### 🔀 [#533](https://github.com/ikawrakow/ik_llama.cpp/pull/533) - Much faster CPU prompt processing (part 2) | **Author** | `ikawrakow` | | :--- | :--- | | **State** | ❌ **Closed** | | **Created** | 2025-06-17 | | **Updated** | 2025-06-18 | --- #### Description This PR is a follow up of #531 and applies the technique to `IQK` quants. Here is a PP-512 performance comparison for LlaMA-3.1-8B-Instruct on a Ryzen-7950X CPU between the main branch and this PR: | model | size | test | t/s (main) | t/s (PR) | Speedup | | ---------------- | ---------: | ------------: | ---------------: | ---------------: | --------: | | llama 8B IQ2_KS | 2.05 GiB | pp512 | 203.08 ± 0.39 | 372.48 ± 3.69 | 1.834 | | llama 8B IQ2_K | 2.22 GiB | pp512 | 195.04 ± 2.44 | 365.58 ± 4.25 | 1.874 | | llama 8B IQ3_K | 3.21 GiB | pp512 | 167.65 ± 0.53 | 354.90 ± 3.44 | 2.117 | | llama 8B IQ4_KS | 3.98 GiB | pp512 | 198.28 ± 0.57 | 362.81 ± 1.74 | 1.830 | | llama 8B IQ4_K | 4.21 GiB | pp512 | 177.08 ± 1.71 | 360.58 ± 1.96 | 2.036 | | llama 8B IQ5_KS | 4.91 GiB | pp512 | 182.40 ± 1.62 | 358.66 ± 3.39 | 1.966 | | llama 8B IQ5_K | 5.14 GiB | pp512 | 158.74 ± 0.87 | 354.68 ± 0.75 | 2.234 | | llama 8B IQ6_K | 6.19 GiB | pp512 | 147.07 ± 0.80 | 353.20 ± 3.48 | 2.402 | To put things into perspective, the fastest mainline `llama.cpp` quant on this CPU is `Q4_0`, and I get **170 t/s** with today's build (`build: 860a9e4ee (5688)`). For a bit of history, when [PR 6414](https://github.com/ggml-org/llama.cpp/pull/6414) was added to `llama.cpp`, it received 92 :+1:, 32 :tada:, 34 :heart:, and 30 :rocket:. It only supported `Q4_0` and `Q8_0`, and speedup compared to the master branch at the time was in the range of 40-50%, for a PP-512 of **135 t/s** on the Ryzen-7950X CPU used for the above table. There was a [blog post](https://justine.lol/matmul/) received with [great fanfare on HN](https://news.ycombinator.com/item?id=39890262). --- #### 💬 Conversation 👤 **ubergarm** commented the **2025-06-17** at **16:45:36**:
Thanks, this is huge. I feel like this will make ~70B dense models much better for hybrid inferencing on home rigs. Hope to try some quants soon! --- 👤 **Nexesenex** commented the **2025-06-17** at **18:31:50**:
Very impressive, @ikawrakow! All your recent commits motivates me to put more of IK_Llama on my Kobold.Cpp fork. I already have overall twice its CPU PP perfs thanks to your amazing work, and I merged most of your quants, including the last Trellis! Way to make an enthusiast happy!