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1. AMD Rolls Out Gemma 4 Model Support Across Full Range of GPUs & CPUs

   AMD now supports Google’s Gemma 4 models (2B–31B parameters) across its entire hardware lineup, including Instinct GPUs (datacenters), Radeon GPUs (workstations), and Ryzen AI processors (PCs). The integration is compatible with vLLM, SGLang, llama.cpp, Ollama, and Lemonade Server, aiming to optimize AI performance for both cloud and local deployment.

   2026-04-05 Tags: amd, google, gemma 4, gpu, cpu, radeon, ryzen, models, vllm, sglang, llama.cpp, machine learning, hardware support, llm, hardware by klotz
2. SGLang - Home

   SGLang is a fast serving framework for large language models and vision language models. It focuses on efficient serving and controllable interaction through co-designed backend runtime and frontend language.

   2025-04-30 Tags: llm, vision language models, inference engineering, quantization, sglang by klotz
3. Achieving Faster Open-Source Llama3 Serving with SGLang Runtime (vs. TensorRT-LLM, vLLM)

   This blog post benchmarks and compares the performance of SGLang, TensorRT-LLM, and vLLM for serving large language models (LLMs). SGLang demonstrates superior or competitive performance in offline and online scenarios, often outperforming vLLM and matching or exceeding TensorRT-LLM.

   2024-07-27 Tags: sglang, tensorrt-llm, vllm, llama, llm by klotz

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