Skip to main content

How Surogate Works

Surogate is designed from the ground up for maximum throughput and efficiency when training Large Language Models (LLMs) on modern NVIDIA hardware. Unlike many frameworks that rely heavily on high-level abstractions which introduce overhead, Surogate bridges the gap between Python's flexibility and CUDA's raw performance.

Core Architecture​

1. Native C++/CUDA Execution Engine​

The heart of Surogate is a high-performance engine written in C++ and CUDA. This allows for:

  • Zero-overhead execution: Minimized Python-to-CUDA dispatch latency.
  • Custom Kernel Fusions: Specialized kernels that combine multiple operations (like activation, normalization, and projection) into a single pass, drastically reducing memory bandwidth bottlenecks.
  • Memory Management: Fine-grained control over GPU memory allocation and reuse.

2. Multi-threaded Scheduler​

Surogate uses a custom multi-threaded backend that handles data loading, gradient synchronization, and kernel dispatch in parallel. This ensures that the GPU is never idling while waiting for the next batch of data or for CPU-bound tasks.

3. Mixed-Precision & Quantization​

Surogate is at the forefront of mixed-precision training:

  • BF16 & FP8: Native support for modern data types on Hopper (H100/H200) and Blackwell (B200) GPUs.
  • NVFP4: Cutting-edge support for 4-bit floating point training on Blackwell architectures.
  • Stochastic Rounding: Used in low-precision training to maintain numerical stability without sacrificing speed.

Scalability​

Multi-GPU Training​

Surogate implements a high-efficiency Distributed Data Parallel (DDP) strategy. Using multi-threading, it overlaps gradient communication with computation, allowing for near-linear scaling across multiple GPUs.

Multi-Node with Ray​

For very large-scale training, Surogate integrates with Ray to manage multi-node clusters. This provides a seamless transition from a single workstation to a massive GPU cluster.

Deployment & Production​

Surogate is built for reliability. With deterministic configurations and explicit training "recipes," users can expect predictable results every time.