Understanding GPU Inference Workloads [D]

<!-- SC_OFF --><div class="md"><p>doing infrastructure evaluation for inference workloads and running into the same problem everywhere: every platform publishes p50 cold start

<!-- SC_OFF --><div class="md"><p>Hi everyone,</p> <p>We're a small team building a decentralized AI inference network powered by idle GPUs from machines around the world. Instead

<!-- SC_OFF --><div class="md"><p>Spoke with many providers who lurk in this sub, plus met folks who work in inference.</p> <p>For a 10k monthly revenue, a provider was able to

<p>80% of engineering teams miss their AI infrastructure cost forecasts by more than 25% — not because they're spending wrong, but because they're managing three fundamentally

<!-- SC_OFF --><div class="md"><p>I’m thinking of expanding an on-device inference SDk into a full blown AI inference platform and seeing more and more inference platform popping

<!-- SC_OFF --><div class="md"><p>I run all sorts of models locally, but I only have a 3090ti. With vllm I’m able to batch and get 2-6 parallel streams in low context. That

<!-- SC_OFF --><div class="md"><p>Posting this here since you guys are the more tech-savvy compared to <a href="/r/StableDiffusion">/r/StableDiffusion</a>. </p> <p>I’m looking at