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A collection of bookmarks filtered by the tag "Distributed Inference".

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wafer-ai/gpu-perf-engineering-resources: A curated resource list for learning AI performance engineering, from GPU fundamentals to production inference.

wafer-ai/gpu-perf-engineering-resources: A curated resource list for learning AI performance engineering,...

CUDA Programming Kernel Optimization Inference Engines

Aug 25, 2026

amd-strix-halo-vllm-toolboxes/rdma_cluster/setup_guide.md at main · kyuz0/amd-strix-halo-vllm-toolboxes

amd-strix-halo-vllm-toolboxes/rdma_cluster/setup_guide.md at main · kyuz0/amd-strix-halo-vllm-toolboxes

Tensor Parallelism RDMA Distributed Inference

Jun 28, 2026

Distributed Inference Bookmarks | William Callahan - Bookmarks

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github.com

May 3, 2026

GitHub - Luce-Org/lucebox-hub: Lucebox optimization hub: hand-tuned LLM inference, built for specific consumer hardware.

GitHub - Luce-Org/lucebox-hub: Lucebox optimization hub: hand-tuned LLM inference, built for specific consumer hardware.

Lucebox optimization hub: hand-tuned LLM inference, built for specific consumer hardware. - Luce-Org/lucebox-hub

LLM Inference RTX 3090 Quantization Kernels Speculative Decoding CUDA Optimization

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github.com

May 3, 2026

GitHub - noonghunna/club-3090: Community recipes for serving LLMs on RTX 3090. Multi-engine (vLLM, llama.cpp, SGLang) and model-agnostic. Currently shipping Qwen3.6-27B configs for 1× and 2× cards.

GitHub - noonghunna/club-3090: Community recipes for serving LLMs on RTX 3090. Multi-engine (vLLM, llama.cpp, SGLang) and model-agnostic. Currently shipping Qwen3.6-27B configs for 1× and 2× cards.

Community recipes for serving LLMs on RTX 3090. Multi-engine (vLLM, llama.cpp, SGLang) and model-agnostic. Currently shipping Qwen3.6-27B configs for ...

Local LLM Deployment Llama.cpp GPU Optimization vLLM Large Language Models Serving

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learn-inference.com

August 20, 2026

Learn Inference: inference engineering, explained interactively

Learn Inference: inference engineering, explained interactively

An interactive guide to inference engineering: how generative AI models are served in production, from attention kernels to multi-cloud capacity.

Generative AI Model Serving Attention Kernels Inference Engineering GPU Infrastructure

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