Runpod secures $100m Series A funding from Summit Partners

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We’ve got huge news, folks - we just closed a $100M Series A funding round led by Summit Partners. Four years ago this company was two people managing racks in a New Jersey basement, and today, we’re home to over a million developers. Most of you found us by word of mouth, not ads, and this subreddit is one of the principal avenues for that, with over a thousand of our most passionate users taking the time to create this community, brick by brick.

That capital will go directly into building our platform, providing enormous leverage to build out new features and secure new hardware to make your experience even better. For everyone that stuck by us through the supply crunch and went to bat for us internally even when it wasn’t easy to do so, we appreciate you more than you know and we can’t wait to show you what’s coming next. Read more about it in our CEO’s blog post here:

https://www.runpod.io/blog/one-million-developers

reddit.com

u/RP_Finley — 12 days ago

▲ 7 r/RunPod

Deploy when available is now GA - no more refreshing or sniping tools required!

This new flow will allow you to queue for any GPU spec that may not be immediately available, whether it’s a spec that is totally rented out at the moment, or if you just need more than can be immediately given.

To get started, if you are still on the old deploy flow, you will first need to enable the New Pod deploy page under Early Access features at https://console.runpod.io/user/early-access

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From there, select your desired pod spec. If it’s currently out of capacity, the deploy button at the bottom will change to Deploy When Available. Clicking this button will provide the following prompt:

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Note that your pod will deploy immediately and begin billing as soon as it’s available, whenever that might be, even if you’re away from your computer. If you’d prefer to set time to be off limits so you aren’t billed for a pod you can’t use, we encourage you to use the subscription window to set valid times for deployment. You’ll be notified when your pod is live either through console or email depending on your preference; we are working on adding SMS as an additional option to be released shortly.

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In addition, remember that for a successful deployment, the pod configuration must be something that might feasibly become available at some point, even if briefly. As always, we're constantly working on supply to make larger deploys possible. We hope this makes reserving a pod easier - let us know what you think!

reddit.com

u/RP_Finley — 18 days ago

▲ 21 r/RunPod

GPU Supply and Availability Megathread With Ongoing Updates

Supply is tight right now, and we've been getting a flood of separate "no GPUs available" / "why can't I find an X" threads. To keep the sub readable and make it easier for everyone to track the situation, all supply, availability, and capacity complaints/questions go in this thread from now on. New standalone threads on the topic will be redirected here. We'll continue to edit/update this thread as we go and I'd recommend sorting comments by New to see what's been discussed recently.

TL;DR

We're in a broad, industry-wide GPU crunch, not a Runpod-specific outage. High-end SKUs (H100, H200, B200) are scarce everywhere and finding the GPUs to begin with is a challenge, let alone partners that also have everything else on lock like data center certifications. We prioritize stability and reliability for our users and we need to be selective on who we bring on as serving partners.
We are adding GPUs on a near-daily basis, but new supply gets snapped up quickly a "shadow backlog" of demand).
Best moves to actually get/keep a card: be flexible on SKU, reserve/commit in advance, and use CloudSync so you're not pinned to one datacenter. While of course we'd prefer you to use network storage, and we are working on new features to augment network volume viability, in truth offsite storage and using CloudSync to bring it into new pods may be more appropriate for some use cases in this climate. Look at the costs and drawbacks and do what's best for you for storage, not for us.
We are now posting biweekly aggregated GPU-onboarding updates - we did this in Discord for our first update but they need to be mirrored here and we will start doing that.

Our CTO Brennen Smith recently authored an article on what's causing this (The GPU supply supercycle is here), and this is a structural shift, not a temporary blip. Three forces are hitting at once:

Memory bottleneck: NAND/DRAM producers retooled their fabs toward HBM3 for AI accelerators, which cut into standard memory capacity. Memory is now the binding constraint on GPU manufacturing.
Hyperscaler buyouts: Large players are buying out years of factory production in advance, leaving neoclouds and independent providers to compete for what's left.
Nvidia's architecture transition: Hopper and Ada Lovelace architecture production has wound down to make room for Blackwell. Some of the higher end cards can still be found in scale, but finding, say, 4090s is proving challenging especially with more recent GPUs providing a more appealing return on investment for the same infrastructure. Blackwell production is ramping but there's a gap.

