RAM compression shrinks frontier models by 50–60%, making it feasible to run them in your own VPC, on your own hardware, or both. Your infrastructure. Your data. Fully under your control.
Book a Technical BriefingModels are too large for private infrastructure.
Frontier models demand hundreds of gigabytes and multi-GPU setups. Running them in your own VPC or on-prem has been financially impractical.
API calls mean your data leaves your perimeter.
Every token sent to a third-party API is data you no longer control. For regulated industries, that's a compliance risk. For everyone, it's a strategic one.
You can't prove what a compressed model lost.
Compress a model to fit your hardware and you're guessing about what capabilities survived. Weeks of benchmarking, or blind faith.
The economics haven't worked. Until now.
Running your own model used to cost more than the API. RAM compression changes that equation entirely.
Now it's feasible. We built the tools to make it work.
Our exclusive model compression framework that makes frontier models feasible to run in your own infrastructure, whether that's a VPC, a Mac Studio, or an air-gapped facility.
109B
Parameters on a Mac Studio
4.6%
Better quality than GPTQ at 72% less memory
0
Training data required
<5m
To quantize on CPU, no GPUs needed
Gemma 4 31B at half the size
RAM compresses Gemma 4 31B from 62GB to 31GB, matching the full-precision MMLU-Pro benchmark score. Fits on a single GPU instance in your VPC.
Your VPC, under $9k a year
A compressed 31B model running 10 hours a day on AWS, Azure, or GCP costs under $9k/year on-demand, under $3k on spot. Compare that to $50–100k+ in API fees.
Or on a Mac Studio, zero recurring cost
Same model, same quality, running natively on Apple Silicon via MLX. One-time hardware cost. No metered billing. No data ever leaves the building.
In your AWS VPC. In your Azure subscription. On a Mac Studio at your office. In an air-gapped facility. We deploy frontier AI into environments you fully control, with every token processed inside your security perimeter.
This isn't a proof of concept. It's production-grade private AI running in healthcare systems, financial institutions, and regulated enterprises today.
Your VPC
AWS, Azure, GCP — your account
Apple Silicon
Mac Studio & M-series fleets
On-Premises
Your data centre, your rules
Air-Gapped
Classified & secure environments
Most AI consultancies resell someone else's tools. We publish original research, build the compression and governance platforms, and deploy production systems into infrastructure you fully control. End to end. No handoffs.
We develop RAM, original compression research validated across 7 model families and 40,000+ benchmark questions.
We fit frontier models to your exact hardware budget. Tell us your device and memory target, we deliver an optimised model.
Production-grade deployment into your VPC, on your Mac fleet, or on-premises. Your environment, your security perimeter, your control.
Monitoring, model updates, Watchman re-certification, and continuous improvement. We keep your private AI running at peak performance.
Shrink frontier models by 50–60% with zero quality loss. A 62GB model becomes 31GB, fitting a single GPU instance or a Mac Studio. Under $9k/year on cloud, or one-time hardware cost on-prem. Your data stays in your infrastructure.
Audit every compressed model before it goes live. Watchman tells you which capabilities are preserved, at risk, or degraded, and scans for 22 categories of LLM security vulnerabilities. Compliance-ready reports for regulated industries.
The enterprise platform that ties it all together. Shepherd takes a frontier model, compresses it with RAM, validates it with Watchman, and deploys it to your VPC, Mac fleet, or air-gapped facility. CI/CD integration included.
Your private AI deployment is only as good as the team behind it. We monitor, update, and improve your system continuously, with Watchman audits on every model update and 24/7 operational support.
Specialist services across the full AI stack.
Our engineers embed directly into your teams, transferring sovereign AI knowledge while delivering results.
Our proven Neural Pod operating model for de-risked velocity and cradle-to-grave ownership of AI initiatives.
Ethical, compliant AI systems with our RAI framework, from EU AI Act readiness to algorithmic impact assessments.
Purpose-built infrastructure for MINT-optimised LLM inference, from Apple Silicon fleets to air-gapped Kubernetes clusters.
Enterprise-grade security for your sovereign AI, data residency, encryption, audit trails, and identity integration.
Bespoke AI applications built on sovereign infrastructure, from LLM-powered agents to computer vision and predictive analytics.
7
Model families tested
40,000+
Benchmark questions
8B–400B+
Parameter range
Peer-reviewed
Original research
Every month on hosted AI deepens your dependency, on someone else's infrastructure, someone else's pricing, someone else's terms. Your data. Your models. Your authority.
We have the research, the frameworks, and the production expertise to make it real. Let's talk.
Schedule Your Technical Briefing30-minute call. No sales pitch. Just engineers.