llm-d
PulseGate's liveness check found it on 13 Sep 2026; it is registered on GitHub and has been in the index since 27 Jun 2026. How this is checked
llm-d is an open-source, high-performance distributed inference serving stack designed for deploying large language models on various hardware accelerators. It provides orchestration, optimization, and benchmarking tools for production ML inference, targeting ML engineers and infrastructure teams.
Inferred · not functionally tested
Overview
5 featuresPurpose: Simplifies and optimizes the deployment of large language models for high-performance inference on diverse hardware.
Inferred · not functionally tested
Audience: machine learning engineers
Inferred · not functionally tested
Functions: monitoring
Inferred · not functionally tested
Interfaces: API: indicated (inferred, not tested) · MCP: unknown · CLI: indicated (inferred, not tested) · Self-hosting: indicated (inferred, not tested)
Recorded constraints: pricing: open_source · license: Apache-2.0 · platforms: WEB · deployment: browser, cli, api_only, self_hosted
Constraint provenance is unknown; confirm requirements with the publisher.
Record sources: llm-d.ai · github.com. These links do not verify the individual claims.
In the Inference & model serving space, llm-d takes a focused approach. Inferred · not functionally tested: It simplifies and optimizes the deployment of large language models for high-performance inference on diverse hardware. Inferred · not functionally tested: llm-d is an open-source project aimed at machine learning engineers. Basis unknown · not verified: The project is open source (Apache-2.0). Basis unknown · not verified: It ships for the web, the command line, and API, and it can be self-hosted.
Behind llm-d is CNCF (Red Hat, Google Cloud, IBM Research, CoreWeave, NVIDIA), and it first shipped in 2025. Development happens publicly on GitHub with 3.5k stars and 530 commits in the last 90 days. Inferred · not functionally tested: Among its 5 catalogued features are distributed inference, kubernetes support, and model orchestration.
Summary written by a language model from the project’s public pages.
Tasks: Inferred · not functionally tested
- Distributed inference
- Kubernetes support
- Model orchestration
- Accelerator optimization
- Benchmarking
Topics: Inferred · not functionally tested
Built with & integrations
Trust & compliance
Indexing history
What PulseGate has recorded for this listing
Frequently asked questions about llm-d
- What is llm-d?
- Inferred · not functionally tested: Llm-d simplifies and optimizes the deployment of large language models for high-performance inference on diverse hardware. It is catalogued under Inference & model serving on PulseGate.
- Who is llm-d for?
- Inferred · not functionally tested: llm-d is an open-source project built for machine learning engineers.
- Is llm-d free?
- Basis unknown · not verified: Yes — llm-d is open source under the Apache-2.0 license and free to use.
- What platforms does llm-d run on?
- Basis unknown · not verified: llm-d runs on the web, the command line, and API. It can also be self-hosted.
- Is llm-d still active?
- PulseGate's liveness check found it on 13 Sep 2026. Its GitHub repository shows 530 commits in the last 90 days.
- What are alternatives to llm-d?
- Similar projects tracked by PulseGate include Mesh LLM, vLLM, and Trysil.Mesh LLMvLLMTrysil
- Who develops llm-d?
- llm-d is developed by CNCF (Red Hat, Google Cloud, IBM Research, CoreWeave, NVIDIA).
- When did llm-d launch?
- llm-d first shipped in 2025.
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