Products & Installation
98 articlesHow to Choose Edge AI Silicon: A Vendor-Agnostic Decision Framework
A vendor-agnostic framework for choosing edge AI silicon by constraint order: power envelope, model type, stream count, latency determinism, toolchain maturity, form factor and supply longevity. Includes a decision table…
Specialist Edge AI Silicon: Coral, Ambarella, SiMa.ai, Kneron and Beyond
A survey of specialist edge AI silicon beyond Hailo, Axelera and Blaize: Google Coral Edge TPU, Ambarella CVflow vision SoCs and N1, SiMa.ai MLSoC and Modalix, Kneron, DEEPX DX-M1, EdgeCortix…
Qualcomm, Intel and AMD at the Edge: NPUs, OpenVINO and Adaptive SoCs
How the three big incumbent semiconductor vendors play edge AI: Qualcomm Dragonwing NPU platforms, Intel Core Ultra NPUs with OpenVINO and the Movidius lineage, and AMD Versal AI Edge, Kria…
NVIDIA Jetson Deep Dive: Orin Family and Jetson Thor for Edge AI
A technical deep dive on NVIDIA Jetson for edge AI: Orin Nano, Orin NX and AGX Orin specifications, the Blackwell-generation Jetson Thor modules, configurable power modes, the JetPack, CUDA and…
The Edge AI Silicon Landscape in 2026: Incumbents vs Specialists
A vendor-neutral map of edge AI silicon in 2026: NVIDIA, Qualcomm, Intel, AMD and Google versus specialists such as Hailo, Axelera, Blaize, Ambarella and SiMa.ai. Segments the field by architecture,…
Deploying Edge AI Fleets in India: Provisioning, OTA and DPDP
An operations playbook for running hundreds of edge AI inference endpoints across India: golden-image provisioning, OTA model updates and rollback, drift monitoring, connectivity and cabinet thermal realities, spares strategy, and…
Edge AI Quantisation: INT8, INT4 and Vendor Toolchains
How quantisation actually works on edge AI accelerators: INT8 vs INT4 trade-offs, post-training quantisation vs quantisation-aware training, building calibration datasets, measuring accuracy loss honestly, and why compiled-model performance varies far…
Blaize GSP Deep Dive: Graph Streaming for Low-Latency Edge AI
A technical deep dive into the Blaize Graph Streaming Processor: how depth-first graph execution across 16 cores keeps activations on-chip, the reported 16 TOPS at ~7 W envelope, company-published bandwidth…
Axelera Metis Deep Dive: Digital In-Memory Compute for Edge Vision
A technical deep dive into the Axelera Metis AIPU: the digital in-memory computing architecture behind its published 214 TOPS (INT8) peak and ~15 TOPS/W efficiency, quad RISC-V dataflow cores, M.2…
Hailo-8 and Hailo-10H Deep Dive: Dataflow Silicon for Edge Inference
A technical deep dive into the Hailo-8 and Hailo-10H edge AI accelerators: the structure-driven dataflow architecture, vendor-stated TOPS and power figures with their precision caveats, the Hailo-10H direct DDR interface…
Tiering Architecture for AI Data: Hot NVMe, Warm Capacity, Cold Object
A sizing guide for the three-tier AI data hierarchy: a hot TLC NVMe tier sized to the active working set, a warm QLC capacity tier, and cold object storage for…
Benchmarking AI Storage: IO500, fio, mdtest, and MLPerf Storage
A how-to for measuring AI storage honestly: what IO500, fio, mdtest, IOR, and MLPerf Storage each actually test, which numbers predict training performance, the standard ways vendor quotes mislead, and…
Local NVMe Scratch vs Shared Storage: Data Locality for GPU Nodes
When node-local NVMe beats the shared filesystem, and how to use both well. Covers staging patterns, scratch sizing per GPU node, cache tiers that hydrate automatically, what belongs where, and…
Erasure Coding vs Replication for AI Datasets: Choosing per Data Class
Replication is fast and simple; erasure coding turns 33 percent usable capacity into 75 percent or better. This comparison works through durability maths, rebuild behaviour, write amplification on flash, and…
Storage Network Design for AI Clusters: Dedicated vs Converged Fabric
Should storage traffic share the GPU compute fabric or get its own network? A reference-architecture view of the three fabrics in an AI cluster, when convergence is safe, when checkpoint…
The Small-File and Metadata Problem in AI Training Storage
Why datasets of millions of small files stall GPU training even on storage that benchmarks at hundreds of GB/s. Covers the per-file metadata cost, how metadata servers scale in Lustre…
All-Flash NVMe Design for AI: TLC vs QLC, DWPD, and EDSFF Form Factors
A buyers guide to the flash layer of an AI storage tier: choosing TLC vs QLC NAND, reading DWPD endurance ratings against checkpoint-heavy write patterns, picking between U.2, E1.S, and…
