BareTorch is a commercial developer SDK and runtime engine that compiles subquadratic, O(1) state memory models natively onto Apple, Qualcomm, and ARM NPUs—enabling ultra-fast, low-memory local LLM execution without Triton or CUDA fallbacks.
01 / Commercial Developer SDK
A commercial developer platform enabling software engineering teams to compile, quantize, and deploy sub-billion parameter LLMs directly to iOS, Android, and Mac hardware with zero CUDA lock-in.
Import standard PyTorch or Hugging Face weights directly into the BareTorch CS-LRAD compilation pipeline.
Converts quadratic attention into subquadratic O(1) state memory sequence mixers using native GEMM matrix primitives.
Exports `.pte`, CoreML, and ONNX binaries natively targeting Apple Silicon, Snapdragon, and ARM NPUs.
Deploy responsive local AI features into iOS and Android apps without server latency or monthly cloud API bills.
Run low-latency, low-wattage autonomous decision models on local NPU hardware with zero internet dependency.
Process sensitive enterprise data on end-user devices with zero server-side telemetry or compliance risks.
02 / Structural Paradigm Shift
Traditional LLMs either burn through mobile battery with quadratic attention tax or fail to compile on non-NVIDIA NPU chips due to custom CUDA kernels.
SOTA subquadratic models (Mamba, FlashKDA, GDN2) rely on low-level CUDA/Triton kernels. Edge compilers (ExecuTorch, CoreML, QNN) cannot lower them, causing crashes or slow CPU fallbacks.
Standard Transformers (Llama 3.2 1B) export cleanly, but their growing KV-cache reads gigabytes from mobile LPDDR RAM every token—stalling generation speed and draining battery.
BareTorch architectures structure sequence states into pure PyTorch matrix block multiplications (`matmul`). 100% subquadratic efficiency with universal compiler portability.
03 / Foundational Innovation
Architected by Kaggle Grandmaster talent and tested on commodity multi-GPU clusters, BareTorch layers challenge SOTA cloud models on pure GEMM paths.
Chunk-Segmented Low-Rank Associative Delta Engine
Utilizes a low-rank (r=8) information bottleneck that acts as an implicit noise filter for historical memory while reducing state-update memory bandwidth by 6x. Outspeeds Triton GDN2 by 1.8x during step inference.
Causal Block-Kronecker Cascade
Structures the sequence timeline into a multi-scale binary tree (S=11 stages). Achieves an impressive 18.64 validation perplexity at the 200M Base tier, closing the gap with FlashAttention Transformers.
Subquadratic Interleaved with GQA & RoPE
The production MVP target: interleaving 3 subquadratic layers with 1 Grouped-Query Attention layer cuts KV-cache RAM footprint by 80% while retaining 100% Needle-in-a-Haystack retrieval accuracy.
04 / Pretraining Runway
Pretrained over a 4.2B token runway on commodity RTX 4090 / Threadripper hardware. CS-LRAD natively outspeeds Triton-fused kernels on wall-clock execution.
| Model Configuration | Val PPL | Train Time (s) | Inference Speed | Compilation & Runtime Profile |
|---|---|---|---|---|
| CS-LRAD Base (200M) | 18.92 | 27,223.50s | 96.23 tok/s | Pure GEMM • Outspeeds Triton & Flash |
| Transformer SOTA Base (FlashAttn) | 17.43 | 27,937.40s | 87.02 tok/s | Requires FlashAttention CUDA kernel |
| GDN2 Base (Triton Kernel) | 15.57 | 29,983.23s | 53.66 tok/s | Custom Triton kernel • Fails on NPU |
| CBKC Base (200M) | 18.64 | 54,266.66s | 29.35 tok/s | Pure GEMM Binary Tree • High Capacity |
| CS-LRAD Small (100M) | 24.08 | 12,342.54s | 113.41 tok/s | Pure GEMM • Fast convergence |
| Transformer SOTA Small | 23.05 | 11,094.79s | 113.37 tok/s | FlashAttention Baseline |
05 / Leadership & Core Team
BareTorch is architected and executed by top 0.1% global machine learning talent with hands-on experience scaling enterprise AI infrastructure platforms.
Founder & Principal AI Architect
Model Rampage / BareTorch AI Platform
Kaggle Grandmaster (Top 0.1% globally) with 8+ years of competitive deep learning execution. Ex-Abacus.ai infrastructure experience building enterprise MLOps pipelines and serverless model serving engines. Leading BareTorch's core subquadratic research and native NPU compiler target strategy.
100% Standard PyTorch Ops
Every BareTorch layer is written in high-level PyTorch matrix equations (`matmul`, `silu`, `add`). Because there are no custom low-level CUDA bindings or Triton code, edge compilers lower the model graph natively into NPU hardware instructions.
# Low-Rank Associative Delta Engine (CS-LRAD)
# Pure GEMM execution pass over C=32 macro-chunks
U_decayed = (U * beta_gate) * (exp_Lambda[:, :, :, -1:, :] / exp_Lambda)
S_historical = torch.matmul(
M_chunks,
torch.matmul(U_decayed.transpose(-1, -2), V).view(B, H, N, r * d_h)
).view(B, H, N, r, d_h)
Y_global = torch.matmul(R * exp_Lambda, S_historical) * scaling
Y_local = torch.matmul(torch.matmul(Q, K.transpose(-1, -2)) * M_links, V)
Out = (Y_local + Y_global).view(B, L, inner_dim)06 / Open-Source Codebases
Inspect our fully validated PyTorch training frameworks, subquadratic layer definitions, and step-inference benchmarking scripts.
Contains prototyping modules, custom sequence mixer definitions (CS-LRAD, CBKC, CCRS, COFE, CKTS), loss functions, and local multi-GPU benchmarking loops.
Production-ready pretraining and distributed DDP pipeline optimized for scaling 500M to 4B hybrid foundational models across multi-node GPU cloud clusters.
07 / Company Credentials
BareTorch is built by an independent technology startup with direct experience operating at Silicon Valley AI infrastructure platforms (Abacus.ai) and competitive global benchmarks.
8+ years of competitive deep learning execution, optimizing non-linear model paths against rigorous global benchmarks.
Direct experience building, scaling, and integrating enterprise MLOps pipelines and serverless model serving loops.
Targeting native execution on mobile devices (Pixel 8 Pro, iPhone) via ExecuTorch, ONNX Runtime, and Apple CoreML.