Category: VMware

For Private AI in HomeLAB, I was searching for budget-friendly GPUs with a minimum of 24GB RAM. Recently, I came across the refurbished NVIDIA Tesla P40 on eBay, which boasts some intriguing specifications:

• GPU Chip: GP102
• Cores: 3840
• TMUs: 240
• ROPs: 96
• Memory Size: 24 GB
• Memory Type: GDDR5
• Bus Width: 384 bit

Since the NVIDIA Tesla P40 comes in a full-profile form factor, we needed to acquire a PCIe riser card.

A PCIe riser card, commonly known as a “riser card,” is a hardware component essential in computer systems for facilitating the connection of expansion cards to the motherboard. Its primary role comes into play when space limitations or specific orientation requirements prevent the direct installation of expansion cards into the PCIe slots on the motherboard.

Furthermore, I needed to ensure adequate cooling, but this posed no issue. I utilized a 3D model created by MiHu_Works for a Tesla P100 blower fan adapter, which you can find at this link: Tesla P100 Blower Fan Adapter.

As for the fan, the Titan TFD-B7530M12C served the purpose effectively. You can find it on Amazon: Titan TFD-B7530M12C.

Currently, I am using a single VM with PCIe pass-through. However, it was necessary to implement specific Advanced VM parameters:

• pciPassthru.use64bitMMIO = true
• pciPassthru.64bitMMIOSizeGB = 64

Now, you might wonder about the performance. It’s outstanding, delivering speeds up to 16x-26x times faster than the CPU. To provide you with an idea of the performance, I conducted a llama-bench test:

./llama-bench -t 8
| model                          |       size |     params | 
| ------------------------------ | ---------: | ---------: | 
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | 
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | 
| backend    |    threads | test       |              t/s |
| ---------- | ---------: | ---------- | ---------------: |
| CPU        |          8 | pp 512     |      9.50 ± 0.07 |
| CPU        |          8 | tg 128     |      8.74 ± 0.12 |

./llama-bench -ngl 3800
ggml_init_cublas: GGML_CUDA_FORCE_MMQ:   no
ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes
ggml_init_cublas: found 1 CUDA devices:
  Device 0: Tesla P40, compute capability 6.1
| model                          |       size |     params |
| ------------------------------ | ---------: | ---------: |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B |
| backend    | ngl | test       |              t/s | 
| ---------- | --: | ---------- | ---------------: | 
| CUDA       | 3800 | pp 512     |    155.37 ± 1.26 |
| CUDA       | 3800 | tg 128     |      9.31 ± 0.19 |

 ./llama-bench -t 8 -m ./models/13B/ggml-model-q4_0.gguf
| model                          |       size |     params |
| ------------------------------ | ---------: | ---------: |
| llama 13B mostly Q4_0          |   6.86 GiB |    13.02 B |
| llama 13B mostly Q4_0          |   6.86 GiB |    13.02 B |
| backend    |    threads | test       |              t/s |
| ---------- | ---------: | ---------- | ---------------: |
| CPU        |          8 | pp 512     |      5.18 ± 0.00 |
| CPU        |          8 | tg 128     |      4.63 ± 0.14 |

./llama-bench -ngl 3800 -m ./models/13B/ggml-model-q4_0.gguf
ggml_init_cublas: GGML_CUDA_FORCE_MMQ:   no
ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes
ggml_init_cublas: found 1 CUDA devices:
  Device 0: Tesla P40, compute capability 6.1
| model                          |       size |     params |
| ------------------------------ | ---------: | ---------: |
| llama 13B mostly Q4_0          |   6.86 GiB |    13.02 B |
| llama 13B mostly Q4_0          |   6.86 GiB |    13.02 B |
| backend    | ngl | test       |              t/s | 
| ---------- | --: | ---------- | ---------------: | 
| CUDA       | 3800 | pp 512     |    134.74 ± 1.29 |
| CUDA       | 3800 | tg 128     |      8.42 ± 0.10 |

Feel free to explore this setup for your Private AI in HomeLAB.