Cluster Hub
Silicon, JAX, Networking (NCCL/Ring bottlenecks), TPUs, GPU optimization, and Deep Tech.
The bottleneck for LLMs is memory bandwidth, not compute. Discover how to use speculative decoding on GCP to achieve 3x speedups by using small "draft" models to accelerate massive "oracle" models.
CPU load is a trailing indicator for AI inference. Discover how to use libtpu metrics and the GKE Gateway API to build high-density, memory-aware traffic routing for TPUs.
Is your agent actually reasoning, or just lucky? Discover why trajectory analysis and synthetic red-teaming are the only ways to build production-grade autonomous systems.
Agents are stateless. Their memory is not. Scaling the LLM reasoning loop is trivial compared to solving the transactional concurrency of agent memory on Kubernetes.
When XLA's heuristics fail for custom attention mechanisms, you can't just hope for a compiler update. Here is how you write Triton-like kernels directly in Python using JAX Pallas.
Using a 'Draft' model costs 10% more VRAM but saves 50% Latency. Here is the mechanics of the gamble.
A war story of chasing a 5ms latency spike to a single loose thread. How to read Nsight Systems and spot Warp Divergence.
Recompilation is the silent killer of training throughput. If you see 'Jit' in your profiler, you are losing money. We dive into XLA internals.
The AI industry is shifting from celebrating large compute budgets to hunting for efficiency. Your competitive advantage is no longer your GPU count, but your cost-per-inference.
Explore how quantization and hardware co-design overcome memory bottlenecks, comparing NVIDIA and Google architectures while looking toward the 1-bit future of efficient AI model development.
In distributed training, the slowest packet determines the speed of the cluster. We benchmark GCP's 'Circuit Switched' Jupiter fabric against AWS's 'Multipath' SRD protocol.
As the AI industry moves from model training to large-scale deployment, the strategic bottleneck has shifted from parameter count to inference orchestration. This post explores how advanced...
The competitive advantage in AI has shifted from raw GPU volume to architectural efficiency, as the "Memory Wall" proves traditional frameworks waste runtime on "data plumbing." This article explains...
An end-to-end guide to orchestrating Custom Qwen3 pre-training on Google Cloud's Trillium TPUs. I dive into modifying the Qwen3 architecture for structured JSON outputs, leveraging XPK for...
As hardware lead times and power constraints hit a ceiling, the competitive advantage in AI has shifted from chip volume to architectural efficiency. This article explores how JAX, Pallas, and...
Google Cloud’s G4 architecture delivers 168% higher throughput by maximizing PCIe Gen 5 performance. This deep dive examines the engineering stack driving these gains, from direct P2P communication...
Understanding how to partition a single GPU into multiple isolated instances for cost-efficient AI workloads, with a deep dive into NVIDIA's MIG technology and the architectural differences between...
As organizations pivot from AI experimentation to enterprise-scale deployment, a recurring structural friction often emerges. Through my engagements with leadership teams in APAC, it has become clear...
Generative AI has shifted data center traffic patterns, making network performance the new bottleneck for model training. This post contrasts how the "Big Three" cloud providers utilize distinct...
Demystifying hardware acceleration and the competing sparsity philosophies of Google TPUs and Nvidia. This post connects novel architectures, like Mixture-of-Experts, to hardware design strategy and...
AI benchmarks are fundamentally broken, putting enterprise budgets at risk. This post deconstructs the technical flaws and outlines a strategy for building internal evaluations that actually predict...
This post contrasts the switching technologies of NVIDIA and Google's TPUs. Understanding their different approaches is key to matching modern AI workloads, which demand heavy data movement, to the...
It's not just about specs. This post breaks down the core trade-off between the GPU's versatile power and the TPU's hyper-efficient, specialized design for AI workloads.
A guide for technology executives on how to move beyond proofs-of-concept and realize sustainable, transformative value from agentic AI by focusing on business-first strategies.
Large-scale recommendation models involve a two-part process. First, a "sparse lookup" phase retrieves data from memory, a task that is challenging for standard GPUs. Second, a "dense computation"...
Technical debt is not new, This weekend I went down the trail to read-up on its impact due to the increased throughput of code generation thanks to AI. Turns out AI code generation is a double-edged...