Runpod

@runpod

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runpod.io

Joined March 2022

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Runpod
@runpod
Sep 3
In 2023, everyone was talking about training. That conversation still matters, but it's not what's eating your week anymore. The fight moved to inference, latency per token, throughput under real load, and a GPU bill that grows faster than you'd like...
Article
AI Infrastructure Stack in 2026: A Practical Guide
In 2023, the daily fight was training: distributed jobs across A100s, gradient synchronization at scale. That conversation is still relevant, but it's no longer the primary constraint for most...
4
Runpod
@runpod
14h
If your inference is memory-bound at low batch sizes, speculative decoding can help. N-gram speculation costs zero extra VRAM and works surprisingly well on RAG and summarization workloads.
Speculative decoding: faster inference without a bigger GPU
From runpod.io
Runpod
@runpod
20h
When your AI product goes viral overnight, you don’t want your infrastructure to hold you back. Yet, for many startups, that’s exactly what happens. Queues that spike to thousands of users mean you can’t scale fast enough. And when traffic slows down, all of a sudden you sit
Runpod
@runpod
Sep 5
"Private AI server" means something different to everyone who says it. We wrote the guide to what each option actually involves and when each one makes sense.
Private AI Server: What it Means and How to Run One
From runpod.io
Runpod
@runpod
Sep 5
A year ago, models like Kimi K3 were a 64-GPU conversation. But now, it fits on a single 8xB300 pod. See our walkthrough below on how to run Kimi K3 on Runpod:
Deploying Kimi K3 in 4-bit on a single 8xB300 Pod on Runpod
From runpod.io