# vLLM v0.19.0 Ships with Gemma 4 Support and Zero-Bubble Async Scheduling

- Link: https://thestackobserver.com/vllm-v0-19-0-ships-with-gemma-4-support-and-zero-bubble-async-scheduling/
- Published: 2026-04-06T08:32:57-04:00
- Author: Stackxx

The vLLM project has released **v0.19.0**, a major update bringing 448 commits from
197 contributors. This release represents a significant advancement for AI inference
workloads, introducing support for Google’s Gemma 4 architecture alongside substantial
performance optimizations that practitioners have been anticipating.

## Gemma 4 Support Lands

The headline feature is full support for Google’s Gemma 4 architecture, including
MoE (Mixture of Experts), multimodal capabilities, reasoning models, and tool-use
functionality. vLLM v0.19.0 requires transformers>=5.5.0 for Gemma 4 support, with
the project recommending their pre-built Docker image `vllm/vllm-openai:gemma4` 
for immediate production use.

This positions vLLM as one of the first inference engines to fully support Gemma
4’s complex architecture. For organizations evaluating Google’s latest open weights
models, v0.19.0 removes a significant integration barrier.

## Zero-Bubble Async Scheduling + Speculative Decoding

Perhaps more consequential for production deployments is the maturation of vLLM’s
async scheduling system. Version 0.19.0 adds **zero-bubble async scheduling with
speculative decoding**—meaning the inference pipeline can overlap prefill and decode
operations without gaps, significantly improving throughput for latency-sensitive
workloads.

The Model Runner V2 (MRV2) subsystem sees continued investment, adding:

 * Piecewise CUDA graphs for pipeline parallelism
 * Spec decode rejection sampler with greedy/logprobs support
 * Multi-modal embeddings for speculative decoding
 * Streaming inputs support
 * EPLB (Expert Parallel Load Balancing) support

## ViT Full CUDA Graph Capture

Vision Transformer (ViT) encoders now support full CUDA graph capture, reducing 
overhead for multimodal models that process both text and images. This addresses
a long-standing performance gap where vision components added unpredictability to
inference latency. With full CUDA graph capture, vision encoders now exhibit the
same predictable latency characteristics as text-only models.

## CPU KV Cache Offloading and Generalized DBO

v0.19.0 introduces a **general CPU KV cache offloading mechanism for V1**, complete
with pluggable cache policies and block-level preemption handling. This allows deployments
to trade GPU memory for latency—useful for serving large models on hardware-constrained
environments or maximizing throughput on shared infrastructure.

The Dual-Batch Overlap (DBO) microbatch optimization now works with general models,
not just specific architectures. Previously limited to particular model configurations,
DBO can now accelerate inference across the supported model catalog.

## Hardware Support Expansions

The release adds support for **NVIDIA B300/GB300 (SM 10.3)** with all-reduce fusion
enabled by default and tuned communicators. Intel XPU gains MLA model support and
CompressedTensor W4A8 support. AMD ROCm improves to ROCm 7.2.1 with torch 2.10 and
triton 3.6, adds DeepEP as an all2all backend, and introduces persistent MLA kernels
via AITER.

## Transformers v5 Compatibility

With HuggingFace Transformers v5 recently released, vLLM v0.19.0 includes extensive
compatibility fixes across the model catalog. This prevents the class of integration
failures that typically accompany major Transformers releases, allowing teams to
upgrade their model ecosystem components without coordination friction.

## Model Support Additions

New architectures supported include Cohere ASR, Cohere Transcribe, ColQwen3.5 4.5B,
LFM2-ColBERT-350M, Granite 4.0 1B Speech, and Qwen3-ForcedAligner. The speculative
decoding system gains Eagle3 support for Pixtral and various stability improvements.

## Security Hardening

The release adds `VLLM_MAX_N_SEQUENCES` environment variable enforcement and frame
limits in VideoMediaIO to prevent resource exhaustion attacks. Teams operating vLLM
in multi-tenant or exposed environments should prioritize this update.

## Sources

 * [vLLM v0.19.0 Release Notes](https://github.com/vllm-project/vllm/releases/tag/v0.19.0)—
   vLLM Project