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XiongjieDai/GPU-Benchmarks-on-LLM-Inference

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Multiple NVIDIA GPUs or Apple Silicon for Large Language Model Inference?

7 mentions on Hacker News by 7 people: 0 stories, 7 comments · first 2024-01-09 · latest 2026-03-09

Mentions per year 7 mentions · busiest year 2024 (4)
20082026
  1. A 10GB 3080 still beats even an M2 Ultra with 192GB... memory bandwidth is not the only factor. https://github.com/XiongjieDai/GPU-Benchmarks-on-LLM-Inferen...
    comment by ranger_danger · 2026-03-09 · in How to run Qwen 3.5 locally
  2. For LLM inference of batch size 1, it's hard to be saturate PCIe bandwidth specially for less powerful chips. You would get close to linear performance[1]. The obvious issue is few things on multiple GPU is harder, and many softwares don't…
  3. That’s what I hope for, but everything that isn’t bananas expensive with unified memory has very low memory bandwidth. DGX (Digits), Framework Desktop, and non-Ultra Macs are all around 128 gb/s, and will produce single digits tokens per…
    comment by m0th87 · 2025-06-09 · in Building an AI server on a budget
  4. It's really slow. Like, unusably slow. For those interested in self-hosting, this is a really good resource: https://github.com/XiongjieDai/GPU-Benchmarks-on-LLM-Inferen...
    comment by revnode · 2024-12-05 · in AmpereOne: Cores Are the New MHz
  5. > It's still "fast enough" for even 120b models in practice Is it? This is very subjective. The Mac Studio would not be "fast enough" for me on even a 70b model, not necessarily because its output is slow, but because the prompt evaluation…
    comment by angoragoats · 2024-10-29 · in New Mac Mini with M4
  6. LocalLLaMA subreddit usually has some interesting benchmarks and reports. Here is one example, testing performance of different GPUs and Macs with various flavours of Llama: https://github.com/XiongjieDai/GPU-Benchmarks-on-LLM-Inferen...
  7. https://github.com/XiongjieDai/GPU-Benchmarks-on-LLM-Inferen... should give you consumer comparison