1Cat-vLLM: An AWQ 4-bit Inference Engine Optimized for Tesla V100 GPUs

1Cat-vLLM is a specialized fork based on vLLM, deeply optimized for Tesla V100 GPUs. It supports AWQ 4-bit quantized inference, is compatible with CUDA 12.8 and modern large models (e.g., Qwen3.5, MoE architectures), and aims to extend the practical lifespan of Tesla V100 GPUs, providing a feasible solution for users of this hardware to run modern large language models.

The original vLLM has limitations in supporting older GPUs. While Tesla V100 was once a mainstay for AI training and has been replaced by A100/H100, it still exists in large quantities in the second-hand market and cloud rentals with obvious cost-performance advantages. 1Cat-vLLM fills this gap, allowing V100 users to enjoy modern inference optimization technologies and extend the practical lifespan of this classic data center GPU.

AWQ 4-bit Quantization Support: AWQ is a quantization technique that preserves model accuracy, compressing the model size to about 25% of the original while maintaining acceptable inference quality; CUDA 12.8 Compatibility: Supports the latest CUDA 12.8 toolchain, facilitating deployment in Windows environments; Modern Model Verification: Has been verified to support large language models like Qwen3.5 27B/35B and MoE architecture models; Multi-GPU Support: Optimized for computing environments equipped with multiple Tesla V100 GPUs, supporting distributed inference load distribution.

System Requirements:

• Operating System: Windows 10 or later (64-bit)
• GPU: At least one Tesla V100 (SM70 architecture)
• CUDA Version: Must have CUDA 12.8 installed
• Memory: Minimum 16GB RAM
• Storage: At least 10GB of available space
• Network: Internet connection required to download software

Installation Process: Download the installation package from the GitHub Releases page, extract it, run the main application (.exe file), and allow network access permissions from Windows Firewall.

Troubleshooting for Startup Issues: Check if the Tesla V100 driver is up-to-date, close applications occupying GPU resources, and confirm CUDA 12.8 is installed correctly.

Although AWQ quantization can significantly reduce memory usage and improve inference speed, it has a slight impact on model output (an inherent characteristic of the quantization process). Users need to evaluate the trade-off based on the scenario:

• Scenarios with high fault tolerance (e.g., dialogue, creative writing): Usually acceptable;
• High-precision scenarios (e.g., code generation, mathematical reasoning): Need to carefully assess the impact.

Target Users:

• Users who own Tesla V100 GPUs and want to run modern large language models
• Developers who need to deploy AI inference services in Windows environments
• Researchers and enthusiasts seeking cost-effective inference solutions
• Institutional users hoping to extend the value of existing hardware investments

Notes: The project is explicitly optimized for Tesla V100; other GPUs may not work properly.

1Cat-vLLM is a highly targeted optimization project that addresses the practical pain points of Tesla V100 users. Through AWQ 4-bit quantization and CUDA 12.8 support, this generation of classic GPUs can continue to play a role in modern AI applications. For users who have V100 resources and want to explore large language model deployment, it is a solution worth trying, reflecting the value of software optimization in enhancing the value of old hardware.