~7,500x
Faster than an on-board 8B model, answering from its own memory on a Jetson
Do not just prompt the model. Teach the system.
A local, teachable memory layer that sits around your models. It preserves verified work, corrections, context, and route state so expensive model work runs only when needed, on a single edge device or a multi-GPU server. Today it is a family: Studio for reuse, a drop-in Gateway for any provider, and Fleet for many devices.
From Jetson at the edge to x86_64 multi-GPU servers in the data center. 7-day free trial per device. See the benchmarks.
NeuraFrame started as a reuse engine. The same teachable memory now shows up in four ways, so you can put it wherever your model work happens. Not sure which fits? Start here: what you need, which to choose, and first reuse in about 10 minutes.
The reuse engine. It preserves verified work and corrections so repeated model work is not recomputed, from exact repeats up to entities followed across a video, on your own hardware. New: code footage against your OWN standard with codebooks, one model call per real finding. Complex identification
The drop-in. Put it in front of any model provider with a one-line change and serve repeats from memory, with freshness windows, pinned answers you teach it, and a live readout of what it saves. Gateway docs
For many devices. Curated round-trip learning: devices learn, you approve what should spread, and it is distributed back, signed. How Fleet works
Modern AI systems repeatedly process the same prompts, documents, images, workflows, corrections, and context. That repeated work costs latency, energy, GPU time, tokens, and money.
NeuraFrame Studio™ sits around existing models and local workflows. It tracks verified results, corrections, context, and route state so familiar work can be reused, uncertain work can be reviewed, and novel work can still go to the model. It also knows that answers live in time: a stable answer is reused for as long as it stays true, and a time-sensitive one (weather, a price, anything current) is re-fetched once its freshness window passes, so a saved call never becomes a stale answer.
For Jetson Orin, ARM64 edge devices, robotics, and local AI systems.
For Linux workstations, servers, local LLM hosts, and development machines.
If the license expires, NeuraFrame™ enters pass-through mode. Your model can still answer directly.
The results a cache cannot produce, measured on ResNet-152, Llama 8B, and a Jetson Orin, and re-run against the shipped engine on every release.
On the same recurring vision stream, a tuned whole-image cache served 6 to 13 stale answers over real changes. NeuraFrame™ served zero, while sending the model about a hundredth of the pixels. A cache cannot be both safe and thrifty; a memory that understands change can.
40% of Llama 8B calls avoided on same-meaning, different-words questions at a conservative threshold, with 100% reuse precision. Byte-matching caches score zero here by definition.
A correction changed every future answer immediately, and taught facts composed correctly with each other. That is the difference between a chat history and a memory.
About one fifth the energy of a prefix-cache-only path on the same workload, because whole answers are reused, not just prompt prefixes.
Three answers and a 10-minute first run: what you need, which one fits what you are running, and your first reuse served from memory. Already know? Grab the download and go.