Edge finds the largest model that stays above your accuracy threshold within each device's memory and latency limits. If data can't leave the device, it can fine-tune there and push the resulting adapter to every device running the same base model.
Edge lists every device with the model it runs, its tokens per second and its free memory, and can run fine-tuning jobs on the devices that hold the data.
Weights, KV cache, vision encoder and runtime overhead against the memory the device actually has free, and the decode speed its bandwidth allows.
Quantise the weights, KV cache and vision encoder, prune layers if needed, and check accuracy after each step. Edge stops before the model drops below your threshold.
QLoRA fine-tuning within the device's memory using gradient checkpointing, with a live loss curve and an estimate of the improvement before you start.
GGUF, MLX, ExecuTorch, Core ML, QNN and TensorRT, signed and versioned, with staged rollouts.
NVIDIA Jetson Orin and Thor · iPhone and Apple silicon · Pixel and Android NPUs · Qualcomm Snapdragon and Dragonwing · Raspberry Pi 5 with Hailo · x86 and Arm CPUs.
On many devices, yes. QLoRA fine-tunes of 1–8B models fit in the memory of a Jetson Orin, an Apple M-series machine or a workstation GPU. Edge plans the job to fit the device and estimates the time and the improvement before it starts.
Edge exports to Qualcomm QNN, to Apple's Neural Engine through Core ML, and to Hailo on the Raspberry Pi, and checks accuracy again after conversion.
Send us the model, the hardware and your target, whether that's tokens per second, latency, memory or cost. In a 30-minute call we'll tell you whether we think it's doable and roughly what it would take.
