packet.ai provides on‑demand access to high‑end NVIDIA GPUs—including 96 GB GDDR7 Blackwell models—through a pay‑per‑second pricing model that is typically 50% cheaper than traditional cloud providers. Users can launch workloads via SSH, web UI, CLI, or API, with no contracts, preemptions, or hidden fees, and benefit from isolated containers and encrypted connections for data security.
Funding
Funding not disclosed
Founders
Product
Problem
Developers and teams building AI/ML applications often face high costs, idle compute waste, and complex provisioning when accessing high‑performance GPUs, especially with spot instances that can be preempted or contracts that require long‑term commitments.
Solution
Packet.ai offers on‑demand GPU cloud instances that run on full‑silicon NVIDIA B200, H200, and RTX PRO 6000 Blackwell GPUs with up to 96 GB of GDDR7 memory. Users can launch a GPU in seconds via SSH, a web terminal, CLI, or API, and each workload runs in an isolated container with dedicated GPU memory and encrypted connections. Pricing is transparent, billed per second with no contracts, hidden fees, or preemption, and the service includes a 99.9 % SLA. The platform provides pre‑installed CUDA, popular ML libraries, persistent storage, and developer tools such as Jupyter, VS Code, and one‑click HuggingFace model deployment, enabling rapid development and scaling of AI workloads.
Target Audience
Primary customers are AI/ML developers, data‑science teams, and startups that need high‑performance GPU compute for training, inference, or model prototyping without long‑term cloud contracts.
Features
- Full‑silicon NVIDIA B200, H200, and RTX PRO 6000 Blackwell GPUs (96 GB GDDR7) with real‑time performance
- Per‑second, contract‑free billing and transparent pricing (e.g., $0.66 /hr for RTX 6000 Pro)
- Multiple access methods: root SSH, browser‑based web terminal, CLI, and REST/OpenAI‑compatible APIs
- Isolated containers with dedicated GPU memory and AES‑256/TLS 1.3 encryption for data security
- Pre‑installed CUDA 12.8, PyTorch, TensorFlow, and other ML toolchains; Docker support and Jupyter integration
- Persistent NVMe SSD storage and shared volumes that survive pod termination, with auto‑refill wallet and budget controls
- Real‑time monitoring dashboard showing GPU utilization, VRAM, temperature, power draw, and detailed activity logs
- Managed inference via Token Factory (pay‑per‑token, OpenAI‑compatible) and one‑click HuggingFace model deployment