AMD MI300X
PulseAugur coverage of AMD MI300X — every cluster mentioning AMD MI300X across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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New tools and research tackle GPU optimization for AI workloads
Several research papers and a new open-source tool address challenges in optimizing AI workloads on GPUs. COMPASS-ABS aims to reduce fragmentation in shared GPU clusters for deep learning training, improving resource ut…
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Top GPU Neoclouds 2026: CoreWeave, Nebius, Lambda, Crusoe, Groq Ranked
A 2026 ranking of GPU neocloud providers highlights CoreWeave as the sole Platinum-rated option, commanding premium pricing for high-end NVIDIA GPUs. Nebius leads in publishing on-demand pricing for the latest B300 chip…
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Kog's new engine boosts AI inference speed on existing GPUs · 4 sources tracked
French startup Kog has developed a new inference engine designed to significantly accelerate AI model performance on existing datacenter GPUs, such as the AMD MI300X and NVIDIA H200. The company's software-driven approa…
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Developer runs 122B Qwen LLM locally on AMD MI300X GPU
A developer has successfully deployed the Qwen3.5-122B large language model locally on AMD MI300X GPU infrastructure. This setup utilizes vLLM and SGLang for inference, enabling an autonomous agent built with OpenClaw. …
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MoonMath AI open-sources HIP attention kernel for AMD MI300X, beating AITER v3
MoonMath AI has open-sourced a new bf16 forward attention kernel for AMD's MI300X GPU, written in HIP. This kernel reportedly outperforms AMD's own AITER v3 across various configurations, achieving up to a 1.26x speedup…
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MoonMath AI open-sources AMD MI300X attention kernel outperforming AITER v3 · 3 sources tracked
MoonMath AI has released an open-source HIP attention kernel for AMD's MI300X GPU, which reportedly outperforms AMD's own AITER v3. The kernel achieves speedups of up to 1.26x by optimizing memory placement and using on…
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Qwen3 32B fine-tuning fails on AMD MI300X
A fine-tuning attempt of the Qwen3 32B model on AMD MI300X hardware encountered significant issues, leading to wasted resources and a lack of learning. The process reportedly consumed $10 in GPU credits before it was re…
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Kog AI achieves 3,000 tokens/s LLM inference on standard GPUs
Kog AI has launched a tech preview of its Kog Inference Engine (KIE), demonstrating significantly faster real-time LLM inference speeds on standard datacenter GPUs. The engine achieves 3,000 output tokens per second on …
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Triton MoE kernel achieves high performance on AMD, NVIDIA
A new fused Mixture-of-Experts (MoE) dispatch kernel, written entirely in Triton, achieves 89-131% of the performance of Stanford's Megablocks library. This kernel notably runs on AMD MI300X hardware without any code mo…
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MachinaCheck uses specialized agents to ensure manufactured parts are machinable
MachinaCheck is a novel multi-agent system designed to bridge the gap between CAD design and CNC manufacturing, ensuring parts are machinable before production. This system utilizes specialized agents that parse geometr…
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Zyphra's TSP strategy boosts LLM training throughput by 2.6x
Zyphra has developed a new technique called Tensor and Sequence Parallelism (TSP) designed to optimize the training and inference of large transformer models. This hardware-aware strategy combines aspects of Tensor Para…
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MachinaCheck automates CNC manufacturability analysis using on-premise AI
A new system called MachinaCheck has been developed to automate the manufacturability assessment of CNC parts, reducing the process from an hour to 30 seconds. This multi-agent AI system leverages the Qwen 2.5 7B Instru…