Litespark Inference on Consumer CPUs: Custom SIMD Kernels for Ternary Neural Networks

Problem
This preprint addresses the significant gap in the deployment of large language models (LLMs) on consumer-grade hardware, specifically personal computers, which are often underutilized for AI workloads due to the prohibitive computational requirements of standard inference methods. Existing frameworks inadequately leverage the potential of ternary neural networks (TNNs), which constrain weights to {-1, 0, +1}, thus failing to optimize for the integer operations that could significantly reduce computational overhead.

Method
The authors introduce Litespark-Inference, a novel inference framework that employs custom SIMD (Single Instruction, Multiple Data) kernels to optimize the execution of TNNs. The core technical contribution lies in replacing traditional matrix multiplication operations with optimized integer addition and subtraction, specifically targeting the integer dot product instructions available on modern CPUs. This approach allows for efficient computation without the need for floating-point arithmetic, which is typically resource-intensive. The implementation is designed to be user-friendly, being pip-installable and directly integrable with the Hugging Face ecosystem, facilitating ease of use for practitioners.

Results
Litespark-Inference demonstrates substantial performance improvements over standard PyTorch inference on Apple Silicon, achieving a 9.2x faster time-to-first-token, 52x higher throughput, and a 14x reduction in memory usage. These results are corroborated by similar performance enhancements observed on Intel and AMD processors, indicating the framework’s broad applicability across different consumer CPU architectures. The benchmarks highlight the effectiveness of the custom SIMD kernels in fully utilizing the computational capabilities of consumer hardware, thus making TNNs more accessible for real-time applications.

Limitations
The authors acknowledge several limitations, including the potential for reduced model expressiveness due to the ternary weight constraint, which may not be suitable for all applications. Additionally, the framework’s performance is contingent on the specific hardware capabilities of the CPU, which may vary across different consumer devices. The paper does not address the implications of model training with ternary weights or the potential trade-offs in accuracy that may arise from this quantization approach. Furthermore, the reliance on SIMD instructions may limit compatibility with older CPU architectures that do not support these operations.

Why it matters
The implications of this work are significant for the deployment of AI models in resource-constrained environments. By enabling efficient inference of TNNs on consumer CPUs, Litespark-Inference democratizes access to advanced AI capabilities, allowing a broader range of users to leverage LLMs without the need for expensive hardware. This advancement could catalyze further research into lightweight model architectures and optimization techniques, potentially leading to the development of more efficient AI systems that can operate effectively on a wider array of devices.

Authors: Nii Osae Osae Dade, Tony Morri, Moinul Hossain Rahat, Sayandip Pal
Source: arXiv:2605.06485
URL: https://arxiv.org/abs/2605.06485v1