InnomiumInsights
The engineering record behind the releases.
Model updates, benchmarks, architecture decisions, and Arena notes — shared across Agency, Arena, and Compute.
All insights
11 insights
vision
An Edge Vision Evaluation Protocol Teams Can Actually Run
A practical protocol for accepting person, vehicle, or fire detectors — scene splits, hard negatives, runtime proof, and alert workflow checks.
Innomium Vision TeamJuly 12, 2026platform
How Innomium Arena Challenges Are Structured
Arena turns bounded research questions into public challenge rounds — with baselines, submission contracts, and reviewable artifacts instead of slideware.
Innomium PlatformJuly 10, 2026vision
Ember: Outdoor Fire Detection for Mission Sites
Fire and early-smoke detection tuned for outdoor fuel, logistics, and perimeter monitoring — now shipping with live demos and an active builder challenge.
Innomium Vision TeamJuly 8, 2026vision
Vantage Reaches 93% on Highway Scenes
Vehicle detection optimized for toll lanes, merge points, and low-light traffic — compact enough for edge gates and parking operations.
Innomium Vision TeamJuly 5, 2026vision
Sentinel Hits 92% on Crowd Scenes
Our edge person detector now holds 92% accuracy on dense airport and stadium footage — in a 19MB ONNX package with a live browser demo.
Innomium Vision TeamJuly 2, 2026company
Buying AI Engineering When You Need Production, Not Slides
How to extend delivery with specialized vision and LLM engineering — without six-month hiring cycles or science-project deliverables.
InnomiumJuly 1, 2026vision
Building the Innomium Vision Layer
How we turn existing camera infrastructure into real-time intelligence — without new hardware, cloud lock-in, or heavyweight models.
Innomium Vision TeamJune 28, 2026vision
Distilling Vision Models 100× Smaller Than Foundation Backbones
Mission detectors do not need billion-parameter backbones. Here is how Innomium distills task-specific skills that stay accurate at a fraction of the size.
Innomium ResearchJune 15, 2026llm
Introducing Continuum1-9B
A fully linear 8.6B foundation model with 2M native context — hybrid GLA + Gated DeltaNet, open weights, and production kernels on Hugging Face.
Innomium LLM TeamMarch 1, 2026llm
Why Linear Attention for 2M Context
Quadratic attention does not scale to million-token workloads. Continuum's linear layers keep latency predictable for production pipelines.
Innomium LLM TeamFebruary 20, 2026
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