State-Space Foundation Architectures for Infinite Context AI
At Brute Force Intelligence, we design constant-memory state-space model (SSM) architectures that replace resource-intensive Transformer KV-caches. Our flagship research delivers linear runtime complexity and constant GPU RAM consumption across unlimited context windows—enabling scalable, high-throughput inference on enterprise GPU infrastructure.
Loci-S4D combines continuous diagonalized state-space discretization with quasi-random Halton sequence anchoring and dynamic Gaussian window focus. This hybrid design allows the network to maintain precise long-range temporal recall without requiring quadratic memory expansion during generation.
Replaces token-by-token spatial attention with a continuous temporal state operator. Leverages normal-plus-low-rank spectral decomposition to preserve long-range dependencies across millions of tokens without forgetting early context.
Employs low-discrepancy quasi-random spatial sequences to anchor recurrent memory states across high-dimensional latent vectors, preventing gradient degradation over extended token sequences.
Our step-by-step technical roadmap to deliver our first production-ready, medium-sized (20B–50B parameter) Loci-S4D foundation model to market.
Optimize custom Triton GPU kernels for channel-parallel state recurrences to maximize Model Flop Utilization (MFU ≥ 90%) on NVIDIA H100 and A100 clusters. Validate zero memory decay on synthetic sequences up to 1M tokens.
Execute pre-training runs on 500 Billion tokens of curated code, mathematical reasoning, and long-form document datasets. Evaluate needle-in-a-haystack accuracy and inference speed against standard Llama-3 and Mamba baselines.
Scale up compute to train the full 20B–50B parameter architecture across 2.5 Trillion tokens. Deploy 3D parallelism (Tensor, Pipeline, and Sequence Parallelism) using AWS EC2 UltraClusters to deliver enterprise-grade performance.
Apply Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) for multi-step reasoning. Package the final weights into lightweight vLLM and TensorRT-LLM runtimes for low-latency API and edge deployment.
Brute Force Intelligence leverages high-performance AWS cloud infrastructure to manage large-scale data pipelines, model checkpointing, and GPU cluster orchestration.
• Compute: AWS EC2 p5.48xlarge (8x H100) and g5.12xlarge (4x A10G) instances for distributed pre-training and kernel validation.
• Storage: Amazon S3 for high-throughput streaming of terabyte-scale datasets and distributed checkpointing.
• Networking: AWS Elastic Fabric Adapter (EFA) enabling 3.2 Tbps node-to-node interconnect for low-latency multi-node scaling.