·INFERENCE ARCHITECTURE

Complex context.
Parsimonious compute.

We engineer context optimization designed for frontier models: three critical bottlenecks, one unified engine.

ARCHITECTURE PAGES · 0110M+ WINDOWS
LONG-HORIZON AGENTS
RETAINED REASONING100% DETERMINISTIC
LONG-HORIZON AGENTS
10M+ WINDOWS
Parsim Long Horizon autonomous agent neural context trajectory

Deterministic Memory Across 100,000 Step Loops

Autonomous coding and research agents repeatedly crash when prompt history fills standard attention buffers. Parsim retains permanent system prompt anchors while compacting historical reasoning chains.

ARCHITECTURE PAGES · 0216.4X FACTOR
KV-CACHE COMPRESSION
HBM3e REDUCTION85.4% SAVED
KV-CACHE COMPRESSION
16.4X FACTOR
Parsim KV cache memory compression architecture

Subspace Low-Rank Key-Value Head Projection

By dynamically identifying shared attention manifolds across decoder heads, Parsim eliminates up to 85% of redundant key-value tensors before they consume High Bandwidth Memory (HBM3e).

ARCHITECTURE PAGES · 03O(N log N) SCALING
SUB-QUADRATIC PROJECTION
ATTENTION SCALINGO(N log N)
SUB-QUADRATIC PROJECTION
O(N log N) SCALING
Parsim sub quadratic token projection matrix

Triton-Accelerated Dynamic Sparse Kernel

Custom fused CUDA and Triton kernels route tokens to relevant receptive fields in sub-quadratic time, avoiding quadratic memory wall blowouts on frontier models like DeepSeek-R1 and Llama-3.3.