Inference Acceleration

Inference Acceleration encompasses the hardware, software, and model optimizations that make AI inference faster, cheaper, and more energy-efficient. Hardware accelerators include NVIDIA H100/H200 GPUs (the workhorse of frontier-model inference), Google TPUs (powering Gemini), AWS Inferentia and Trainium chips, Groq's LPU (notable for extreme low-latency LLM serving), Cerebras wafer-scale chips, and Apple's Neural Engine for on-device work. Software techniques include continuous batching, paged attention (popularized by vLLM), speculative decoding, FlashAttention, KV cache management, and tensor parallelism. Model techniques include quantization (INT4, INT8, FP16, BF16), pruning, distillation, and architecture search. Real-world impact is dramatic — Groq has demonstrated Llama models running at 500+ tokens per second, and vLLM's optimizations routinely improve throughput by 10-24x. AI governance, AI compliance, and AI risk management programs incorporate acceleration choices into deployment records supporting responsible AI in production enterprise AI environments.

Centralpoint Routes Around Hardware Decisions: Oxcyon's Centralpoint AI Governance Platform sits above the hardware layer — calling OpenAI, Gemini, Llama (on Groq, Together, or your own H100s), or embedded models seamlessly. Centralpoint meters consumption, keeps prompts and skills on-prem, and embeds chatbots into your portals via a single JavaScript line.


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