Editing Openai/6960ed20-8dfc-8005-a089-1bb9023ccffa (section)

=== Assistant: There isn’t a public, exact number for how much RAM OpenAI bought overall for all of its servers or data centers — that… ===
There isn’t a public, exact number for how much RAM OpenAI ''bought'' overall for all of its servers or data centers — that’s internal infrastructure detail that the company hasn’t disclosed in specific terms. However, we do have some insight into the scale of memory and hardware involved in AI model training and deployment, which gives a sense of the magnitude:

===== For large language models (LLMs), RAM and memory requirements depend on the model size: =====
* OpenAI’s newer open-weight models like gpt-oss-20b can run on systems with ~16 GB of memory (RAM) or more. mint<ref>{{cite web|title=mint|url=https://www.livemint.com/ai/artificial-intelligence/openai-launches-gpt-oss-120b-and-20b-models-that-can-work-without-the-cloud-on-your-computers-11754474117999.html|publisher=livemint.com|access-date=2026-01-10}}</ref>
* Larger models like gpt-oss-120b may require dozens of gigabytes of memory (60–80 GB+ virtual memory) on high-end systems to run locally. mint<ref>{{cite web|title=mint|url=https://www.livemint.com/ai/artificial-intelligence/openai-launches-gpt-oss-120b-and-20b-models-that-can-work-without-the-cloud-on-your-computers-11754474117999.html|publisher=livemint.com|access-date=2026-01-10}}</ref>

These numbers are running the models locally — but OpenAI’s cloud infrastructure for production services like ChatGPT is far more massive.

===== OpenAI and its partners are building huge AI infrastructure projects that implicitly require huge amounts of memory: =====
* OpenAI’s Stargate data center project is reported to involve agreements with Samsung and SK hynix to supply up to 900,000 DRAM wafers per month — potentially representing up to ~40% of global DRAM wafer capacity (which means hundreds of gigabytes to terabytes of memory per server cluster). Tom's Hardware<ref>{{cite web|title=Tom's Hardware|url=https://www.tomshardware.com/pc-components/dram/openais-stargate-project-to-consume-up-to-40-percent-of-global-dram-output-inks-deal-with-samsung-and-sk-hynix-to-the-tune-of-up-to-900-000-wafers-per-month|publisher=Tom's Hardware|access-date=2026-01-10}}</ref>

That level of memory supply indicates enormous aggregate RAM/HBM capacity across entire clusters, but doesn’t translate into a single exact RAM purchase number.

===== There are a few reasons you won’t find a single figure for “how much RAM OpenAI bought”: =====
* OpenAI’s infrastructure spans many data centers and partners; memory is purchased, allocated, and deployed over time.
* Memory types include system RAM (DDR5) and specialized high-bandwidth memory (HBM) on GPUs — the latter is crucial for AI workloads but isn’t usually tallied as traditional RAM.
* Deals like Samsung/SK-hynix and GPU purchases with Nvidia/AMD involve DRAM and HBM at massive scale, but details aren’t fully public. The Guardian<ref>{{cite web|title=The Guardian|url=https://www.theguardian.com/technology/2025/oct/06/openai-chipmaker-amd-deal|publisher=The Guardian|access-date=2026-01-10}}</ref>

===== - OpenAI hasn’t publicly disclosed a single total amount of RAM purchased. =====
* AI models and data center builds require huge volumes of memory — from tens of GB per machine to potentially petabytes across entire infrastructures.
* Recent reports suggest memory supply deals that could consume a large fraction of global production capacity, hinting at the enormous scale. Tom's Hardware<ref>{{cite web|title=Tom's Hardware|url=https://www.tomshardware.com/pc-components/dram/openais-stargate-project-to-consume-up-to-40-percent-of-global-dram-output-inks-deal-with-samsung-and-sk-hynix-to-the-tune-of-up-to-900-000-wafers-per-month|publisher=Tom's Hardware|access-date=2026-01-10}}</ref>

If you’re curious about how memory requirements scale with AI model size, I can break that down too!