# GPU Computing Hardware Planning

## Llama3 Inference Speed on GPUs (tokens/second)

<figure><img src="/files/v5zPhgnu0apRyG9Kd0nq" alt=""><figcaption><p>Performance Comparison of Mainstream GPUs on Llama3 8B / 70B</p></figcaption></figure>

<table><thead><tr><th width="166">GPU</th><th width="145">Memory(VRAM)</th><th width="125">8B Q4_K_M</th><th width="89">8B F16</th><th width="129">70B Q4_K_M</th><th width="159">70B F16</th></tr></thead><tbody><tr><td>RTX 4090</td><td>24GB</td><td>127.74</td><td>54.34</td><td>Out of Memory</td><td>Out of Memory</td></tr><tr><td>RTX A6000</td><td>48GB</td><td>102.22</td><td>40.25</td><td>14.58</td><td>Out of Memory</td></tr><tr><td>L40S</td><td>48GB</td><td>113.60</td><td>43.42</td><td>15.31</td><td>Out of Memory</td></tr><tr><td>RTX 6000 Ada</td><td>48GB</td><td>130.99</td><td>51.97</td><td>18.36</td><td>Out of Memory</td></tr><tr><td>A100</td><td>80GB</td><td>138.31</td><td>54.56</td><td>22.11</td><td>Out of Memory</td></tr><tr><td>H100</td><td>80GB</td><td>144.49</td><td>67.79</td><td>25.01</td><td>Out of Memory</td></tr><tr><td>M2 Ultra</td><td>192GB</td><td>76.28</td><td>36.25</td><td>12.13</td><td>4.71</td></tr></tbody></table>

***

## VRAM Requirements for Llama3 Models

| Model      | Q4\_K\_M (Quantized) | F16 (Original) |
| ---------- | -------------------- | -------------- |
| Llama3 8B  | 4.58 GB              | 14.96 GB       |
| Llama3 70B | 39.59 GB             | 131.42 GB      |

Source

{% embed url="<https://github.com/XiongjieDai/GPU-Benchmarks-on-LLM-Inference>" %}

***

## Hardware Configuration Recommendations

MaiAgent recommends two combinations suitable for different groups:

1. **Two H100(80GB)**: Higher budget, prioritizing quality and performance
2. **L40S(48GB) and RTX 6000 Ada(48GB)**: Standard budget, focusing on cost-effectiveness

For more detailed information, please contact MaiAgent's professional consultants at <mark style="color:blue;"><sales@maiagent.ai></mark>


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