Consolidate your own GPUs — or capacity you arrange from anyone — into one private inference layer. With confidential computing, the machine's owner can't see your prompts, your data, or your model. Not the host OS. Not the hypervisor. Not even the operator who owns the machine.
One OpenAI-compatible endpoint, backed by whatever capacity you can get your hands on.
Consolidate every GPU you already own — across servers, offices, and idle workstations — into a single pool with one inference API in front of it.
Strike a deal with a partner, a peer, or a rental provider and fold their GPUs into the same pool on demand — burst when you need to, release when you don't.
Here's the part that changes the math: with confidential computing, you can run on hardware you don't control and its owner still can't see a thing.
Confidential computing turns someone else's GPU into a sealed box. Your workload runs inside; the owner runs the machine but can never look in.
On NVIDIA confidential-computing GPUs (H100, H200, Blackwell B200/GB200), every byte written to GPU memory is encrypted with AES-256. The key is generated inside the GPU's security processor and never leaves the chip — so the contents of VRAM are meaningless to anything outside it.
Your workload runs inside a hardware trusted execution environment on the CPU (AMD SEV-SNP or Intel TDX), and the PCIe link between CPU and GPU is encrypted and integrity-checked. The host operating system and the hypervisor — the layers the machine's owner controls — see only ciphertext moving back and forth.
Before your data or your model ever leaves your side, the remote GPU cryptographically proves it is a genuine NVIDIA device, running in confidential-computing mode, with unmodified firmware and the exact workload you provisioned. If the proof doesn't check out, nothing is sent. Trust is verified by hardware, not promised by the owner.
| Setup | Your prompts & data | Your model weights |
|---|---|---|
| A plain rented GPU | Readable in memory | Readable on disk / in VRAM |
| LLMDeploy consolidation encrypted in transit, isolated |
In-memory on that node* | On that node* |
| LLMDeploy confidential compute NVIDIA CC + attestation |
Ciphertext only ✓ | Ciphertext only ✓ |
* Plain consolidation encrypts data in transit and isolates workloads, but a node you don't physically control is only as private as your agreement with its owner. When the workload must be invisible to the owner, that's what the confidential tier is for.
Confidential computing seals confidentiality and integrity — the owner can never see or tamper with what runs inside, and it all rides on NVIDIA's hardware root of trust that attestation lets you verify. It doesn't hand them an off switch they didn't already have: availability stays theirs, everything inside stays yours.
Rent or borrow GPUs from partners, peers, or the open market when demand spikes — without exposing a single prompt or weight to whoever owns the box.
Healthcare, finance, and public-sector data can run on external capacity while staying cryptographically sealed from the operator — attestation gives you the audit trail to prove it.
Chase price and availability across providers instead of being locked to one datacenter — the confidentiality guarantee travels with the workload, not the vendor.
A fine-tuned model is often the crown jewel. Confidential compute keeps its weights opaque even while it serves inference on infrastructure outside your walls.
We build the consolidation layer that unifies your GPUs — owned or arranged — behind one OpenAI-compatible endpoint, and we stand up the confidential-computing path end to end: CC-mode GPUs, the CPU trusted execution environment, the attestation flow your side verifies, and the open-weight models running inside. You point your application at one URL; the routing, the sealing, and the proof happen underneath.
NVIDIA confidential-computing GPUs: H100 and H200 (Hopper) and Blackwell (B200/GB200), with CC mode enabled and paired with a CPU trusted execution environment (AMD SEV-SNP or Intel TDX). Older GPUs don't have the on-die encryption engine, so they can join a consolidated pool but not the confidential tier.
The overhead lands mostly on encrypted data movement between CPU and GPU — not on the math itself. For steady LLM inference, where work is dominated by on-GPU compute and larger batches, the hit is typically small (single digits to low double digits percent). Tiny, latency-critical requests feel it more. We benchmark it on your actual workload before you commit.
Before any data or model leaves your side, your client pulls the GPU's signed attestation report and checks it against NVIDIA's attestation service and your own policy: genuine device, CC mode on, expected firmware, and the exact workload measured. Keys reach the enclave only if it passes. You hold that gate — not the machine's owner.
Yes — like all confidential computing, the guarantee is rooted in the GPU and CPU vendors' hardware root of trust and firmware. Attestation lets you verify that chain cryptographically on every run, rather than take the host operator's word for anything. If you can't accept a silicon vendor as a root of trust, confidential computing isn't the right tool — self-hosting on hardware you own is.
It seals confidentiality and integrity — the owner can't read or tamper with your data or model. It does not give you availability: whoever owns the machine can still power it off or throttle it, and no confidential-computing platform defeats every physical or side-channel attack. When you also need guaranteed uptime, we pair confidential capacity with your own fleet and failover.
Yes — it's one OpenAI-compatible endpoint. Your own GPUs carry the everyday load; confidential third-party capacity absorbs bursts or isolates the most sensitive jobs. The routing happens under the hood, so your application only ever sees one URL.
Tell us your workload and where your capacity is — we'll design the private layer around it.
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