A Reddit post has caught the attention of AI enthusiasts: a seller is offering a server equipped with four Tesla V100 cards and a total of 128 gigabytes of video memory, all liquid-cooled with a 360-degree system, for a price of $3,687. The amount is remarkably low compared to new workstations with similar capacity, and it brings back a practical question: does it make sense to invest in hardware a few generations old to run large language models locally?

The listing and the configuration

According to the post, the system is described as a 'V100 128G Liquid-Cooled Graphics Card Dock' – a docking station for four V100 GPUs with integrated liquid cooling. No details are provided about the rest of the configuration – processor, system RAM, storage – but the heart of the package is the four Tesla V100 cards, each likely the 32GB HBM2 variant, for a total of 128GB. The price of $3,687, obtained through conversion, appears to be for a used or refurbished system, yet the full liquid cooling setup suggests a solution designed for sustained workloads and low noise.

The Tesla V100 in 2025: why it still matters

Launched in 2017, the Tesla V100 was the first NVIDIA GPU to introduce Tensor Cores optimized for deep learning. Its specs – 32GB of HBM2 with 900 GB/s bandwidth, support for FP16 and FP32 operations, and about 15 TFLOPS of single-precision compute – still allow it to handle inference of LLMs with 7 to 13 billion parameters using 4- or 8-bit quantization. For larger models, combining four cards enables model partitioning via tensor parallelism or serving multiple requests in parallel. It certainly doesn’t compete with A100s or H100s for training, but for those who want to run open-source models locally without relying on cloud services, such a configuration can be an attractive entry point, especially when total ownership cost is evaluated over a two- or three-year horizon.

Liquid cooling: quietness and density

The 360-degree liquid cooling system is a distinctive feature. Passive V100 GPUs require significant airflow in servers, resulting in high noise levels. A liquid-cooled dock allows the cards to be housed in a relatively compact case and operate in residential environments or offices without acoustic discomfort, while keeping temperatures in check even under continuous load. This is crucial for those intending to place the hardware in non-dedicated spaces, such as home labs or small offices, and it reduces thermal degradation over time.

Implications for on-premise deployment

This listing fits a broader trend tracked by AI-RADAR: the growing availability of previous-generation hardware, combined with increasingly efficient serving frameworks, is lowering the economic barrier to on-premise inference. For organizations that must comply with data residency requirements or are comparing total cost of ownership with cloud alternatives, solutions based on used V100s can offer a viable path, provided they accept compromises in peak performance and software support (V100s no longer receive the latest CUDA optimizations). There is no one-size-fits-all answer: choosing between renting cloud GPU instances and making an upfront capital investment for a local server depends on inference volumes, latency requirements, and data sensitivity. Analytical tools like those available at /llm-onpremise can help quantify these trade-offs.

Ultimately, the announcement signals that the used AI hardware ecosystem is maturing, with integrated thermal solutions making management easier. For independent researchers, startups, and IT departments exploring self-hosting of LLMs, configurations like this could become a concrete alternative to costly cloud subscriptions.