GLM-130B vs ggml.ai

GLM-130B, showcased at ICLR 2023, represents a groundbreaking open bilingual pre-trained model that stands out with its impressive 130 billion parameters. Developed for bidirectional dense modeling in both English and Chinese, the GLM-130B leverages the General Language Model (GLM) algorithm for pre-training and is optimized to run inference tasks on a single server setup whether it be the A100 (40G * 8) or the V100 (32G * 8). Furthermore, its compatibility with INT4 quantization means that the already modest hardware requirements can be reduced even further, allowing a server with 4 * RTX 3090 (24G) to support the model with minimal performance degradation.

As part of its training process, the GLM-130B has digested an extensive dataset consisting of over 400 billion text tokens, equally divided between Chinese and English. It boasts exceptional bilingual support, superior performance across various datasets when compared to its counterparts, and offers fast inference times. Additionally, this repository also promotes reproducibility by facilitating open-source code and model checkpoints for over 30 tasks.

ggml.ai is at the forefront of AI technology, bringing powerful machine learning capabilities directly to the edge with its innovative tensor library. Built for large model support and high performance on common hardware platforms, ggml.ai enables developers to implement advanced AI algorithms without the need for specialized equipment. The platform, written in the efficient C programming language, offers 16-bit float and integer quantization support, along with automatic differentiation and various built-in optimization algorithms like ADAM and L-BFGS. It boasts optimized performance for Apple Silicon and leverages AVX/AVX2 intrinsics on x86 architectures. Web-based applications can also exploit its capabilities via WebAssembly and WASM SIMD support. With its zero runtime memory allocations and absence of third-party dependencies, ggml.ai presents a minimal and efficient solution for on-device inference.

Projects like whisper.cpp and llama.cpp demonstrate the high-performance inference capabilities of ggml.ai, with whisper.cpp providing speech-to-text solutions and llama.cpp focusing on efficient inference of Meta's LLaMA large language model. Moreover, the company welcomes contributions to its codebase and supports an open-core development model through the MIT license. As ggml.ai continues to expand, it seeks talented full-time developers with a shared vision for on-device inference to join their team.

Designed to push the envelope of AI at the edge, ggml.ai is a testament to the spirit of play and innovation in the AI community.