Concepts
50 articlesEdge 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…
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…
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…
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,…
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…
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,…
KV-Cache Reuse and TTFT: Why Inference Latency Gets Unstable at Scale
Time-to-first-token degrades under concurrency when the KV cache is evicted and recomputed. Published aiDAPTIV+ figures show average TTFT falling from 250 ms to 78 ms with cache reuse, and variable…
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…
Scale-Up Domains and the Fabric Boundary: Designing Past One Rack
Every rack-scale system has a boundary where fast NVLink ends and slower scale-out networking begins. Where you place that boundary relative to your model's parallelism determines achieved throughput more than…
Continual Pre-Training: When Fine-Tuning Is Not Enough
Fine-tuning teaches behaviour; continual pre-training teaches a language or a domain's underlying distribution. It costs orders of magnitude more and risks catastrophic forgetting. This sets out when it is genuinely…
Content Provenance and Deepfake Detection: GPU Planning for Broadcast
Provenance signing is cheap and reliable; deepfake detection is expensive and unreliable. Broadcasters should invest in C2PA-style credentials for their own output and treat detection as a triage aid, not…
Compute Budgeting After the Pre-Training Plateau
Frontier compute is shifting from pre-training toward post-training and inference-time reasoning. That changes how an enterprise budgets GPU capacity: fewer giant runs, more RL loops and evaluation, and a different…
Training Goodput at 10,000 GPUs: Failures, MFU and Honest Throughput
Llama 3 pre-training on 16,384 GPUs saw 466 interruptions in 54 days. At that failure rate, the number that matters is goodput, not peak FLOPS. This explains MFU, failure modes,…
Object Storage Enters the Training Loop: S3-over-RDMA in 2026
Object storage used to be the cold tier behind a parallel filesystem. In 2026, S3-over-RDMA and DPU-resident object stores put it directly in the GPU data path, which changes how…
800 VDC Power: Preparing Training Halls for Megawatt AI Racks
Rack power went from about 40 kW in the Hopper era to roughly 120 kW with Blackwell, and 800 VDC distribution arrives with megawatt racks from 2027. This explains what…
AI Dubbing at OTT Scale: GPU Pipelines for Indian-Language Localisation
AI dubbing runs five GPU stages - ASR, translation, voice synthesis, lip-sync rendering and QC - cutting localisation cost up to 10x as regional languages pass 60% of Indian OTT…
Shop-Floor Copilots: On-Prem RAG for Maintenance and SOP Knowledge
An industrial copilot is domain RAG over SOPs, manuals and maintenance history with voice and multilingual support for the floor. A 2-4 GPU per-plant server carries the load; the real…
Disaggregated Inference: Splitting Prefill and Decode in 2026
Prefill is compute-bound, decode is memory-bound, and 2026 serving stacks split them onto dedicated GPU pools with KV cache shipped between - worth 2-3x throughput at node scale and far…
The Air-Cooled Middle: RTX PRO Servers Between Workstation and HGX
MGX-based RTX PRO servers pack up to eight 96 GB GDDR7 GPUs - 768 GB aggregate - into standard air-cooled racks, serving multiple 70B-class replicas plus rendering and vGPU duty…
Adaptive RAG in 2026: Routing Between Hybrid, Graph and Agentic Tiers
Enterprise RAG in 2026 is a routed portfolio: hybrid retrieval absorbs most lookups, GraphRAG handles cross-document reasoning at heavy index-time cost, and agentic loops multiply tokens 5-20x per hard question.…
KV Cache Offloading: The New Storage Tier in AI Inference Servers
Long-context and agentic inference made the KV cache a storage problem: about 300-350 KB per token on 70B-class models spills from VRAM to DRAM, NVMe and shared tiers, with LMCache-class…
FREE-AI and Model Risk Rules: Infrastructure Consequences for Banks
RBI FREE-AI (2025) and the draft 2026 Model Risk Management guidance make Indian bank AI examinable: inventoried models, validation environments, challenger serving, full logging and rollback. Plan 1.3-1.6x naive serving…
Self-Hosted Coding Assistants: Running Coder LLMs on Entry Workstations
Open 24-32B coder models now benchmark near commercial APIs and run on a 24-32 GB entry workstation with 30-60 ms local completion latency cloud endpoints cannot match. For Indian services…
Entry AI Workstations in 2026: What 16, 24 and 32 GB Really Run
At the entry tier, VRAM decides everything: 16 GB is a learning machine, 24 GB a value point for 7-13B work, and 32 GB comfortably serves quantised 30B models and…
Day-2 Operations for Rack-Scale AI: Failures, Monitoring and Service
Dense GPU systems fail as routine - Meta logged 419 interruptions in 54 days at 16k-GPU scale - so rack-scale readiness means DCGM-based trend monitoring, 30-60 minute checkpoint cadence, trained…
Token Economics for Rack-Scale AI: Cost per Million Tokens, Honestly
Owned token cost is amortised capex, power, facility and ops divided by tokens actually served - utilisation dominates. Vendor multipliers like 35x vs Hopper assume saturated FP4 reasoning workloads; benchmark…
Buy Blackwell Ultra or Wait for Rubin? Flagship Timing for 2026-27
Vera Rubin is in production with cloud shipments from H2 2026, but enterprise racks realistically land in 2027. Deployed GB300-class output for 12-18 months usually beats the successor delta; wait…
Hosting 100 kW Racks in India: Facility Readiness for Rack-Scale AI
