In-depth technical analysis and market research from CS²B Technologies.
Comprehensive technical analysis of the AI accelerator landscape covering GPU, TPU, and custom ASIC architectures from NVIDIA, AMD, Intel, Google, AWS, and emerging players.
The GPU isn't the endgame. Purpose-built silicon for agent workloads is coming. The infrastructure layer underneath — accelerators, memory, security — that's where the real platform wars are being fought. The future is bright. I'm building it.
"Comprehensive research into BlueField DPU performance under AI microburst workloads. Verified analysis of NVIDIA ASTRA architecture, E/W latency degradation, and real-time QoS enforcement challenges in multi-tenant AI infrastructure.
Publication-quality technical documentation covering NVMe specification evolution for GPU-direct storage access, including the 14 challenges taxonomy for AI infrastructure architects.
I have framed the GPU-storage problem space with publication-quality technical documentation. The 14 challenges taxonomy is genuinely useful for architects designing AI infrastructure. This is among the best GPU-storage integration documentation outside of internal NVIDIA/Micron engineering docs.
"Memory-efficient LLM serving using CXL-based intelligent memory endpoints with hardware-accelerated cache management. 13 chapters, 10 appendices of publication-quality technical documentation.
The Innovation Gap: Per-head tracking, attention-aware eviction, RoPE-aware prefetch, and controller intelligence — these are the missing pieces nobody has built yet. This architecture achieves 97% HBM hit rates and 6× memory expansion. I've documented what the next generation of LLM infrastructure needs to look like.
"Industry analysis of how CXL 3.0 and Ultra Ethernet Consortium (UEC) technologies can be integrated to bridge internal memory fabric with external network fabric. Explores cache-coherent interconnects and memory pooling for next-generation AI infrastructure.
The convergence of CXL memory semantics with UEC's high-performance networking creates a unified fabric for AI workloads. Internal memory pooling meets external scale-out — this is how we build the infrastructure for trillion-parameter models.
"Large-scale AI training is fundamentally bottlenecked by fault tolerance. Current checkpointing approaches stall GPU compute for seconds to minutes, adding 5-15% overhead to training time. At exascale, hardware failures happen daily — and each failure can erase hours of progress.
As GPU architectures move to chiplet designs (AMD MI300, Intel Ponte Vecchio, future NVIDIA), the die-to-die interconnect (UCIe) becomes the natural interception point for state persistence. Every memory transaction already flows through UCIe bridges — why not checkpoint there? Compute never stalls. Checkpointing becomes invisible infrastructure.
"Our comprehensive solution to the Storage Networking Industry Association's identified challenges for AI infrastructure. Bridging the gap between storage systems and AI workload requirements.
SNIA identified the critical challenges facing storage infrastructure in the AI era. We've architected solutions that directly address these gaps — from GPU-direct storage access to intelligent tiering for KV-cache offloading. This is our answer to the industry's call.
"After 31+ years building enterprise systems and deep work in agentic AI, I've seen what happens when AI agents go to production without proper security guardrails. AI agents interpret natural language (vulnerable to manipulation), retrieve sensitive data, interact with external systems — and traditional security models weren't designed for this attack surface.
A single prompt injection can compromise your entire agent workflow. That's why we built Verified AI Agent Security — a Rust-based SDK that wraps your AI/LLM calls with enterprise-grade security controls. Security isn't a feature. It's the foundation that makes everything else possible.
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