KailStream
Local AI Inference Runtime
KailStream is Kaniskten Programming’s runtime engineering project designed to investigate how neural network models can execute efficiently on everyday developer hardware. By orchestrating memory across storage, system RAM, and GPU accelerators, KailStream explores practical on-device AI inference without cloud dependencies.
Interactive Execution Pipeline
Memory tiering and data flow: NVMe Storage → System RAM → GPU VRAM → Execution Core.
Concept visualization of a generic local-inference data path. It does not state which features KailStream has implemented.
System RAM
Staging area between storage and the GPU. Streaming buffers and caches typically live here.
KailStream status: Release status not yet published
Capability Register
Verified implementation status for runtime features. Experimental or unconfirmed items are labeled transparently.
The Three Memory Tiers of Local AI
NVMe Storage
Persistent storage holding quantized weights (e.g., GGUF or safe tensors). Streaming chunks directly to RAM minimizes memory footprint.
Host RAM
High-capacity working memory staging weights and KV caches when GPU memory alone is insufficient for the full parameter set.
GPU VRAM
Ultra-high-bandwidth memory (e.g., 280+ GB/s) feeding tensor cores for low-latency matrix multiplication and attention scoring.