GPUNET

**1/7** Most AI teams pick compute providers with a broken framework. They scan top-10 listicles, compare raw H100 hourly rates, and choose the cheapest option. Six months later, they’re locked in. Here’s the framework that actually works for where to run your AI workloads. **2/7** Stop asking “Which provider is best?” Ask these five questions first: • Workload shape: Heavy batch training or 24/7 low-latency inference? • Budget stage: Bursty pre-seed testing or steady-state Series A scaling? • DevOps bandwidth: Can your team orchestrate multi-provider infrastructure? • Compliance: Do you need strict HIPAA/SOC 2 regional boundaries? • Growth spikes: Can your infrastructure scale 10× instantly without quota delays? **3/7** Understand the market split: • Hyperscalers (AWS/GCP): Strong IAM/MLOps ecosystems and compliance, but premium pricing, high egress fees, and slow quota approvals. • Neoclouds: Fast raw compute, yet you bet uptime on a single vendor’s physical limits. • Aggregated fabrics (@gpunet): Global supply, lowest market-clearing rates, and dynamic scaling across distributed nodes. **4/7** The biggest rookie mistake is treating training and inference as the same workload. • Training: Needs massive centralized clusters, high inter-node bandwidth (NVLink), and “train once” batch economics. • Inference: Needs low latency, regional distribution, fast cold starts, and ruthless cost-per-token optimization. You serve forever. Build the stack accordingly. **5/7** The staged roadmap for AI infrastructure: • Stage 1 (Pre-PMF): Stay 100% on-demand. Never lock into reserved contracts. Prioritize checkpointing discipline and flexible pay-per-hour compute. • Stage 2 (Early scale): Separate training and inference clusters. Run 60-day spend models to test if reserved silicon pays off. • Stage 3 (Scale): Abstract away single-vendor lock-in with open container standards and decentralized compute fabrics. **6/7** This is where @gpunet fits the modern AI stack. Instead of rigid cloud contracts or manual vendor negotiation, it unifies global compute supply into a single fabric: • NVIDIA H100s from $2.20/hr • Instant B200 & Blackwell reservations • OpenAI-compatible GLM 5.3 inference at $0.15/1M tokens **7/7** The future of production AI will not run on a single walled-garden cloud. It will run on a global compute fabric that routes workloads by cost, geography, and latency. Explore the full @gpunet compute, model, and yield ecosystem: world.gpu.net/

Drop-in replacement. Unbeatable token economics. ​@gpunet Models is officially live 🔥 ​Switch your provider in 1 line of code: • ⚙️ 100% OpenAI-Compatible API • 🧠 GLM 5.3 starting at $0.15 / 1M tokens • 🔑 Instant API key generation • 💻 Plugs natively into cURL & the OpenAI SDK ​Stop paying single-provider premiums. ​👉 Claim your API key: cloud.gpu.net/models