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Hardware & Infrastructure

186 summarised stories in Hardware & Infrastructure, each linking back to the original source. Browse all topics →

Monday, 11 May 2026

Building Blocks for Foundation Model Training and Inference on AWS

Hugging Face Blog 2 months ago

AWS released new GPU instance families—P6 with NVIDIA Blackwell B200 and B300 chips, and P6e-GB200 UltraServers with up to 72 GPUs in a single NVLink domain—designed to support foundation model training and inference across pre-training, post-training, and inference phases. The B200 GPU delivers 2.25 PFLOPS of dense BF16/FP16 Tensor throughput and 180 GB of HBM3e memory, while the B300 variant provides 288 GB of memory and support for up to 13.5 PFLOPS of FP4 operations. These systems integrate with open-source tools like PyTorch, Kubernetes, and Prometheus to reduce communication bottlenecks and improve scaling efficiency for large-scale distributed training workloads.

Serving DeepSeek-V4: why million-token context is an inference systems problem

Together AI 2 months ago

DeepSeek-V4's million-token context window relies on architectural changes using compressed sparse attention, heavily compressed attention, and sliding window attention that reduce key-value cache requirements. Together achieved 3.7M tokens of capacity on an NVIDIA HGX B200 node through cache management policies, compared to 1.2M without optimization. Serving V4 efficiently requires inference engines to handle multiple cache types, implement context-aware prefix caching policies, and choose endpoint configurations matched to workload characteristics—making long-context serving primarily a systems problem rather than just a model capability.

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