I build hardware-aware optimization layers, deterministic execution frameworks, and low-level tensor routing middleware for distributed deep learning loops.
I am the author of renorm-native, a completely decoupled, self-bootstrapping middleware layer that eliminates cumulative memory fragmentation and delayed CUDA/ROCm OOM cliffs in long-context transformer architectures.
- Zero-Dependency Hook: Acts as a passive layout referee via an externalized profile matrix (
gateway_profiles.json). - Register-Fused Optimization: Bypasses off-chip HBM round-trips by locking dynamic tensor structures inside local SRAM registers via Triton.
If you are managing multi-node accelerator clusters or fighting unhandled allocation crashes mid-run, check out my definitive engineering guide on memory physics:
📑 [Read: Why Your PyTorch Models Crash at Step 200: The Physics of Cumulative Memory Fragmentation]https://medium.com/@adesoyetobe/why-your-pytorch-models-crash-at-step-200-the-physics-of-cumulative-memory-fragmentation-0b2fc37cd92c?postPublishedType=repub
An in-depth micro-architectural deep-dive exploring how uncoalesced memory transit steps leave jagged micro-gaps across physical cache lines, why standard caching allocators panic, and how block-level tensor padding stabilizes the VRAM floor permanently.
- Hardware Architecture: NVIDIA HBM3e layout mapping, AMD ROCm/wave64 sector alignment.
- Framework Runtimes: Deep PyTorch internals, custom Triton kernels, vLLM worker clustering, FSDP tensor sharding.
- System Design: Property-driven declarative metadata engines, zero-maintenance middleware.
📫 Let's stabilize your compute: If your infrastructure team is scaling high-throughput serving architectures, enterprise SFT runs, or complex agent loops and hitting memory ceilings, open an issue on renorm-native or reach out for custom cluster profiling.