Nvidia V100 32 Gb getting 115 t/s on Qwen Coder 30B A3B Q5
A $500 Nvidia V100 GPU outperforms Apple's latest M3 Ultra and M4 Max chips on AI inference tasks.
In a surprising benchmark for budget AI compute, a user has demonstrated that Nvidia's previous-generation V100 data center GPU, purchased second-hand for approximately $500, remains a formidable performer. The 32GB card, running the Qwen Coder 30B model with specialized A3B Q5 quantization, achieved an inference speed of 115 tokens per second. This performance notably surpasses that of Apple's latest and most powerful consumer silicon, the M3 Ultra and M4 Max, by a significant 20% to 100% margin on comparable AI models. The post has sparked a viral discussion among developers and hobbyists about the cost-per-performance value of decommissioned enterprise hardware versus new consumer or prosumer chips.
Despite being officially unsupported and known for high power consumption and noise, the V100's raw compute capability for large language model inference is challenging to beat at its current price point. The original poster is now exploring scaling this setup by acquiring three more V100s and connecting them via NVLink bridges, while also researching the market for more powerful but pricier A100 80GB cards. This trend underscores a growing niche in the AI community: building powerful, localized inference clusters from deprecated data center components, offering an alternative path to high-performance AI development outside of cloud API costs or expensive new hardware purchases.
- A used Nvidia V100 32GB GPU costs ~$500 and hits 115 tokens/sec on Qwen Coder 30B A3B Q5.
- Outperforms Apple's flagship M3 Ultra & M4 Max chips by 20-100% on similar AI inference tasks.
- Highlights a viable, low-cost path for local AI development using deprecated server hardware.
Why It Matters
It reveals a cost-effective hardware strategy for developers and researchers to run powerful LLMs locally, bypassing cloud costs.