What we're doing

We're adding supply continuously, and decided on a biweekly report cadence aggregating everything is the best way to report this. You probably don't want a new Reddit thread reporting every time we add a new machine. It's hard to communicate this through the customer facing UI - we can genuinely add 1000 GPUs in a single drop, but if they all get rented immediately, from the relative amounts shown in the customer facing UI it appears that nothing changed. We'll be adding these as edits at the bottom of this post as we go.

We've also implemented MIG (multi-instance GPU) that lets us divide up a single GPU and serve it as smaller instances. This of course will always be disclosed as a MIG instance. Right now we're doing this by serving a single 6000 Pro card as four 24GB instances so we can try to balance what we have to meet as many of our clients' user requests as possible.

We've added B300s as well, and more are coming soon.

Supply updates

We'll post our next update on or around June 3rd.

May 6-20, 2026

Large adds (>100 GPUs)

US-PA-1 — RTX Pro 6000
US-TX-6 — B200
EU-IS-5 — H200 SXM
EU-FR-1 — H200 SXM, H100 SXM

Smaller adds (<100 GPUs)

EU-RO-1 — RTX Pro 4000, RTX 4090
US-NC-1 — RTX Pro 6000

We definitely understand how frustrating this is - and we are working hard to get as many GPUs to serve as possible. We also want to give everyone an opportunity to make their voice heard - the only requests I have is that you keep it about the service rather than the people behind it and that you refrain from promoting other inference providers in the process.

Thanks!

u/RP_Finley — 1 month ago

▲ 3 r/RunPod

Flash is GA! When we launched Flash in beta back in March, the bet was simple: take Docker off Python developers' plates and they'll get a lot done. The response told us we were onto something, and today we're shipping it for real. Flash provisions a GPU on Runpod Serverless, installs your dependencies, executes remotely, and returns the result without a Docker image or registry push.

What's new since beta: a cleaner u/Endpoint API with typed GpuType/GpuGroup enums, four endpoint patterns (queue-based, load-balanced HTTP, custom Docker images, and existing-endpoint-by-ID), real production deploys via flash build/flash deploy with cross-platform builds, cross-endpoint function calls for hybrid CPU/GPU pipelines, browser-based flash login, flash undeploy for cleanup, first-class NetworkVolume with multi-datacenter support, and a coding-agent skill package for Claude Code/Cursor/Cline. pip install runpod-flash to try it.

Full post: https://www.runpod.io/blog/flash-is-ga ·

Repo: https://github.com/runpod/flash ·

Docs: https://docs.runpod.io/flash/overview

runpod.io

u/RP_Finley — 2 months ago

▲ 4 r/RunPod

Runpod is hiring a Developer Relations & Community Manager: a senior, hands-on role reporting to the Head of Marketing. The person in this role will be the public face of Runpod in the AI developer ecosystem and the operator who builds the systems that make their developer community self-reinforcing. A typical week might include shipping a tutorial on deploying vLLM, running a community AMA with an engineer, speaking at a conference, and synthesizing community feedback for the Product team.

Responsibilities span both technical advocacy and community ops: creating high-quality technical content (tutorials, guides, sample apps, benchmarks, video demos), building reference architectures and integration examples, representing Runpod at events like KubeCon, NVIDIA GTC, and PyCon, contributing to the open-source AI ecosystem (PyTorch, Hugging Face, vLLM, LangChain), and owning end-to-end community strategy across Discord, Reddit, GitHub Discussions, and social channels. It's a remote-first position at a Series A company in a period of rapid growth. Full details and application link: https://job-boards.greenhouse.io/runpod/jobs/5191760008

job-boards.greenhouse.io

u/RP_Finley — 2 months ago

▲ 3 r/RunPod

We've heard you loud and clear on the supply issues and wanted to show that we are working to meet demand. We have several more in the pipeline coming, but are happy to move this one into production. Let us know what you think!

runpod.io

u/RP_Finley — 3 months ago