llm-d/llm-d
Achieve state of the art inference performance with modern accelerators on Kubernetes
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A Datacenter Scale Distributed Inference Serving Framework
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Latest capture 2026-07-15 03:03
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Scanned 2026-07-15 03:03
Cargo.toml
rust ecosystem,
96 dependencies
pyproject.toml
python ecosystem,
38 dependencies
Cargo.lock
rust ecosystem,
875 dependencies
benchmarks/pyproject.toml
python ecosystem,
11 dependencies
.github/scripts/package.json
javascript ecosystem,
2 dependencies
deploy/operator/go.mod
go ecosystem,
109 dependencies
deploy/snapshot/go.mod
go ecosystem,
116 dependencies
deploy/utils/requirements.txt
python ecosystem,
10 dependencies
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Achieve state of the art inference performance with modern accelerators on Kubernetes
Efficent platform for inference and serving local LLMs including an OpenAI compatible API server.
Low-code framework for building custom LLMs, neural networks, and other AI models
Build, run and scale AI agents like API and microservices - observable,auditable and identity-aware from day one.
A unified library of SOTA model optimization techniques like quantization, distillation, pruning, neural architecture search, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM, TensorRT, vLLM, etc. to optimize inference speed.
A blazing fast AI Gateway with integrated guardrails. Route to 1,600+ LLMs, 50+ AI Guardrails with 1 fast & friendly API.