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SServers99servers99.hashnode.dev4d ago · 6 min read

Bare Metal Automation in 2026: A Blueprint for Provisioning and Lifecycle Management

Bare metal servers remain the critical foundation for workloads that demand predictable performance, strict hardware isolation, and zero-overhead compute power. In 2026, as enterprise AI platforms, hi

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MSManu Shuklaecorpit.hashnode.dev6d ago · 12 min read

India's 1.9 GW data centre reality in 2026: reconciling the capacity and dollar numbers before you host AI workloads

India's 1.9 GW data centre reality in 2026: reconciling the capacity and dollar numbers before you host AI workloads Summary. India's installed data centre capacity reached about 1.9 GW in FY26, up fr

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UTUjjwal Tripathimicrocosmworks.hashnode.dev6d ago · 6 min read

The Hidden Infrastructure Costs of AI Applications (and How to Reduce Them)

When businesses discuss the cost of Artificial Intelligence, the conversation usually starts with model pricing. Teams compare token costs, evaluate subscription plans, and debate whether GPT, Claude,

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MSManu Shuklaecorpit.hashnode.devJul 22 · 17 min read

AMD Helios vs NVIDIA Vera Rubin: 7 spec traps in 2027 AI rack quotes

AMD Helios vs NVIDIA Vera Rubin: 7 spec traps in 2027 AI rack quotes Summary. AMD's Helios page lists 72 Instinct MI455X GPUs, 2.9 exaFLOPS of FP4, 1.4 exaFLOPS of FP8, 31 TB of HBM4 and 260 TB/s of s

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SGSergio González Téllezevankhandev.hashnode.devJul 20 · 3 min read

Why do AI systems with increasingly sophisticated models continue to produce mediocre or inconsistent results?

SEED-011 PROBLEM Why do AI systems with increasingly sophisticated models continue to produce mediocre or inconsistent results? INSIGHT A well-modeled, validated, and governed data lake often contri

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STSakshi Tyagisakshityagi.hashnode.devJul 19 · 3 min read

Beyond 1D Data Parallelism: ZeRO, TP, PP, and CP

Part 4 of 4 , Scaling LLM Training. Code for the series: github.com/rocks-saka/Scaling-llm-training The previous three posts got us a long way on a single axis: map the memory, recompute activations,

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MSManu Shuklaecorpit.hashnode.devJul 19 · 14 min read

The 2026 AI compute crunch: a capacity-planning playbook for GPU and power limits

The 2026 AI compute crunch: a capacity-planning playbook for GPU and power limits Summary. Gartner expects power availability to operationally constrain 40% of existing AI data centers by 2027, with t

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LWLearn with HJblog.hardeepjethwani.comJul 11 · 6 min read

Prompt Caching and Cost Optimization: Make AI Cheaper Without Making It Worse

🚀 Prompt Caching and Cost Optimization: Make AI Cheaper Without Making It Worse 👋 Welcome to Day 73 of 90 Days of AI. 🎯 Today we are tackling Prompt Caching and Cost Optimization: Make AI Cheaper

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LWLearn with HJblog.hardeepjethwani.comJul 11 · 6 min read

AI Gateways: One Door for Models, Costs, Logs, and Sanity

🚀 AI Gateways: One Door for Models, Costs, Logs, and Sanity 👋 Welcome to Day 72 of 90 Days of AI. 🎯 Today we are tackling AI Gateways: One Door for Models, Costs, Logs, and Sanity. The mission is

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LWLearn with HJblog.hardeepjethwani.comJul 11 · 6 min read

The AI Infrastructure Stack: Chips, Memory, Power, and Patience

🚀 The AI Infrastructure Stack: Chips, Memory, Power, and Patience 👋 Welcome to Day 64 of 90 Days of AI. 🎯 Today we are tackling The AI Infrastructure Stack: Chips, Memory, Power, and Patience. The

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#ai-infrastructure

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Trending tags this week

Bare Metal Automation in 2026: A Blueprint for Provisioning and Lifecycle Management

India's 1.9 GW data centre reality in 2026: reconciling the capacity and dollar numbers before you host AI workloads

The Hidden Infrastructure Costs of AI Applications (and How to Reduce Them)

AMD Helios vs NVIDIA Vera Rubin: 7 spec traps in 2027 AI rack quotes

Why do AI systems with increasingly sophisticated models continue to produce mediocre or inconsistent results?

Beyond 1D Data Parallelism: ZeRO, TP, PP, and CP

The 2026 AI compute crunch: a capacity-planning playbook for GPU and power limits

Prompt Caching and Cost Optimization: Make AI Cheaper Without Making It Worse

AI Gateways: One Door for Models, Costs, Logs, and Sanity

The AI Infrastructure Stack: Chips, Memory, Power, and Patience