How RO2 Labs delivers Llama 3 70B inference with zero third-party data routing on single-tenant hardware.
Large language models are transforming how organizations process documents, generate analysis, and accelerate decision-making. But most LLM APIs are built for speed and scale, not for data control.
When you send a prompt to a typical cloud LLM provider, your data travels through shared GPU clusters, crosses multiple network boundaries, and may be logged, cached, or retained by third-party sub-processors. For organizations in defense, healthcare, finance, or legal, this creates data handling risk that no terms-of-service agreement can fully address.
The core issue is architectural. Shared infrastructure means shared risk.
RO2 Labs runs Llama 3 70B on dedicated Apple Silicon hardware in Austin, TX. The inference environment is single-tenant. No other customer workloads, no shared GPU pools, no multi-hop data routing.
The API is fully OpenAI-compatible. Organizations already using the OpenAI SDK can switch by changing a single base URL. No code rewrite, no new client library, no retraining required.
Every request follows the same path:
llm.ro2-labs.aiAt no point does the prompt or response content leave the physical hardware in Austin, TX. There are no third-party inference providers, no GPU rental pools, and no telemetry exports.
Every API response includes machine-readable headers confirming where inference was performed and whether any third-party routing occurred:
{
"model": "llama3:70b",
"choices": [{ "message": { "role": "assistant", "content": "..." } }],
"x-ro2-data-residency": "on-prem-austin-tx",
"x-ro2-third-party-routing": "none"
}
These headers provide an auditable record for compliance teams reviewing data handling practices.
The table below describes common data handling concerns in regulated industries and the architectural properties of our infrastructure that address them. RO2 Labs provides infrastructure controls. Compliance is a shared responsibility between provider and customer.
| Industry | Common Concern | Infrastructure Property |
|---|---|---|
| Defense | Technical data accessible to foreign persons via shared cloud infrastructure | Single-tenant hardware under U.S. jurisdiction. No offshore sub-processors. No foreign-national data access. |
| Healthcare | Sensitive data routed through third-party processors without adequate safeguards | Data never leaves the inference environment. No third-party data processors in the request path. |
| Finance | Customer financial information processed on shared infrastructure | Dedicated hardware. No shared GPU pools. No third-party sub-processors handling customer data. |
| Insurance | Sensitive data exposure through multi-tenant processing environments | Inference runs on isolated, single-tenant hardware. No data leaves the controlled environment. |
| Legal | Privileged documents handled by third-party infrastructure | Documents processed on hardware with no external data routing or third-party access. |
Apple Mac Studio M3 Ultra
28-core CPU / 60-core GPU
96 GB unified memory
Llama 3 70B loaded at full precision
Cloudflare Tunnel (TLS termination)
No open inbound ports
No VPN or bastion required
Location: Austin, TX, USA
The API implements the OpenAI Chat Completions specification. Any application built against GPT-4o or GPT-3.5-turbo can switch to RO2 Labs by changing the base URL:
from openai import OpenAI
client = OpenAI(
base_url="https://llm.ro2-labs.ai/v1",
api_key="ro2_...",
)
response = client.chat.completions.create(
model="llama3:70b",
messages=[{"role": "user", "content": "Summarize ITAR 22 CFR 120."}],
)
Supported parameters include messages, temperature, top_p, max_tokens, and stream. Streaming responses use Server-Sent Events, matching the OpenAI SSE format.
Free tier access is available with 100 API calls per month and no credit card required. Pro and enterprise tiers are available for production workloads.
https://llm.ro2-labs.aihttps://ro2-labs.ai/apihello@ro2-labs.ai