Mission & Compute Philosophy

Democratizing Frontier Intelligence

Valerois AI was founded on a singular mathematical conviction: foundation models do not need quadratic compute clusters if their underlying temporal operators are engineered correctly.

The 63× Efficiency Breakthrough

In standard AI labs, training 100M+ parameter models requires hundreds of thousands of compute hours on multi-node H100 clusters, digesting 300 billion to 1 trillion tokens.

Valerois AI developed Bifrost CSL on a single consumer AMD Radeon RX 9070 XT desktop GPU. By substituting attention matrices with hierarchical dilated state convolutions and SiLU gating, our 116.8M Valkir model reached 54.0% HumanEval AST validity in just 36 cumulative hours on 4.74 billion tokens.

This proves that high-density architectural formulation beats brute-force token saturation by over 60×.

Inspect Valkir Checkpoint Get In Touch

Hardware Optimization TelemetryAMD RX 9070 XT (16 GB)

Native PyTorch:29,550 tok/s • 8.12 GB

ROCm 7.2 Inductor:

37,243 tok/s • 4.87 GB

↑ 26% faster training speed and 40% reduction in peak VRAM via fused causal kernels and memory planning.

Strategic Development Roadmap

Scaling Valkir & Bifrost CSL

Phase 1 • Complete

Valkir 16L Reference

116.8M parameters, 4.74B tokens, 54.0% HumanEval AST validity. Full proof of O(1) circular state memory.

Phase 2 • Active

Valkir 140M Scale-Up

20 layers, hidden dim 768, target 60%+ HumanEval AST, enhanced multi-hop mathematical reasoning with 10B tokens.

Phase 3 • Next

1.5B Frontier Compute

Medium frontier scale model trained on 50B tokens with academic and industry compute grants.

Phase 4 • Long-Term

Enterprise Agentic SWE-bench

Full enterprise scale with automated GitHub issue resolution and agentic code self-correction.

Intellectual Property & Licensing Notice

Bifrost CSL is a proprietary architecture. All Rights Reserved. Public documentation and benchmark scorecards are released under Creative Commons CC BY-NC-ND 4.0 with timestamped prior art disclosures.