BluTrain: A C++/CUDA Framework for AI Systems

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Adhitya Charan, Adwaid Suresh, Anuj Kumar, Aparna A, Dhanakumar K, Dharun M S, Dinesh G, Goutham Kumar Reddy K, Harshini V M, Jenifa D, Jona Delcy C A, Kathirvel S, Killi Uma Maheswara Rao, Kiruthik Kanna M, Kurra Vishnu Sai, Madhumithaa G K, Navin Kumar, Ram Charan Golla, Revathi T, Rishikkanth R, Sanjay Krishna M, Surendra Vendra
• Venue: arXiv: cs.AI
• Published: 2026-06-23
• Collected: 2026-06-24
• URL: http://arxiv.org/abs/2606.24780v1
• PDF: https://arxiv.org/pdf/2606.24780v1
• DOI: UNKNOWN

Plain-Language Brief

BluTrain is a C++/CUDA training framework for AI systems that provides better throughput and memory efficiency compared to industry-standard baselines like PyTorch in training a 124M-parameter GPT-2 model.

What the Document Says

• The paper introduces BluTrain, a C++/CUDA framework for AI systems.
• BluTrain is implemented natively with a typed tensor module, linear-algebra library, caching allocator, multi-mode distributed-execution module, and an MLIR-based deep-learning compiler.
• In formal evaluations, BluTrain outperforms industry-standard baselines (not specified) in both throughput (407K tokens/s compared to PyTorch's 395K tokens/s) and memory efficiency (up to a 22% footprint reduction).
• BluTrain preserves numerical fidelity and converges to a marginally lower final validation loss.

Key Concepts Mentioned

• AI training framework
• C++/CUDA
• GPT-2 model
• Throughput
• Memory efficiency

Claims Made by the Document

• BluTrain provides better throughput compared to industry-standard baselines.
• BluTrain provides better memory efficiency compared to industry-standard baselines.

Evidence Used

• 407K tokens/s for BluTrain versus PyTorch's 395K tokens/s
• up to a 22% footprint reduction

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Progress in deep learning is, at scale, more a matter of systems engineering than of modelling: the behaviour of a model in training (its throughput, its memory footprint, and the numerical fidelity of the result) is determined less by the architecture itself than by how that architecture is expressed on the hardware. To achieve absolute control over this hardware expression while abstracting away systems complexity to make modelling seamless and eliminating the need for repetitive orchestration logic, BluTrain was architected from first principles as a robust, lightweight, and architecture-general training framework in standard C++ and the core CUDA programming model. Every layer is implemented natively: a typed tensor module with reverse-mode autograd, a linear-algebra library, a caching allocator, a multi-mode distributed-execution module, and an MLIR-based deep-learning compiler. In formal evaluations training a 124M-parameter GPT-2 baseline in FP32 on an 8-GPU 6000 Ada system, BluTrain outperforms industry-standard baselines in both throughput (sustaining an average of 407K tokens/s versus PyTorch's 395K tokens/s) and memory efficiency (achieving up to a 22% footprint reduction), while strictly preserving numerical fidelity and converging to a marginally lower final validation loss. With every layer explicitly open to native tuning, the performance ceiling is the framework's own to raise.