NVMe over Fabrics for AI Storage: RoCE vs TCP vs Fibre Channel
How NVMe over Fabrics disaggregates flash for AI clusters, and how to pick a transport. Compares NVMe/RDMA over RoCEv2, NVMe/TCP, and NVMe/FC on latency, network requirements, and operational complexity, with…
Parallel Filesystems Compared: Lustre, BeeGFS, WEKA, Storage Scale, DAOS
A working comparison of the five parallel filesystems that dominate AI cluster shortlists: Lustre, BeeGFS, WEKA, IBM Storage Scale, and DAOS. Covers architecture, metadata design, small-file behaviour, licensing, and the…
Incident Response for AI Clusters: Containment, Forensics and Notification Duties
GPU-cluster incidents look different: cryptojacking, stolen pipeline credentials, model exfiltration - and containment decisions collide with week-long training runs. How to contain without destroying work in progress, what forensics on…
Network and Access Security for GPU Clusters: Segmentation, Jump Hosts and Secrets
A GPU cluster concentrates an organisation's most valuable data, most expensive compute and most privileged credentials in one place. A reference security architecture: network zones, RDMA fabric exposure, jump-host access…
Sector Rules Beyond DPDP: RBI, Health Data and Government Procurement
DPDP is the general law, but the binding constraints on AI infrastructure often come from sector instruments: RBI's payment data direction and FREE-AI framework, ABDM and EHR standards in health,…
Supply Chain and Firmware Integrity for GPU Servers: Secure Boot to Counterfeits
A GPU server's trustworthiness is decided before the OS boots. How to verify hardware provenance and avoid grey-market and counterfeit GPUs, what secure boot and signed firmware actually attest, why…
Model and Data Governance On-Prem: Registries, Lineage and Approval Gates
Governance is what separates a compliant private AI platform from a fast one that fails review. How a model registry, dataset lineage and approval gates work on owned GPU infrastructure,…
Audit Evidence for AI Systems: What Auditors Ask For and How to Build It In
AI audits fail on evidence, not intent. What an auditor actually requests from an AI system - access records, change history, dataset lineage, model documentation, retention proof - and how…
Data Residency Architectures for AI: Where Personal Data May and May Not Flow
DPDP permits cross-border transfer except to restricted countries, so real residency duties come from sector overlays - RBI payment data, government workloads, contracts. Three reference architectures for keeping personal data…
Tenant Isolation on Shared GPUs: MIG, vGPU and Time-Slicing Compared
MIG partitions memory, cache and compute in hardware; vGPU adds hypervisor mediation; time-slicing and MPS share one memory space and are not security boundaries. What each mechanism does and does…
The DPDP Act for AI Infrastructure: What It Requires and What It Does Not
The DPDP Act 2023 and the Rules notified in November 2025 govern how personal data in AI systems is secured, reported and erased - but they do not mandate blanket…
The First 90 Days of an AI Cluster: Early-Life Failures, Spares and Warranty
What actually fails in an AI cluster's first 90 days - GPUs and HBM, optics, NVMe, PSUs - and how to prepare: a spares kit sized to your scale, warranty…
AI Cluster Handover and Documentation: What to Demand From Your Integrator
The complete handover pack to demand before your integrator leaves site: as-built rack elevations and rail maps, firmware and serial inventories, switch and BIOS configuration exports, runbooks, credentials and licences,…
Storage and Data-Path Validation: Proving Storage Will Not Starve Your GPUs
How to validate storage during AI cluster commissioning: fio and gdsio test recipes, per-GPU read bandwidth targets, checkpoint write burst testing, metadata and small-file checks, and the combined GPU-plus-storage soak…
GPU Health on Day 1: DCGM Diagnostics, XID Errors and the Baseline to Record
How to establish a GPU health baseline on day one of a new cluster: DCGM diagnostic levels and what each catches, the XID error codes that matter, ECC and row-remapping…
Commissioning a Liquid-Cooled AI Rack: Fill, Leak Test and Coolant Sign-Off
The commissioning sequence for a direct-to-chip liquid-cooled AI rack: pre-fill flush, pressure decay and wet leak testing, fill and air purge, CDU setpoints, and the coolant chemistry numbers - pH,…
