The Hybrid Intelligence for enterprise edge and physical AI.
Local first. Cloud only when it counts.
One command. Four systems breakthroughs.
Existing serving systems optimize single-model token speed in the datacenter. Fluxion Edge is built for the constrained, multi-model, multi-user reality of the edge.
Elastic memory for multi-user, multi-workload serving
LLM, embedding, and guardrail models colocate under constrained RAM. The KV cache grows and shrinks on demand, so many models and workloads share one device without competing for memory.
Auto-optimized for non-CUDA chips
Self-evolving kernel generation tunes itself to each chip family automatically — Intel XPU, AMD ROCm, Qualcomm, Apple Silicon. No hand-written kernels, no CUDA lock-in.
Hybrid local–cloud routing, with reasoning
Decides what stays on-device and what escalates to the cloud — privacy-preserving first, cost-saving by default, and more accurate than cloud-only at a fraction of the price.
Strong isolation for multi-user workloads
One box serves a whole team. Compute and memory isolation keep every inference session sandboxed from the next, with predictable latency under contention.
Cross hardware
Cross agentic apps
Day 0/1 model support
Hybrid intelligence, across the enterprise.
From the workforce's laptops to the robots on the floor — real-time, private work runs on-device, and the cloud is called only for the heavy, non-urgent fraction. Fluxion handles the split for every workload.
Real-time control local, heavy thinking in the cloud
Perception and control loops run on-device for millisecond latency and full offline operation. Background tasks — planning, mapping, fleet learning — escalate to the cloud only when it makes sense.
An AI teammate on every employee's machine
Agents run seamlessly beside daily apps on the 16/32 GB laptops your workforce already owns, private by default. The router reaches for a frontier model only on the hardest asks.
One box serves a whole team
A local LLM plus guardrail models serve many users at once, each session isolated from the next — with cloud escalation governed by policy.
Which model, on which chip, for which task.
An independent ranking across three axes — model × hardware × agentic benchmark. Pick the hardware you'd deploy and the benchmark you care about, then compare how every model actually performs. Planned coverage: 37 devices, 62 models, 8 agentic benchmarks.
| # | Model × Hardware | Prefill ▾ tok/s · 16K |
Decode ▾ tok/s · 64K |
Agent score ▾ PinchBench |
Battery ▾ hrs / charge |
Hardware ▾ one-time |
Score ▾ / 100 |
|---|
Match model + hardware to your latency, quality, and budget — before you deploy a single device.
See exactly what your silicon can do across every model — and prove its value to your customers.
See how your model performs on every edge device, and where to optimize next.