Small models. Production-grade.
Every byte costs. We deploy language and vision models where the data already lives — Jetson, NVIDIA GB10, Mac M-series. No cloud bill, no egress, no round-trip latency.
Four reasons the cloud bill isn't inevitable.
Frontier models are great. They're not always the right tool. For most real workloads, a well-chosen 3B–8B model on the right hardware beats a cloud API on every axis that matters to a production system.
Data never leaves the device.
Healthcare records, student homework, courtroom video, call center transcripts — most real workloads can't move to a third-party cloud without lawyers, DPAs, and data-residency gymnastics. Running the model locally sidesteps all of it.
No per-token meter. Ever.
A Jetson AGX Orin runs at 60 watts. At cloud-API pricing, the same workload costs more every month than the hardware cost up front. Amortized over a year, it's not close.
No round trip.
Real-time video analysis, voice tutoring, robotics — anything interactive — dies on a 400ms network round trip. Local inference runs in the same process as the sensor, in milliseconds.
Works when the internet doesn't.
A tennis court in a dead zone. A field site with a flaky 4G uplink. A factory floor behind an air gap. If your product only works with cloud connectivity, your market is smaller than you think.
Hardware + model + inference runtime.
We don't pick by brand. We pick by thermal envelope, memory bandwidth, and the specific workload. Here's the stack we reach for most.
Where the model runs
- Jetson AGX Orin — 60W, 275 TOPS, 64 GB unified. Our default for edge inference.
- NVIDIA GB10 — desktop-class for latency-critical workloads; we used it for CoachClaw.
- Mac M-series — developer machines + small-team deployments; Ollama-native.
- Jetson Nano / Orin Nano — sub-20W for truly embedded use.
- x86 with NVIDIA RTX — when you need Windows compatibility + 24 GB VRAM.
What we reach for
- Nemotron Nano 8B — NVIDIA's reasoning SLM; Undervolt runs on this.
- Cosmos Reason 2 — physical reasoning VLM; RefereAI runs on this.
- Qwen 2.5 VL — strong open VLM; Sideline defaults here.
- Gemma 3n — runs in-browser via WebGPU; real option for client-side AI.
- OpenCLIP — embedding model for Studio Copilot's photo search.
- llama.cpp-quantized SLMs — when you need 3B params on a 12 GB box.
How it stays fast
- DeepStream — NVIDIA's video inference pipeline; 672 FPS on Jetson in our testing.
- RAPIDS — GPU-accelerated dataframes; how Undervolt processes 2.2M permits.
- Ollama — one-line local model serving; HD Research agents.
- llama.cpp — CPU/Metal quantized inference; CoachClaw on GB10.
- vLLM — throughput-optimized batch inference when we need it.
- WebGPU — browser-native inference, no server.
Four live systems. Zero cloud inference bills.
Every flagship product below runs its AI workload on-device. These aren't slides — they're running with real users right now.
Undervolt
Nemotron Nano 8B on Jetson AGX Orin, 60W, structuring 2.2M Austin building permits. RAPIDS for GPU-accelerated joins. 1st place NVIDIA DGX AITX.
RefereAI
Cosmos Reason 2 VLM on Jetson at the court edge. DeepStream 7.1 pipeline at 672 FPS. Chain-of-thought line-call reasoning without cloud. Video never leaves the site.
CoachClaw
Nemotron-grade SLM quantized via llama.cpp on NVIDIA GB10. Rules lookups, concussion protocol, practice plans — all from Telegram. Zero cloud dependency by design.
HD Research Hub
Ollama-backed agents for Huntington's Disease literature triage. Runs on Jetson or Mac — no API key, no data egress. Open source.
From "what model should we use" to "it's deployed."
The right-sized model
We benchmark open small models against your real workload — not a synthetic eval. Output: a model + quantization + hardware recommendation with numbers.
Inference stack, end-to-end
Hardware provisioning, OS + drivers, inference runtime (Ollama / llama.cpp / vLLM / DeepStream), monitoring, OTA update flow. Working device, not a notebook.
Wire it into the thing that ships
REST / WebSocket / gRPC / on-device SDK — we meet your product where it is. Agentic pipelines, tool-use, streaming, structured output. You get a product feature, not an inference endpoint.
Have a workload that shouldn't be in the cloud?
Free 30-minute call. We'll tell you honestly whether edge AI is the right call.