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llm-d 1.0 releases production-ready LLM inference stack with multi-tenant QoS and stable API

·2026.10.21 09:00

Key point

The open-source Kubernetes-native stack supports vLLM and SGLang across heterogeneous hardware, claiming up to 3x higher output throughput and 2x faster TTFT.

Details

  • llm-d 1.0 introduces a stable v1 API with multi-tenant quality of service, zero-downtime upgrades, and agent-aware serving for production operations.
  • The Kubernetes-native stack runs vLLM, SGLang, and other engines across GPUs, TPUs, XPUs, CPUs, and NPUs, enabling deployment on existing hardware with consistent performance patterns.
  • Reported performance metrics include 3x higher output throughput, 2x faster Time To First Token (TTFT), up to 70% higher tokens/sec, and a 30% throughput improvement.
  • Specific benchmark results show a 40% reduction in TTFT and Inter-Token Latency (ITL) on Google Vertex.
  • The platform provides "Well-Lit Paths," which are tested deployment recipes for patterns such as prefill/decode disaggregation, wide expert parallelism for MoE models, and workload autoscaling.
open-sourcevllmkubernetessglangllm-inferencellm-d
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