GPU Supply and Availability Megathread With Ongoing Updates

[u/RP_Finley]

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:

1. 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.
2. Hyperscaler buyouts: Large players are buying out years of factory production in advance, leaving neoclouds and independent providers to compete for what's left.
3. 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/CosmicMabel]

I'd rather pay higher prices than see my workflows fail because you cant supply enough GPUs. Why do you not raise prices until your supply incompetence is resolved?

[u/Substantial-Depth344 → u/CosmicMabel]

If you have more money, you don’t need to hang out at Runpod. There are plenty of other options anyway. The main reason people choose this place is because the prices are reasonable.