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vLLM or SGLang? Here’s the actual decision guide.
Through this blog series, we have covered two deep dives on both the inference engines, one on vLLM, one on SGLang, and one long comparison blog on why their results differ in the first place. If you’ve read all three, you already know enough to debate with me on the subject (I highly welcome it). But if you’re here…
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vLLM vs SGLang: Why Their Benchmarks Behave So Differently
Have you ever wondered how the same model on the same GPU configuration with the same quantization, yet one framework gives you a TTFT of 200ms and the other gives you 500ms. Nothing about the hardware or model changed still the same results can be observed. So what changed? This blog isn’t about which framework to pick. I already made…
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vLLM Solved the Wrong Bottleneck… and That’s Why It Won
Imagine spending thousands of dollars every month on GPUs, only to discover they’re sitting idle for a significant portion of the time. Strange, right? Surely the first thought would be: The transformers architecture is the bottleneck? Surprisingly, no. The real bottleneck wasn’t the transformer itself. It was how we managed memory and served hundreds of…
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Scaling Open Source LLMs with a vLLM Inference Server
When Inference Becomes an Infrastructure Problem A language model that performs well in testing can behave very differently once real users begin interacting with it. During development, most inference workloads are predictable. A handful of engineers submit prompts, response times remain stable, and GPU utilization appears manageable. Production environments introduce a different set of conditions.…
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Shelf Intelligence: How AI is Rewriting Retail Execution
A Camera on Every Shelf. Insights on None. That’s where most retail and manufacturing operations sit today. Not lacking data, but buried in it. The hard part beyond capturing information is in turning captured information into a decision fast enough to actually change something. Amit Srivastava, co-founder and CTO of Constems AI, puts it plainly…
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Virtual Machines vs Containers: How Modern Infrastructure Really Runs Applications
Introduction Generations of infrastructures have been shaped by a simple question: ‘How do you run software reliably and at scale on shared hardware?’ Virtual machines were among the first answers to this question, allowing teams to carve out isolated environments on physical servers and run multiple operating systems side by side. Containers arrived later, promising…
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Understanding sovereign AI
It is quite common to think that Sovereign AI is just about where your data lives. However, true sovereignty is about having control over your digital world, not just a local storage spot. This is significant for leaders to factor as they work with global data rules. Where a server sits on a map isn’t…
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Top 10 HPC Cloud Providers in India [2026]
India went from having no real high-performance computing option to having a genuine choice in about eighteen months. L&T is building a 10,000-GPU NVIDIA B300 cluster in Chennai for Together AI on an order worth roughly $1.57 billion. Google broke ground on a gigawatt-scale AI hub in Visakhapatnam under a $15 billion commitment. Microsoft committed…
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Top 10 GPU Cloud Providers in India
With AI, deep learning, and High-Performance Computing (HPC) driving modern innovations, GPU cloud computing is crucial. Instead of buying expensive GPUs, businesses can now rent High-performance GPU instances on-demand, making it affordable and scalable. Whether you’re an AI startup, a researcher training large models, or an enterprise running big data applications, choosing the right GPU…
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Top 10 AI PaaS Providers in India (2026)
AI PaaS Providers in India AI is now part of how businesses operate well beyond how they experiment. Models are being trained, deployed, and used across real workflows, which results in newer demands around infrastructure, scalability, and management. AI Platforms-as-a-Service (AI PaaS) emerged to address this shift. They provide a unified environment to build, deploy,…