KailStream
Local AI Inference Runtime
Running large AI models on local hardware is mostly a memory problem. KailStream is our work on moving model data between storage, system RAM, and GPU memory — so large models can run on the hardware people already own.
Data pipeline concept
An interactive 3D view of data moving from storage through system RAM and GPU memory to compute. This is a conceptual illustration only — not a diagram of the actual KailStream implementation.
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
The problem
Modern AI models can be larger than a GPU's memory, and sometimes larger than system RAM. Running them locally means deciding what lives where, and when to move it.
Without careful memory orchestration, models that could theoretically run on a machine fail in practice because no single memory tier is large enough to hold them.
The approach
KailStream treats storage, RAM, and GPU memory as one unified pipeline. Rather than loading the entire model into one location, data is streamed to where it is needed.
How each specific technique is implemented is documented on this page only once it has been confirmed against the actual codebase.
Pipeline stages
The conceptual data flow from persistent storage to active compute.
01
Storage
Model weights reside on disk in a quantized or full-precision format.
02
System RAM
Layers or tensors are loaded into system memory as needed.
03
GPU VRAM
Active tensors are transferred to GPU memory for acceleration.
04
Compute
Forward pass executes on the GPU or CPU for inference.
Concept overview. Does not assert which stages KailStream has implemented.
Capability status
Each item is marked Implemented, Experimental, Planned, or Not yet published. Nothing is claimed beyond what has been confirmed.
- Local model inferenceNot yet published
- Memory management across storage, RAM and GPU memoryNot yet published
- Quantized model loadingNot yet published
- Streaming model data from storageNot yet published
- Expert caching for mixture-of-experts modelsNot yet published
- Hardware backends (CPU, GPU, accelerators)Not yet published
Benchmarks
No benchmarks have been published. When they are, each result will list hardware, model, quantization, backend, context length, method, throughput, and memory use.