Industry veteran with over 10 years of experience architecting and managing GPU-powered cloud solutions.
Specializes in enabling scalable AI/ML and HPC workloads for enterprise and research applications.
Former lead solutions architect for top-tier cloud providers and startups in the AI infrastructure space.
Anthropic describes Claude Opus 4.7 as the most capable model for complex reasoning and agentic coding. 1M token context window, 128k max output tokens, and adaptive thinking. Claude Opus 4.7 has landed exactly at the right moment. In 2026, the AI conversation has moved past novelty and into execution. Teams want Large Language Models that […]
Artificial intelligence launches come and go, but every so often a model release tells a much bigger story about where a company is heading. Meta Muse Spark is one of those moments. Announced on April 8, 2026, Muse Spark is the first model from Meta Superintelligence Labs, the unit Meta built after reshaping its AI strategy […]
GPU infrastructure for EdTech AI no longer requires upfront hardware investment. Today, teams can use cloud GPUs, serverless inference, and managed endpoints to launch AI features without taking on CapEx first. That matters because EdTech demand is rarely steady. Usage can spike during school hours, exam seasons, enrollment periods, and district-wide rollouts. Buying hardware too early […]
FinOps for AI workloads is no longer just a finance-side concern. It is a daily operational necessity because GPU demand, token usage, inference traffic and supporting services can spike faster than teams can explain or control the cost. As AI moves from experimentation into production, even a single training run, traffic surge or poorly governed […]
If you are choosing cloud GPUs in India for surveillance analytics, the biggest risk is not underbuying performance. It is overpaying for the wrong kind of compute. The real decision is choosing a platform that can handle real-time object detection, centralized analytics for edge-originated video streams, multi-camera inference, video search and model training without creating latency, scaling […]
RAG pipelines break in production. But not dramatically. There will be no loud errors, no obvious crashes. They just quietly degrade. Retrieval quality drops. Embeddings go stale. Vector indexes bloat up. And by the time someone notices, your LLM is confidently answering questions with outdated or corrupted context. If you’re running a RAG system at […]
Choosing the right cloud platform for agentic AI has become a critical decision for enterprises moving beyond pilots and into production-grade automation. As autonomous agents take on more complex workflows, success depends on more than model quality alone. Teams also need the right runtime for model access or model serving, orchestration, security, observability, networking and cost control. Deloitte reports […]
AI has gone mainstream. In 2026, the question is which AI platform actually makes sense for your specific situation. That shift matters more than people realize, because these platforms are not really competing against each other on the same terms. Let’s see how. Sarvam AI is not trying to be a cheaper ChatGPT or a regional […]
Quick Answer HGX B300 makes the strongest case when HBM capacity, KV cache, attention-heavy reasoning or scale-out networking limits your workload. HGX B200 remains compelling when 180 GB per GPU is sufficient or FP64-heavy HPC matters. For inference, compare cost per delivered token at your target latency and concurrency, not GPU-hour alone. B300’s premium […]
If you have ever watched a GPU-render crawl at two percent progress while your deadline sprints toward you, you already know the real enemy is rarely compute-related. On modern path tracers, the more common failure mode is memory pressure like: The NVIDIA RTX PRO 6000 Blackwell Workstation Edition changes the ceiling in a big way. With 96GB […]