7 days to SuperAI 2026 🇸🇬 Come see us at #PB21 We're bringing the Frontier Inference Cloud for Agents to Marina Bay Sands, June 10–11. Agents bottlenecked on latency, cost, or reliability? Let's talk. 🎤 CEO, Byung-Gon Chun @ Plaud Main Stage 🎤 VP of Sales (APAC),…

2. How to estimate GPU VRAM requirements for an AI model: A common heuristic is reserving an additional 20% of weight memory, though high-concurrency or long-context workloads may require 30–40%+. Examples with 20% headroom: → DeepSeek-V4-Flash: 142 GB + 28.4 GB = 170.4…

1. How to estimate GPU VRAM requirements for an AI model: Start with the minimum VRAM required to load the model: W = P × B Where: P = parameter count B = bytes per parameter at your target precision Real-world examples: → DeepSeek-V4-Flash (284B at 4-bit): 142 GB →…

What it delivered: — Autoscaling absorbs submission peaks with zero manual intervention — Automatic fault recovery keeps service stable through surges — Current A100 setup profiled to handle 2× traffic, clear path to H100/H200 — One platform to consolidate workloads…

As the service grew, Upstage needed an inference platform that could: Handle bursty, time-sensitive traffic efficiently Consolidate workloads from multiple vendors Scale cost-effectively — without over-provisioning GPUs They chose FriendliAI Dedicated Endpoints as their…