In plain words, what is this thing
A maxed-out gaming PC has maybe 32 GB of graphics memory and runs small AI models, the chatbot-toy tier. This has 748 GB of unified memory and 20 petaflops of AI compute. That gap is not "a bit faster." It is the difference between running a toy assistant and running the kind of AI that companies rent entire data centres for. Except this one sits on a desk, runs Windows, and the bill stops the moment we own it.
- RTX 5090, 32 GB graphics memory
- Runs small, quantised models
- One AI task at a time
- Still renting the cloud for anything serious
- 748 GB coherent unified memory
- 20 petaflops of FP4 AI compute
- Hundreds of AI agents, all at once
- Zero cloud dependency. Data never leaves the room
DELTA: roughly a 20x jump in AI muscle. Same desk. Same power socket.
What it unlocks
Four capabilities a normal machine simply cannot reach. Each one maps to something on Synthera's roadmap.
The whole model, in memory
Hold a complete trillion-parameter model in memory at once. Frontier-lab intelligence, offline and private.
No queue, no meter
Real-time inference and on-device training. Nothing waits in a cloud queue, no per-token meter running in the background.
An agent workforce
Hundreds of AI agents running concurrently, not one at a time. A digital team that never sleeps.
Cloud dependency
Client data never leaves the room. For the companies Synthera builds for, that privacy is a selling point, not just a saving.
What we build on it
This is not a gadget. It is the production infrastructure of an AI development firm. Six things it lets Synthera run that we cannot run today.
Ship our own AI products
Build and run Synthera's own AI applications on-device. No per-token API bill eating our margin from day one, and full control over the stack we sell.
Build client AI systems
Develop and deliver custom agents, copilots, RAG and automation for clients, all built and tested on hardware we own instead of a metered cloud account.
Train proprietary models
Fine-tune custom models on client and domain data without shipping a single file to anyone's cloud. The weights are Synthera IP, not something we rent back through an API.
Run real agent platforms
Hundreds of agents at once means we can build genuinely agentic products, for ourselves and for clients, not toy demos that fall over past a few parallel tasks.
An in-house R&D lab
Run and benchmark frontier open-source models locally, prototype fast, and experiment without a meter running. This is what keeps Synthera ahead of firms renting by the hour.
A sales and investor weapon
Walk into a client or investor meeting with an actual AI supercomputer running their use case live. Proof that Synthera is real infrastructure, not slideware. It closes the room.
The business case
This is capital, not a toy purchase. Here is the math, partner to partner.
What it replaces (recurring)
- Cloud GPU rental for AI workloads
- Stacked AI subscriptions and per-token API fees
- Outsourced render and compute credits
- A chunk of contractor dev time
What it earns (compounding)
- Synthera's own AI products ship with real margin, not rented compute
- A client AI service line we can deliver from day one
- Proprietary fine-tuned models that are ours to keep
- One asset that every Synthera product and contract runs on
The ask, plainly
Fund the first GB300 DGX Station for Synthera. I run the build, we own the compute outright. Most AI firms start life renting GPUs by the hour and bleed margin from day one. We start with the engine already in the room. The hardware is the cheapest part of what it makes. Let's not rent our own company's brain.
Anoof · CTO · Synthera Systems | for Naxief · CEO