@gpunet first RWA Pool batch was successfully completed. ✅ Now, the next opportunity to access tokenized GPU hardware investments is here. 📈 Target APR: 20% 💵 Minimum: $1,000 💰 Returns in USDC at the end of the term Explore the RWA Pool 👇 world.gpu.net/ Real hardware. Real compute. Tokenized access.

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

1/ Feels like we've spent the last two years obsessing over training. Bigger models. Bigger clusters. Bigger training runs. Meanwhile, the economics of AI are shifting somewhere else: Inference. 🧵 2/ Training gets the headlines. Inference gets the bill. Frontier-model training costs are measured in the hundreds of millions. But once a model is deployed, every prompt, every agent action, and every generated token becomes an ongoing expense. You train once. You serve forever. 3/ The data is moving fast. Inference represented roughly 1/3 of AI compute in 2023. Deloitte projects it will reach roughly 2/3 by 2026. That's a massive shift in just a few years. 4/ The mistake many teams make? Treating training and inference like they need the same infrastructure. Training wants giant centralized clusters. Inference often benefits from: • Lower latency • Regional deployment • Better cost efficiency • Proximity to users Different workload. Different priorities. 5/ For production AI, the key metrics aren't just FLOPS anymore. They're: • Cost per token • Latency • Geography • Power efficiency (PUE) That's where margins are won or lost. 6/ The challenge is that compute is fragmented across hundreds of providers and regions. That's where @gpunet comes in. Instead of manually researching infrastructure, teams can compare providers, discover available capacity, and find infrastructure that fits their inference workload. The future of AI won't run on a single cloud. It will run on a global compute fabric. #AI #GPU #DataCenters #InferenceAI

The biggest problem with AI video generation isn't quality it's control. Astra Video by @gpunet changes that: Upload your starting frame Set your target ending frame Adjust camera angles & motion intensity Render in seconds Powered by the SeeDance engine on GPU.net’s decentralized GPU network meaning top-tier output at fraction-of-a-cent compute costs. Full creator control without the random morphing. 👉 legacy.gpu.net/astra/video

The complete @gpunet compute, yield, and AI product suite at a glance: 🖥️ GPU Cloud : Enterprise H100 rentals starting at $2.20/hr + B200 & Blackhole reservations 👉 cloud.gpu.net/ 📈 RWA Pool : Tokenized GPU hardware investments delivering 20% APR ($1,000 min) 👉 ganchain.gpu.net/rwa-pool 🪙 GPU Staking :Stake your GPU tokens to earn massive APR 👉 ganchain.gpu.net/ 🤖 AI Models : Managed model hosting & inference with pay-per-token API pricing 👉 legacy.gpu.net/models 🎬 Astra Video : Pay-per-second AI video generation with zero monthly sub traps 👉 legacy.gpu.net/astra/video 🎨 Astra Image :High-res image creation at fraction-of-a-cent compute costs 👉 legacy.gpu.net/astra/image ⚡ Astra Agents : Low-latency, automated AI agent execution stack 👉 legacy.gpu.net/astra/agents 🌐 Access everything across the @gpunet world on a single page: 👉 world.gpu.net/