Chainzano Blog
Articles
Browse published Chainzano materials by topic, level and editorial date.
Choosing AI Precision: BF16, FP8, INT8 and INT4
Lower numeric precision can reduce memory use and increase throughput, but each format changes model quality, hardware support and operating risk. Selection requires measured evidence.
How GPU Fabrics Scale: NVLink, InfiniBand and Ethernet
AI clusters use several network layers for different distances and traffic patterns. A sound fabric design matches each layer to the workload, topology and growth plan.
How to Commission an AI and HPC Cluster
Commissioning turns installed equipment into accepted capacity. A structured test sequence proves physical safety, component health, fabric performance, workload behavior and recovery before service starts.
Inside a Production Model-Serving Stack
A production AI endpoint needs routing, model workers, scheduling, observability and controlled releases. Each layer must protect response quality while using available accelerator capacity efficiently.
Measure Useful AI Capacity, Not GPU Activity
A busy accelerator does not prove that an AI service meets its goal. Capacity planning must connect infrastructure use with completed work, latency, quality and demand.
Predictive Operations for AI and HPC Infrastructure
AI and HPC systems produce many repeated events around a smaller set of real risks. Predictive operations needs evidence control, pattern learning and bounded action paths.
Sharing GPUs Without Losing Control
Shared accelerator pools can improve use and access, but isolation, placement and service policy must remain clear. The correct method depends on workload behavior and risk.
Storage Must Keep Expensive Accelerators Fed
Fast accelerators cannot deliver useful performance while they wait for data. Storage design must follow the complete path from source data to memory, checkpoints and results.
What the Full Cost of AI Infrastructure Includes
Accelerator price is only one part of an AI platform budget. Power, cooling, network, storage, facilities, software, people and idle risk shape the full cost.
Why AI Infrastructure Is Moving to Rack-Scale Systems
Modern AI clusters depend on compute, network, cooling and power as one system. Rack-scale design makes these dependencies explicit before equipment reaches the data center.
Private Knowledge Is the Missing Layer for Local LLMs
Local LLMs need more than model weights. They need trusted, permission-aware private knowledge that can be retrieved close to the user, workflow and data.
Distributed Inference Is an Orchestration Problem, Not Just a GPU Problem
Adding GPUs is not enough for scalable AI inference. Distributed inference needs routing, telemetry, cache awareness, local data access and controlled fallback paths.