DeepSeek Performance Meets 1-bit Scaling
Large language models (LLMs) are driving incredible advances in AI, but they come at a steep cost-massive compute requirements, huge memory footprints, and enormous energy consumption. Even open models like DeepSeek, while powerful, are often out of reach for most individuals and organizations without significant resources.
But there's a new frontier. Microsoft's BitNet b1.58, a 1-bit LLM architecture, demonstrates that it's possible to achieve comparable performance to full-precision models with a fraction of the cost and energy consumption.
We believe it's time to bring these innovations together. Jump to our roadmap to see how we plan to make this vision a reality.
Develop and benchmark a small-scale 1-bit LLM prototype to demonstrate feasibility and performance parity at a smaller scale.
Scale the model training to DeepSeek-sized datasets using the optimized architecture and quantization techniques.
Compare the scaled model's performance and efficiency against existing models, including DeepSeek and other full-precision LLMs.
Deploy a distributed computing infrastructure inspired by Aria-node to coordinate training across decentralized networks. This system will utilize encrypted gRPC (gRPC Remote Procedure Call) channels as the backbone for inter-node communication. Each node in the network will host a subset of the neural network-specifically, a distinct chunk of experts in a mixture-of-experts (MoE) architecture. gRPC will manage the orchestration of expert selection, activation routing, and model update propagation. By leveraging efficient streaming capabilities and bi-directional communication of gRPC, we aim to simulate distributed training and inference workflows where different nodes activate only the necessary experts, optimizing both compute and bandwidth.
We also plan to harness NVIDIA's PTX (Parallel Thread Execution) language to directly interface with GPU hardware for low-level parallelism optimization. Our roadmap includes experimental simulations of quantum bits (qubits) using PTX for GPU-based environments. While PTX optimizes low-level parallelism on NVIDIA GPUs, we will also explore TPU-specific optimizations using Google's XLA (Accelerated Linear Algebra) compiler stack. This dual approach allows us to target both GPU and TPU ecosystems effectively. The quantum simulation efforts aim to blend classical parallelism with quantum-inspired computation, opening new possibilities for efficiency and performance gains across hardware platforms.
Release the model, training recipes, and tooling as open-source resources, enabling others to build upon this work.
Want to dive deeper into the research and technologies that power this project? Visit our Research & References page for key papers and supporting tools.
Follow our journey as we push the boundaries of LLM efficiency and accessibility. Subscribe for updates or contribute to the project on GitHub.
Together, we can reshape what's possible with AI.