Private inference for the world's best open models
Run DeepSeek, Llama, Qwen, GLM, Gemma and Mistral on dedicated GPUs inside your own VPC or on-prem. Hundreds of tokens per second, sub-second time-to-first-token, and nothing of yours is ever logged, stored, or trained on.
Zero data retention means your prompts and completions exist only in GPU memory for a single request. Nothing is written to disk, nothing is logged, and nothing is ever used to train a model.
Built for regulated enterprises
Everything your security and compliance teams demand
Open-source models give you control over cost, latency and lock-in. HyperInfer gives you the privacy, isolation and certifications to actually run them in production.
Zero data retention
Prompts and completions are never logged or stored. Contractually guaranteed in your DPA.
Single-tenant GPUs
Dedicated B100 / B200 hardware reserved for you alone. No shared queues, no noisy neighbors.
Runs in your VPC
Deploy inside your own cloud account or datacenter so data never crosses your security boundary.
No training on your data
Your inputs and outputs are never used to train or fine-tune any model, ours or anyone else's.
Encrypted end-to-end
TLS 1.3 in transit, AES-256 at rest for any optional artifacts, customer-managed keys available.
Full audit trail
Tamper-evident access logs, SSO/SAML, RBAC and SIEM export — without logging a single prompt.
The model catalog
Frontier open weights, served fast
Every model runs on dedicated B100 / B200 hardware with custom kernels and continuous batching. No noisy neighbors, no shared queues.
| Model | Parameters | Context | Throughput | Status |
|---|---|---|---|---|
DeepSeek V4 FlashEfficient · DeepSeek | 284B MoE | 1M | 318 t/s | Live |
DeepSeek V4 ProReasoning · DeepSeek | 1.6T MoE | 1M | 188 t/s | Live |
Qwen3.5 397B A17BGeneral · Alibaba | 397B (17B active) MoE | 256K | — | Live |
GLM 5.2Agentic · Z.ai | 744B MoE | 1M | 247 t/s | Live |
Gemma 4 31BEfficient · Google | 31B | 256K | 352 t/s | Live |
Llama 4 MaverickGeneral · Meta | 400B MoE | 1M | 296 t/s | Live |
Mistral Large 3General · Mistral AI | 675B (41B active) MoE | 256K | — | Live |
Independently benchmarked
The fastest private inference, measured
Custom GPU kernels, speculative decoding and continuous batching push tier-1 open weights well past the field — without sharing your hardware with anyone.
Single 8×B100 node · 2k in / 512 out · concurrency 64 · Jun 2026
Zero data retention
Your data's entire lifecycle, in milliseconds
Prompts and completions live only in GPU memory for the duration of a single request. Nothing touches disk. Nothing is logged. Nothing is used for training — contractually guaranteed in your DPA.
- 01
Request received
Authenticated over TLS 1.3, routed only within your pinned region.
- 02
Loaded to GPU
Tokens decrypted into volatile GPU memory. Never written to disk.
- 03
Inference runs
Computed on your dedicated, single-tenant accelerators.
- 04
Response streamed
Output returned token-by-token directly to your client.
- 05
Memory purged
GPU memory cleared on completion. 0 bytes persist. No logs.
End-to-end: request received → response streamed → memory purged. Total persisted footprint: 0 bytes.
Deploy anywhere
Inference that comes to your data
Run HyperInfer wherever your compliance boundary lives — fully managed in your cloud account, in your own datacenter, or fully air-gapped.
In your VPC
Fully managed inference inside your AWS, GCP or Azure account. We operate it; the data never leaves.
On-premises
Bring HyperInfer to your own datacenter and existing GPU fleet, fully behind your firewall.
Private cloud
Dedicated, isolated capacity in a HyperInfer region with strict data residency guarantees.
Air-gapped
Fully disconnected deployments for classified, defense and the most sensitive workloads.
- uae
- us-east
- us-west
- eu-west
- eu-central
- uk
- apac-sg
- apac-syd
Enterprise-grade
Certified, contracted, and on call
Audited controls, signed agreements, and the support structure regulated industries require — from financial services to healthcare to the public sector.
- SOC 2 Type II
- HIPAA
- GDPR
- ISO 27001
- ISO 42001
- PCI DSS
- CCPA
- FedRAMP (in process)
- 99.99%
- Uptime SLAFinancially backed, multi-region failover.
- <150ms
- Time to first tokenMedian, in-region, at production load.
- 24/7
- Dedicated supportSlack channel + named solutions engineer.
- 30 min
- Sev-1 responseContractual response time for criticals.
From the blog
Field notes on private inference
Deep dives on compliance, deployment, performance, and the engineering behind zero data retention — written for the teams that have to ship AI in regulated environments.
- Compliance
The EU AI Act and your inference stack
The EU AI Act is binding law with real deadlines. Here's who it covers, what changes in August 2026, and how in-region private inference helps you meet your obligations.
· 11 min read
- Security
Why single-tenant GPUs matter for privacy
Multi-tenant GPUs share HBM, schedulers and side-channel surface with unknown code. Single-tenant architecture is the difference between logical isolation and genuine hardware dedication.
· 9 min read
- Performance
B200 vs H100 for LLM inference
How B200's 192GB HBM3e, native FP4, and ~8 TB/s bandwidth compare against H100 and H200 for LLM serving — and why supply, not spec sheets, is the real bottleneck in 2026.
· 10 min read
- Migration
Migrating off OpenAI to open weights
Same OpenAI SDK, different endpoint. A practical migration path off proprietary APIs to open-weight models: shadow first, evaluate carefully, route gradually, cut over cleanly.
· 9 min read
- Compliance
What SOC 2 Type II means for inference
SOC 2 Type II audits a provider's controls over months, not a single day. Here's what the five Trust Services Criteria mean for inference, how to read a report, and what to ask for.
· 9 min read
- Guide
Air-gapped LLM inference architecture
How to run frontier open-weight models with zero external connectivity — local registries, physical-media model updates, and the cryptographic controls for classified workloads.
· 10 min read