Site Readiness for AI Rack Delivery: Power, Floor Loading, Access and Staging
A pre-delivery site readiness checklist for heavy AI racks in India: feeder and breaker capacity, floor loading and point loads, door widths and lift limits along the access route, network…
NCCL Tests and Collective Benchmarks: Validating an AI Fabric Before Production
How to use nccl-tests to prove a GPU fabric works: what bus bandwidth means, the numbers a healthy NVLink and 400G RDMA fabric should hit, how to scale tests from…
Burn-In and Validation for GPU Clusters: Catching Failures Before Production
How to burn in a new GPU cluster: which stress tools to run, how long a thermal soak really needs, what infant-mortality failures look like in logs, and the exit…
Acceptance Testing an AI GPU Cluster: What to Verify Before Sign-Off
A practical acceptance-test framework for AI GPU clusters: the four test layers, the HPL and nccl-tests numbers to demand, DCGM health gates, and a sign-off matrix that defines who signs…
Cabling and Optics for AI Fabrics: DAC, AOC, 800G Transceivers and Link Budgets
Cabling is where AI fabrics quietly fail. This guide compares DAC, AEC, AOC and pluggable 800G optics by reach, power and failure mode, explains link budgets and pre-FEC margin, shows…
ConnectX-8 SuperNICs and BlueField DPUs: NIC, SuperNIC and DPU Roles Explained
SuperNICs and DPUs solve different problems: ConnectX-8 moves GPU traffic at 800G with minimal latency, while BlueField DPUs run infrastructure software on their own cores. This article defines NIC vs…
Spectrum-X and Ultra Ethernet vs InfiniBand: Where AI Fabrics Head in 2026-2027
Ethernet is mounting a serious counter-attack on InfiniBand for AI clusters. This article examines what Spectrum-X actually adds over plain Ethernet, where the Ultra Ethernet Consortium stands after Specification 1.0,…
Rail-Optimised Topology in Depth: How Rails Map GPUs to Leaf Switches
Rail-optimised topology wires NIC k of every GPU server to the same leaf switch, so same-rank collective traffic completes in one hop. This article explains the GPU-to-leaf mapping, why it…
AI Fabric Fundamentals: Scale-Up, Scale-Out and Why Networks Decide GPU Utilisation
In a GPU cluster the network is not plumbing, it is part of the compute. Scale-up versus scale-out domains, why collective operations run at the speed of the slowest link,…
CDUs Explained: Sizing Coolant Distribution for 50-200 kW AI Racks
A CDU is the pump, heat exchanger and control loop between your GPU cold plates and the building. Liquid-to-liquid versus liquid-to-air, in-rack, in-row and sidecar options, and why supply temperature…
Open Rack v3 (ORv3) Explained: Busbars, Power Shelves and 21-Inch Trays
ORv3 replaces per-server AC cords with a rack-level 48 V DC busbar fed by centralised power shelves, and adds blind-mate connections plus 21-inch tray support. What changes mechanically and electrically,…
Open Compute Project Explained: What OCP Means for AI Infrastructure Buyers
OCP is an open hardware standards community whose rack, power and cooling specifications now shape how high-density AI systems are built. What OCP is, which specifications matter for AI, and…
Hailo vs Axelera vs Blaize: Choosing Edge Inference Silicon in 2026
Hailo-10H, Axelera Metis and Blaize GSP solve edge inference three different ways: ultra-low-power NPU with local DRAM, high-TOPS vision AIPU, and graph-native dataflow. A published-specification comparison and a decision frame…
Edge AI Accelerators vs GPUs: When a GPU Is the Wrong Answer for Inference
A GPU is built for batched throughput in a rack. Edge inference is the opposite problem: one stream, low latency, single-digit watts. Purpose-built accelerators from Hailo, Axelera and Blaize target…
PCIe Gen6 and 800G SuperNICs: What the 2026 Server Refresh Changes
ConnectX-8 combines an 800G NIC with an integrated PCIe Gen6 switch in one device, exposing 48 Gen6 lanes from an x16 connector. That consolidation removes components from AI server designs…
Workstation Thermals and Power in Indian Offices: Planning for 600 W Cards
A modern AI workstation can draw over a kilowatt at the wall and runs at that level for hours, not seconds. In an Indian office that is a thermal and…
Local RAG on a Mid-Tier Workstation: Index Size and Honest Limits
A mid-tier workstation can host a genuinely useful private RAG system over a few hundred thousand documents. This sets out where the memory goes between generation model, embeddings and index,…