A 120 kW NVL72-class rack exceeds most legacy Indian hall designs, so facility readiness is the long pole: direct-to-chip liquid cooling, 415 V high-amperage feeds, 2-tonne floor loading and contracted…
AI Workstation TCO in India: Duties, GST and the Cloud Crossover
GPU hardware enters India at 0% basic duty under ITA-1, and the 18% IGST is input-creditable, so the buy-vs-rent question is pure utilisation arithmetic. Against $2-3/GPU-hour cloud rates, a daily-driver…
From Desk to Server Room: When a Team Outgrows AI Workstations
Four signals say a team has outgrown workstations: GPU queueing, duplicated model weights, uptime needs and VRAM ceilings. Measure two weeks of utilisation, then buy a boring first server -…
One Card, Two Pipelines: Hybrid Rendering and AI Workstations in 2026
Rendering and AI converged on the same silicon: 96 GB Blackwell workstation cards run V-Ray by day and diffusion or 70B inference overnight, while DLSS 4 and neural texture compression…
Fine-Tuning LLMs on a Workstation: LoRA and QLoRA Memory Math
Fine-tuning memory is weights plus gradients, optimiser states and activations. QLoRA needs about 12 GB for 7B, 44 GB for 32B and 88 GB for 70B, so a 96 GB…
The 96 GB Desk-Side Tier: What a Mid-Range AI Workstation Runs in 2026
A 96 GB-class workstation card (RTX PRO 6000 Blackwell: 24,064 CUDA cores, ECC GDDR7 at ~1.8 TB/s) runs quantised 70B inference and 7-32B fine-tuning on a desk. The ceiling: unquantised…
Small Language Models and the NPU Myth: What Actually Runs Locally in 2026
A 3–9B model now carries most of an agentic loop locally, faster and more privately than a cloud API. But the NPU is not what runs it: Ollama, llama.cpp and…
Local AI Workstations in 2026: Running 70B Models at Your Desk
With 96 GB of GDDR7 on a single RTX PRO 6000 Blackwell card, a desk-side workstation can now hold a 70B model at Q8 — and two Max-Q cards pool…
Blackwell Ultra to Vera Rubin to Feynman: The 2026–2028 AI Training Cluster Roadmap
NVIDIA now ships one AI architecture per year: Blackwell Ultra today, Vera Rubin from H2 2026, Rubin Ultra in 2027, Feynman in 2028. For most training clusters the deciding factor…
AI-Citation-Ready GPU Server FAQ for RDP GPU Mart
Choosing a GPU server for AI workloads requires matching memory capacity, interconnect bandwidth, and compliance posture to your specific pipeline. This reference answers the questions AI engineers and procurement teams…
GPU Cluster Networking for Training and Fine-Tuning
GPU cluster networking is the critical path for distributed training and fine-tuning: the interconnect fabric between GPUs determines whether your cluster scales linearly or stalls on collective communication. For workloads…
BFSI Private AI GPU Server Controls and Auditability
The BFSI sector requires robust AI GPU server controls and auditability to ensure compliance with regulations and manage risks effectively. Leveraging advanced GPUs like the NVIDIA H200 can enhance performance…
Healthcare AI Infrastructure Readiness in India
Healthcare AI infrastructure needs data-governance, imaging throughput, storage retention, and uptime planning before model choice. For Indian providers, RDP GPU Mart can frame GPU servers and storage as a governed…
Best On-Prem Setup for a Startup Training Small LLMs (<13B)
A startup working with sub-13-billion-parameter models needs far less hardware than the headlines suggest: a single GPU workstation with 48–141 GB of VRAM handles fine-tuning (QLoRA/LoRA) and inference for models…
Media & Entertainment: GPU for Rendering + Generative AI (India)
GPUs now sit inside the production pipeline, not beside it. Indian studios use them for rendering, AI denoising, real-time in-camera VFX, and generative video — with executives expecting 80–90% efficiency…
Government & PSU AI: Data-Residency-First GPU Infrastructure (India)
Government and public-sector AI in India is sovereignty-first: workloads must run on in-country, DPDP-aligned infrastructure, and for many departments on MeitY-empanelled platforms specifically. That points to on-prem GPU clusters or…
AI Infrastructure for Manufacturing: Vision + Predictive Maintenance (India)
Indian manufacturers have moved AI from pilot to plant floor: GPU-powered computer vision drives zero-defect quality control, and machine-learning models predict equipment failure before it happens. Because these run at…
On-Prem AI for BFSI: Running Fraud & Risk Models In-House (India)
Indian banks and financial firms increasingly run fraud and risk AI on their own GPU infrastructure — because RBI data-localization, the DPDP Act, and PCI-DSS require transaction data to stay…
On-Prem GPU for Healthcare AI & Medical Imaging (India)
Indian hospitals are running AI inside live diagnostic workflows — and most keep it on-premises. On-prem accounted for ~58% of healthcare-AI deployments in 2025, because patient data (PHI) is treated…
FP8 / FP4 Explained: Precision, Throughput & Cost Trade-offs
Precision is how many bits represent each number in a model. Lowering it from FP16 to FP8 to FP4 roughly halves memory and increases throughput at each step, letting the…
HBM3e & GPU Memory: Why It Decides Your Model Size
A GPU's high-bandwidth memory (HBM) sets two things: capacity decides which models fit, and bandwidth decides how fast they run. Because large language models are memory-bound — their speed is…
Sovereign AI in India: What It Takes to Build In-Country GPU Infrastructure
Sovereign AI means a nation can train, run, and govern AI on infrastructure, data, and models it controls in-country. India is building it fast — the IndiaAI Mission has deployed…