2 means:

  • Fewer code paths to maintain — one execution engine for all dense models reduces surprise behavior differences between model types.
  • Better speculative decoding throughput — important for latency-sensitive agent loops where the model generates short tool calls.
  • Expanded hardware options — robust AMD ROCm and Intel XPU support gives you more flexibility on cloud and on-premises infrastructure choices.
  • FP8 MoE support — for teams running large MoE models (like Mixtral-style architectures), FP8 quantization on MoE layers delivers better throughput without precision collapse.

The removal of PagedAttention V0 is a clean break. If your stack was already on V1/MRv2 (the default since v0.4.x), this upgrade is largely transparent. If you were on a forked or customized version that preserved V0 behavior, now is the time to modernize.

Sources

  1. vLLM v0.25.0 Release Notes — GitHub
  2. vLLM Official Documentation

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260714-0800

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