Beyond GPUs, efficiency gains are coming from networking, memory bandwidth and low-level kernels. Examples from F5, AMD and Meta show that orchestration, data movement and attention kernels can multiply throughput at constant compute.
F5's test on BIG-IP Next for Kubernetes shows that the same GPU cluster can deliver more than triple the performance by tuning traffic balancing and routing. For on-premise deployments, node-level efficiency becomes central to TCO; the risk is coupli...
Meta's GEM attention kernel, rewritten in TLX, beats FlashAttention-4 on jagged Blackwell workloads: +13% forward and +50% backward on average, with about 3,200 lines versus 10,000. The result shows explicit hardware control no longer requires hand-w...
In F5's lab, using BIG-IP Next for Kubernetes showed more than triple performance on the same GPU cluster. The result shifts focus from raw compute to network and orchestration efficiency: for on-premise LLM deployments, data traffic can be the real ...
AMD's new 256-core EPYC delivers memory bandwidth at 91% of an RTX 5090 through a 16-channel DDR5-12800 controller. The LocalLLaMA community responds with sarcasm and realism about the cost of high-density RDIMMs and the question of how many tokens p...
Nvidia unveils RTX Mega Geometry 2.0, which streams ray-tracing geometry into VRAM only when needed. The approach reduces memory pressure and points to a broader shift in how local workloads use GPU memory, with implications for teams evaluating on-p...
