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vLLM

Verified
Artificial IntelligenceGlobal
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High-throughput, low-latency LLM serving engine powered by PagedAttention.

Tracked Changes
6
Pricing Shifts
0
Features & Launches
6
Last Verified Event
Sep 12, 2026

vLLM Chronological Timeline

2026
featureSep 12, 2026
96% Verified

v0.29.1rc0

vLLM v0.29.1rc0 introduces dual-key Gumbel-max watermarking support specifically for speculative decoding workflows. This implementation enables cryptographically verifiable provenance for model outputs generated through speculative execution pipelines.

Before: Speculative decoding pipelines lacked native support for Gumbel-max watermarking, limiting provenance tracking in high-throughput inference.
After: Native support for dual-key Gumbel-max watermarking is now integrated into the speculative decoding execution path.
View full change record & proof ➔
product launchSep 11, 2026
96% Verified

proto-v0.1.0

The vLLM project has released vllm-proto version 0.1.0. This release introduces the initial implementation of the proto package within the vLLM ecosystem.

Before: The vLLM repository lacked a dedicated proto-specific package for standardized protocol definitions.
After: The vllm-proto package is now available at version 0.1.0.
View full change record & proof ➔
featureSep 10, 2026
96% Verified

v0.29.0

Model Runner V2 (MRV2) is now the default execution engine for all models in vLLM. This release introduces CUDA graph memory profiling for KV cache auto-sizing and batch-sharded sampling to reduce per-step logits memory usage.

Before: Model Runner V1 was the default execution engine, with MRV2 limited to specific pooling models.
After: Model Runner V2 is the default engine for all models, featuring batch-sharded sampling, CUDA graph memory profiling, and enhanced speculative decoding support.
View full change record & proof ➔
featureSep 8, 2026
96% Verified

v0.29.0rc6

vLLM v0.29.0rc6 introduces a default dense prefix cache configuration for hybrid model architectures. This update addresses issue #55 to ensure consistent memory management across mixed-model deployments.

Before: Hybrid models lacked a default dense prefix cache configuration, potentially leading to suboptimal KV cache utilization.
After: Hybrid models now utilize dense prefix caching by default, improving memory efficiency and inference performance.
View full change record & proof ➔
featureSep 8, 2026
96% Verified

v0.29.0rc5

<p>[Core] Default prefix_cache_retention_interval to dense for Mamba + E…</p>...

Before: Previous platform capabilities and architecture.
After: Updated platform deployment with v0.29.0rc5.
View full change record & proof ➔
featureSep 4, 2026
96% Verified

v0.29.0rc4: [Bugfix] Avoid sync in TRT-LLM ragged prefill

<p>Generated-by: Codex <a href="mailto:codex@openai.com">codex@openai.com</a></p> <p>Signed-off-by: Codex <a href="mailto:codex@openai.com">codex@openai.com</a></p>...

Before: Previous platform capabilities and architecture.
After: Updated platform deployment with v0.29.0rc4: [Bugfix] Avoid sync in TRT-LLM ragged prefill.
View full change record & proof ➔

Complete vLLM Change Log Index

DateChange TitleTypeImpactDetails
Sep 12, 2026v0.29.1rc0feature8/10View ➔
Sep 11, 2026proto-v0.1.0product_launch5/10View ➔
Sep 10, 2026v0.29.0feature9/10View ➔
Sep 8, 2026v0.29.0rc6feature7/10View ➔
Sep 8, 2026v0.29.0rc5feature8/10View ➔
Sep 4, 2026v0.29.0rc4: [Bugfix] Avoid sync in TRT-LLM ragged prefillfeature8/10View ➔