Industry IQ – QumulusAI
Microsoft’s introduction of the Maia 200 adds to a growing list of hyperscaler-developed processors, alongside offerings from AWS and Google. These custom AI chips are largely designed to improve inference efficiency and optimize internal cost structures, though some platforms also support large-scale training. Google’s offering is currently the most mature, with a longer production…
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Industry IQ - QumulusAI
OpenAI–Cerebras Deal Signals Selective Inference Optimization, Not Replacement of GPUs
OpenAI’s partnership with Cerebras has raised questions about the future of GPUs in inference workloads. Cerebras uses a wafer-scale architecture that places an entire cluster onto a single silicon chip. This design reduces communication overhead and is built to improve latency and throughput for large-scale inference. Mark Jackson, Senior Product Manager at QumulusAI, says…
Industry IQ - QumulusAI
NVIDIA Rubin Brings 5x Inference Gains for Video and Large Context AI, Not Everyday Workloads
NVIDIA’s Rubin GPUs are expected to deliver a substantial increase in inference performance in 2026. The company claims up to 5 times the performance of B200s and B300s systems. These gains signal a major step forward in raw inference capability. Mark Jackson, Senior Product Manager at QumulusAI, explains that this level of performance is…
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QumulusAI Brings Fixed Monthly Pricing to Unpredictable AI Costs in Private LLM Deployment
Unpredictable AI costs have become a growing concern for organizations running private LLM platforms. Usage-based pricing models can drive significant swings in monthly expenses as adoption increases. Budgeting becomes difficult when infrastructure spending rises with every new user interaction. Mazda Marvasti, CEO of Amberd, says pricing volatility created challenges as his team expanded its…
Amberd Moves to the Front of the Line With QumulusAI’s GPU Infrastructure
Reliable GPU infrastructure determines how quickly AI companies can execute. Teams developing private LLM platforms depend on consistent high-performance compute. Shared cloud environments often create delays when demand exceeds available capacity Mazda Marvasti, CEO of Amberd, says waiting for GPU capacity did not align with his company’s pace. Amberd required guaranteed availability to support…
QumulusAI Secures Priority GPU Infrastructure Amid AWS Capacity Constraints on Private LLM Development
Developing a private large language model(LLM) on AWS can expose infrastructure constraints, particularly around GPU access. For smaller companies, securing consistent access to high-performance computing often proves difficult when competing with larger cloud customers. Mazda Marvasti, CEO of Amberd AI, encountered these challenges while scaling his company’s AI platform. Because Amberd operates its own…
Custom AI Chips Signal Segmentation for AI Teams, While NVIDIA Sets the Performance Ceiling for Cutting-Edge AI
Microsoft’s introduction of the Maia 200 adds to a growing list of hyperscaler-developed processors, alongside offerings from AWS and Google. These custom AI chips are largely designed to improve inference efficiency and optimize internal cost structures, though some platforms also support large-scale training. Google’s offering is currently the most mature, with a longer production…
