Edge Language Model Inference with Ternary Quantization
Large language models running on edge — sparse attention, compressed KV cache, local personality.
Explore the Vision
Discover this technology through five complementary perspectives — from technical architecture to partnership outcomes. Each layer reveals a different aspect of how this innovation creates value.
Large language models running on edge — sparse attention, compressed KV cache, local personality.
What It IS
Technical VisionThe architectural essence — what makes this technology work
A large language model running entirely on a local device — ternary sparse attention gating 85% of computation, compressed key-value cache fitting in on-chip memory, responses personalised to the user. A personal language model that never phones home.
Abstract
Deployment of large language models to edge devices via ternary quantization, enabling on-device LLM inference under 500ms latency.
Visual Essence
A large language model running entirely on a local device — ternary sparse attention gating 85% of computation, compressed key-value cache fitting in on-chip memory, responses personalised to the user. A personal language model that never phones home.
Technology Domains
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