Lumino AI

OpenAI-compatible hosted model APIs and GPU cloud with transparent pricing, cached-input accounting on supported models, and one API for chat, coding, reasoning, image, video, and speech workloads.

Packet.ai

packet.ai is an LLM inference and GPU cloud platform built by hosted.ai. It delivers top-tier GPU performance on NVIDIA B200 (bare metal), A100, L40S, RTX Pro 6000, and RTX 4090 at up to 99% less than typical providers. Same models. Same API. Dramatically lower cost.At its core is Token Factory, packet.ai's LLM inference layer that's fully OpenAI-compatible. Drop it in as a replacement for OpenAI, Anthropic, or any major inference provider without changing your code. Sub-second latency for interactive apps. Batch throughput for high-volume workloads.Key Features:- Up to 99% cheaper than OpenAI for the same LLM output- OpenAI SDK compatible, zero migration friction- NVIDIA B200 bare metal, A100, L40S, RTX Pro 6000, RTX 4090 GPU access- Dynamic and Dedicated GPU plans with monthly subscriptions- Sub-second latency for real-time applications- 99.9% SLA, 24/7 human support- No credit card required to start, 10K free tokens- No contracts on inference, cancel any timeUse Cases:For AI startups and developers running LLM inference at scale, packet.ai eliminates the biggest cost: the compute bill. Whether you're building a RAG pipeline, an AI agent, a chat interface, or a batch processing workflow, you get the same NVIDIA silicon at a fraction of what you'd pay elsewhere. For teams doing GPU-intensive training or fine-tuning, choose between Dynamic (scheduler-enforced pool access) or Dedicated (reserved GPU) monthly plans.Pricing:You can begin by signing up for free, no credit card required. GPU compute available on monthly Dynamic and Dedicated subscription plans. ~30-35% lesser than hyperscalersWhy packet.ai:Most platforms claim GPU sharing but deliver one of two things: hard partitioning via NVIDIA MIG (fixed slices, rigid, requires a reboot to resize) or best-effort sharing via NVIDIA MPS (concurrent but no real memory isolation between tenants). packet.ai uses a third approach: scheduler-enforced dynamic sharing across a pool of GPUs. Your container gets zero direct GPU access. Every call is proxied, so isolation and VRAM limits are enforced in software, at any size, across a whole pool of GPUs instead of one card at a time. A pool, not a partition.a hosted·ai project.