GPU Programming: Architecture, Interconnects and LLMs
Published on September 17, 2025, the material collects GPU programming resources and includes a chapter on chips, interconnects, and implications for LLM workloads.
Source: Recursos de programação para GPUs e introdução a sistemas GPU (jax-ml.github.io). Text prepared with AI from this source.
What happened and what to do
Published on September 17, 2025, the material collects GPU programming resources. One chapter covers chip architecture, how chips connect, and the implications of these characteristics for LLM workloads, with a focus on NVIDIA GPUs. It provides engineers with technical context for assessing how architecture and interconnects affect these workloads.
A company can apply this knowledge to map its AI workloads, compare processing and communication requirements, and identify capacity needs or infrastructure changes. Wendelmaques can support this diagnosis and, where appropriate, scope monitoring pipelines and dashboards to track GPU utilization and performance.
How the consultancy can help
Wendelmaques can assess GPU infrastructure and workloads, define an implementation scope, and support monitoring and capacity analysis operations.
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