What this is
This week, a Reddit user in the LocalLLaMA community (a hub for enthusiasts running LLMs locally) posted that they used a vLLM fork called 1Cat-vLLM (vLLM is a leading open-source inference framework that optimizes throughput for serving multiple requests simultaneously) to get Nvidia's V100 GPU — released in 2017 — running 35-billion-parameter LLMs. Benchmarks show inference speed on the V100 approaches that of the new consumer chip Strix Halo.
This isn't a product launch — it's a performance optimization experiment from the open-source community. But it points to something worth noting: decade-old hardware, with the right software optimizations, can still run mid-sized LLMs.
Industry view
The mainstream narrative is "if you're compute-starved, buy the latest H100 or B200." This post offers the flip side: the compute bottleneck lives largely in the software layer, not the hardware layer. Community members cite parallel efforts — like optimized forks of llama.cpp (another lightweight inference framework) that push consumer GPUs close to data-center speeds.
But there are counterarguments. The V100 only ships with 16GB or 32GB of VRAM — not enough to load the current mainstream 70B+ models, and still stretched for serious production workloads. This kind of "old-GPU revival" fits private deployment scenarios for SMEs better than the scale-out services run by cloud providers. Stability and long-term maintenance of open-source forks are hidden costs too — you can't judge by benchmarks alone.
Impact on regular people
For enterprise IT: If your data center still runs V100s or even older cards, don't rush to scrap and replace. Software optimization can keep legacy assets productive.
For individual careers: People who can tune open-source inference frameworks will be scarcer than those who only know how to call APIs — especially at the "last mile" of AI deployment.
For the consumer market: As inference costs continue to fall, they'll eventually show up in AI service pricing. Per-token (the smallest unit of text a model processes) API billing may get cheaper than it is today.