[kpw94]

> About the generation speed: ~100-150 t/s on the RTX 5090 and ~40 t/s on the Mac

Curious if you can share the prefill speed too?

I run locally on a crappy desktop (some AMD iGPU with Vulkan llama.cpp, 32 GB DDR4 RAM) for experimentation. I get 15 tok/s on generation for the qwen & gemma4 MoE models. I get around 150 tok/s prefill speed.

Reason I'm asking about the prefill is looking at my stats at work, I use between 20M to peaks of 300M input tokens daily. Some of those token are cached but in general, I seem to have roughly 500x more input tokens than output. So interested in prefill tok/s stats.

Huge Thank you for llama.cpp btw!!

[ggerganov]

Here are the prefill speeds:

    Device 0: NVIDIA GeForce RTX 5090, compute capability 12.0, VMM: yes, VRAM: 32109 MiB
  | model                          |       size |     params | backend  |  fa |            test |                  t/s |
  | ------------------------------ | ---------: | ---------: | -------- | --: | --------------: | -------------------: |
  | qwen35 27B Q4_K - Medium       |  15.92 GiB |    27.32 B | CUDA     |   1 |   pp2048 @ d512 |      3714.02 ± 10.85 |
  | qwen35 27B Q4_K - Medium       |  15.92 GiB |    27.32 B | CUDA     |   1 |  pp2048 @ d1024 |      3684.86 ± 15.21 |
  | qwen35 27B Q4_K - Medium       |  15.92 GiB |    27.32 B | CUDA     |   1 |  pp2048 @ d2048 |       3650.80 ± 8.53 |
  | qwen35 27B Q4_K - Medium       |  15.92 GiB |    27.32 B | CUDA     |   1 |  pp2048 @ d8192 |       3473.88 ± 0.97 |
  | qwen35 27B Q4_K - Medium       |  15.92 GiB |    27.32 B | CUDA     |   1 | pp2048 @ d32768 |       2754.69 ± 4.07 |

  ggml_metal_device_init: GPU name:   MTL0 (Apple M2 Ultra)
  | model                          |       size |     params | backend  | fa |            test |                  t/s |
  | ------------------------------ | ---------: | ---------: | -------- | -: | --------------: | -------------------: |
  | qwen35 27B Q8_0                |  26.62 GiB |    26.90 B | MTL      |  1 |   pp2048 @ d512 |        379.75 ± 0.21 |
  | qwen35 27B Q8_0                |  26.62 GiB |    26.90 B | MTL      |  1 |  pp2048 @ d1024 |        377.15 ± 0.35 |
  | qwen35 27B Q8_0                |  26.62 GiB |    26.90 B | MTL      |  1 |  pp2048 @ d2048 |        371.46 ± 0.91 |
  | qwen35 27B Q8_0                |  26.62 GiB |    26.90 B | MTL      |  1 |  pp2048 @ d8192 |        344.84 ± 0.41 |
  | qwen35 27B Q8_0                |  26.62 GiB |    26.90 B | MTL      |  1 | pp2048 @ d32768 |        222.42 ± 5.29 |

Btw, based on your numbers, I think our use cases are quite different. I use the agent for very targeted sessions - basically things that are clear to me how to do, just want to automate them. My workflow is usually: new session -> read this, this and this -> do that. I.e. I don't let it wander at all in the codebase, so I rarely exceed the context window.

Also, I get a lot of mileage from the ngram-based speculative decoding functionality [0] as it allows me to iterate on the implementation much faster.

[0] https://github.com/ggml-org/llama.cpp/pull/19164

[kpw94]

Thanks! Super helpful.

I do use it the same way as you're describing on personal projects at home, in a very crude manner (pasting code snippets in llama server web UI prompt. Next will attempt OpenCode)

At work I use it in similar manner with more mature tools, but the vast majority of token spend comes from a totally different workflow: "pretend the AI is a fleet of junior/intern engineer you're delegating work to", where the agent will on its own do the implementation, commit the changes etc.

It does indeed spend a lot of tokens wandering the codebase, talking to MCPs, loading skills etc.