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machine-learning-infrastructure

Here are 25 public repositories matching this topic...

Compiler MVP that detects Transformer fusion patterns, generates optimized CUDA kernels with WMMA Tensor Cores, and executes them on real GPU hardware — 10.5 TFLOPs on RTX 2070, correctness validated against PyTorch.

  • Updated Jul 26, 2026
  • Python

tpu-doc is a zero-dependency diagnostic binary for Google Cloud TPU environments that instantly validates hardware health, discovers software stack configurations, and provides AI-powered log analysis to eliminate expensive debugging downtime.

  • Updated Jan 6, 2026
  • Rust

Analytical benchmark connecting pipeline parallelism and KV cache management: bubble-time overlap for disaggregation, prefix reuse tax of PP stages, and tiering speedup across NVLink, InfiniBand, and PCIe

  • Updated Jul 26, 2026
  • Python
tungsten-alpha

Tungsten Alpha: A JAX-native, 7-layer Adelic-Riemannian Operating System. Implements resurgent gradient flow through discrete logic and Levi-Civita parallel transport for decentralized XLA clusters. Hardened for zero-jitter, systolic array execution.

  • Updated Mar 16, 2026
  • Python

Monte Carlo simulation benchmark for MoE expert activation memory variability and admission control under non-deterministic routing — including routing skew amplification, top_k sweep, and policy comparison across uniform, skewed, and very_skewed distributions

  • Updated Jul 26, 2026
  • Python

A research-grade course in Numerical Methods and Optimization for ML. No deep learning libraries—just pure math and NumPy. Rebuilds automatic differentiation engines, stable SVD/Cholesky algorithms, quasi-Newton solvers (BFGS), stable log-sum-exp layers, and Gaussian Process regressions from scratch

  • Updated May 27, 2026
  • Jupyter Notebook

Analytical benchmark for sliding window attention KV cache management: quality vs window size tradeoffs, SWA vs eviction comparison, prefix sharing interaction, and operational window recommendations across four attention distributions

  • Updated Jul 26, 2026
  • Python

Discrete-event simulation benchmark for LLM request cancellation handling, measuring wasted compute, KV residency overhead, and survivor latency recovery across polling and push-based strategies.

  • Updated Jul 25, 2026
  • Python

Simulation benchmark for canary deployment strategies in LLM model updates, measuring safety, rollback behavior, bad exposure, and rollout speed across good, bad, and marginal candidate scenarios

  • Updated Jul 26, 2026
  • Python

Discrete-event simulation for LLM serving capacity planning: how many GPUs for a p99 TTFT SLO under ShareGPT traffic, batching fragmentation, and autoscaling lag — with cliff analysis and three levels of planning rigor.

  • Updated Jul 26, 2026
  • Python

Analytical benchmark connecting tensor parallelism and KV cache management in LLM serving: GQA sharding regimes, all-gather vs recompute tradeoffs, PCIe viability, and operational decision matrix across NVLink, InfiniBand, and PCIe

  • Updated Jul 26, 2026
  • Python

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