Charg Cloud

Inference costs are dropping. Training costs are not. The companies building real AI infrastructure, not demos but actual production systems, are getting very deliberate about where they spend on compute. Charg exists because we think the market deserves a provider who takes that seriously. And because we control the supply chain behind it. Core 4 Solutions sources, recertifies, and serializes enterprise GPU hardware at scale. When supply gets tight, we have options other providers do not. That is not a pitch. It is just how we are built. #GPUCloud #AIInfrastructure #CloudCompute #Charg

Available for hardware purchase, hosted GPU capacity, or custom cluster deployments: Massive A100 capacity for cost-effective inference, fine-tuning, and legacy training workloads. 160x A100 40GB compute nodes • 8x A100 40GB SXM4 GPUs • 8x ConnectX-6 200GbE NICs • 2x AMD EPYC 64-core CPUs • 1TB DDR4-2933 RAM 96x A100 80GB compute nodes • 8x A100 80GB SXM4 GPUs • 8x ConnectX-6 200GbE NICs • 2x AMD EPYC 64-core CPUs • 2TB DDR4-2933 RAM Plus, 180x Mellanox MQM8700 Quantum HDR InfiniBand switches. That’s 2,048 A100 GPUs, 2,048 ConnectX-6 200GbE NICs, and 256 dense 8-GPU compute nodes. DM me if you’re looking for A100 capacity at scale. #GPUs #AIInfrastructure #A100 #Inference

It started as a crustacean joke. Six weeks later, OpenClaw had 150,000 GitHub stars and one of its AI agents had autonomously built a social network for other AI agents. Here's what it signals for compute infrastructure.

Nvidia isn't just selling chips anymore. It's financing the entire AI ecosystem to run on its hardware, and enterprises may not realize they're already inside it.

GPU availability is becoming just as important as GPU performance. We’re seeing more teams design around what they can actually get access to, not just what looks best on paper. Between: - long lead times - cloud constraints - budget pressure “Perfect” setups often give way to available and scalable ones. That’s where flexible, on-demand infrastructure starts to win. Availability is quietly becoming part of the architecture decision.

The gap between “cloud is easy” and “cloud is cost-effective” is getting wider. We’re seeing more teams start in the cloud (makes sense), but stick with it longer than they probably should. At a certain point: -workloads stabilize -usage becomes predictable -costs quietly compound That’s usually where a dedicated setup starts to make a lot more sense. We’re seeing more teams hit that inflection point lately.

Most teams don’t need the latest GPUs. They need the right setup. We’re seeing a lot of companies default to top-tier hardware (H100, etc.) when their workloads don’t actually require it. In many cases, a well-configured cluster with slightly older GPUs: - gets the job done - costs significantly less - available now The gap between “latest” and “right-sized” is where a lot of inefficiency lives.

On the ground at CloudFest. Conversations > slides.

CloudFest Europe is almost here. We’ll be onsite all week—happy to connect if you are too!

Interesting piece on rising AI cloud costs. In my experience, it’s mostly two things: mass adoption + underutilized GPUs. Lots of companies are scaling AI workloads before they understand infrastructure efficiency. Worth the read! Appreciate the conversation and inclusion Madiha Tariq Charg Cloud AppVerticals