本地运行大模型效果及配置展示

电脑上用ollama安装了qwen2.5:32b，deepseek-r1:32b，deepseek-r1:14b，llama3.1:8b四个模型，都是Q4_K_M量化版。

运行过程中主要是cpu和内存负载比较大，qwen2.5:32b大概需要22g，deepseek-r1：32b类似。显卡的运行状态在使用nouveau驱动的情况下使用cpu-x没有读取到。前段时间换成NVIDIA驱动后又试了下qwen2.5：32b， 使用nvidia-smi读取到了运行状态，之前会占用大量内存的情况现在也没有再出现，但输出速度几乎不变，不太确定正不正常。考虑到切换NVIDIA驱动后我的两块屏幕无法显示，加上其他一些arch用户在更新系统时遇到的和NVIDIA驱动相关的问题，我又切换回了nouveau。

运行效果方面的话，两个32b的模型的效果是最好的，一些复杂问题的准确性也比两个要高，但是速度也是最慢的，对我来说属于勉强能用，如果速度能达到10 tokens/s的话，用起来就比较流畅了。

环境及配置

• 系统:：Arch Linux
• CPU: AMD Ryzen 7 5800H with Radeon Graphics (16) @ 4.463GHz
• GPU: AMD ATI Radeon Vega Series / Radeon Vega Mobile Series
• GPU: NVIDIA GeForce RTX 3050 Ti Mobile / Max-Q
• GPU驱动：nouveau
• 内存: 64G (32x2) DDR4 3200MHZ

qwen2.5:32b

deepseek-r1:32b

llama3.1:8b

一些术语解释

Total Duration:
The total time it took the model to complete the task. This includes all processing time.

Load Duration:
The model’s time to load or initialize before starting the task.

Prompt Eval Count:
The number of tokens (individual words or sub-word units) in the input prompt given to the model.

Prompt Eval Duration:
he model’s time to process and understand the input prompt.

Prompt Eval Rate:
The speed at which the model processed the input prompt, measured in tokens per second.

Eval Count:
The total number of tokens the model processes during the entire task, including both the prompt and the generated output.

Eval Duration:
The model’s time to process all the tokens during the task.

Eval Rate:
The overall processing speed of the model during the task, measured in tokens per second.