QumulusAI - Breaking AI’s Biggest Barriers
QumulusAI is a fully integrated AI infrastructure solution, encompassing the entire stack—from high-performance computing clouds to both on- and off-grid data centers powered by natural gas generation. Our scalable, energy-efficient solutions eliminate computational bottlenecks in AI development, ensuring enterprises and innovators have the compute resources they need, when they need them. With QumulusAI, development teams train models faster, deploy smarter, and push the limits of AI innovation.
Industry IQ
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…
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…
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…
Client Stories
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…