Beyond NVIDIA: Is AMD the only GPU alternative for HPC/AI Workloads

AMD MI355X Analysis Strong Node-Level Inference, but Not Yet Rack-Scale 288 GB HBM3E Memory 8 TB/s Bandwidth 1.4 kW Power Draw Quick Navigation * 1. Overview * 2. Performance Analysis * 3. Memory Advantage * 4. Interconnect Bottleneck * 5. Scale-Out Gaps * 6. Future Roadmap * 7. Software Ecosystem * 8. Final Analysis 1. Overview AMD's CDNA 4-based MI350X and MI355X offer clear gains over the MI300 generation. With 288 GB of HBM3E, 8 TB/s of band

A recent Synergy Research Group report has revealed a striking statistic that should give every infrastructure professional pause: hyperscalers—AWS, Azure, Google, Meta, and their peers—now account for 44% of the world's total data center capacity. More importantly, this figure is projected to surge

During my deployment engineering days at Amazon, we were in the middle of what felt like controlled chaos. New AWS regions launching monthly, services expanding globally, data centers coming online faster than anyone thought possible.

The world of large‑language‑model inference moves fast. Meta’s Llama 4 and DeepSeek's range of models turns yesterday’s “good enough” hardware into today’s bottleneck, so picking the right platform is more strategic than ever. I compared eight options that keep popping up in various engineering and sales conversations, including consumer RTX GPUs, Apple Silicon, NVIDIA’s H‑series, Groq’s purpose‑built LPU, Cerebras’ wafer‑scale engine, and turnkey DGX workstations. Each proves valuable in th