Independent AI Research & Infrastructure
Intelligence,
unbundled.
Open components for building, adapting, and deploying intelligent systems.
Intelligence shouldn't be a black box.
Modern AI systems are overwhelmingly delivered as opaque, all-or-nothing monoliths. When something fails—or costs too much, or drifts—engineers are forced to guess from the outside.
Subsumptive is built on a different premise: intelligence should be assembled from discrete, inspectable building blocks that developers can understand, benchmark, and replace independently.
Inspectable by default
Every layer of an intelligent system—from tokenization to memory retrieval and execution—should be observable, deterministic where possible, and free of hidden state.
Replaceable components
Monolithic APIs couple your architecture to a single vendor's assumptions. Composable interfaces let you swap tokenizers, adapters, or runtimes without rewriting your system.
Engineering over alchemy
We treat AI infrastructure as systems engineering: explicit contracts, measurable latency budgets, and reproducible behavior under load.
Not another model.
A different way to build.
Subsumptive Blocks is a proposed modular architecture for assembling AI systems from discrete, interoperable primitives. Inspect each component below to explore its role and relationships.
Tokenizer Block
Decouples vocabulary and segmentation from model weights. Enables domain-specific compression, byte-level fallbacks, and deterministic token budgets before inference.
encode(input) -> TokenStream | decode(tokens) -> OutputBuilt in the open.
Foundational infrastructure should not require blind trust. Subsumptive Blocks is designed from the ground up as open, portable software you can read, fork, and run anywhere.
- Inspectable implementations01
- Composable interfaces02
- Community contributions03
- Portable deployment04
- No mandatory cloud dependency05
import {
TokenizerBlock,
MemoryBlock,
ModelAdapterBlock,
Runtime,
} from "@subsumptive/blocks"; // proposed interface
const system = Runtime.compose({
tokenizer: TokenizerBlock.byteLevel({ vocabSize: 65536 }),
memory: MemoryBlock.episodic({ maxContextTokens: 8192 }),
adapter: ModelAdapterBlock.lora({ rank: 16, target: "attention" }),
policy: {
deterministic: true,
maxLatencyMs: 120,
cloudFallback: false,
},
});
const trace = await system.execute(input);From components to systems.
While every Subsumptive Block is designed to run locally or on your own hardware, scaling composed systems across teams requires dedicated infrastructure. Subsumptive Cloud is our planned execution and registry layer.
Private Registries
Versioned, content-addressed distribution for custom tokenizers, adapters, and memory schemas across teams.
Managed Components
Stateless and stateful Subsumptive Blocks provisioned with explicit SLA bounds and deterministic isolation.
Hosted Inference
Low-latency execution fabric tuned for modular graphs, heterogeneous accelerators, and predictable tail latency.
Usage-Based Infrastructure
Transparent metering per block invocation and token stream—no opaque platform markups or seat minimums.
Measure everything.
Assume nothing.
Our research program focuses on the physical and economic constraints of deployed intelligence—isolating variables rather than relying on aggregate benchmarks.