Tesla P100 vs M60 for AI

Tesla P100 vs M60 for AI | Generated by AI

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Question: Compare NVIDIA Tesla M60 vs Tesla P100 for AI workloads.

Answer:

Short version: Tesla P100 is much faster for AI/compute. Tesla M60 is basically a virtualization/display GPU with two old Maxwell chips. For LLM training/inference, P100 wins by a large margin.

NVIDIA Tesla M60 vs NVIDIA Tesla P100

	Tesla M60	Tesla P100
Architecture	Maxwell (2015)	Pascal (2016)
GPU cores	2 × GM204	1 × GP100
CUDA cores	2048 × 2 = 4096	3584
VRAM	16GB GDDR5 (2×8GB)	16GB HBM2
Memory bandwidth	~320 GB/s	732 GB/s
FP32 compute	~9 TFLOPS	9.3 TFLOPS
FP16 compute	weak	18.7 TFLOPS
Memory ECC	Yes	Yes
Power	~300W	250W
Cooling	Passive	Passive
AI usefulness	Low	Much better

Sources: NVIDIA lists Tesla P100 PCIe at 3584 CUDA cores, 9.3 TFLOPS FP32, 18.7 TFLOPS FP16, and 16GB HBM2 with 732GB/s bandwidth. ([NVIDIA Images][1])

1. The biggest difference: memory

AI workloads are often memory-bandwidth limited.

Transformer inference:

tokens/sec ≈ memory bandwidth / model size

A 7B model FP16:

7B params × 2 bytes ≈ 14GB

You constantly stream weights.

M60:

320 GB/s

P100:

732 GB/s

P100 has more than 2× bandwidth.

For LLM inference, this matters more than raw FLOPS.

2. Tensor / FP16 performance

Neither card has Tensor Cores.

But:

M60:

Maxwell
FP16 is basically not useful

P100:

Pascal
FP16 optimized
18.7 TFLOPS

P100 was literally designed for deep learning and HPC. ([NVIDIA][2])

3. For LLMs

Example:

Qwen / Llama 7B quantized

Both have 16GB.

Could run:

7B Q4
~4-5GB

But speed:

Tesla M60:

slow decode
maybe 5-10 tok/s

Tesla P100:

maybe 15-30 tok/s

(depends heavily on software/kernel)

Community experiments also show P100 remains surprisingly useful for cheap LLM inference because of HBM2 bandwidth. ([Reddit][3])

4. Training / fine-tuning

P100 wins.

Example LoRA:

Llama 7B
4-bit QLoRA

M60:

possible
painful
old CUDA
slower

P100:

reasonable
better bandwidth
ECC
CUDA support

But important:

Neither supports:

BF16 Tensor Core training

You need:

V100
A100
RTX 30/40 series
H100

for modern efficient training.

5. Multi-GPU

M60 is interesting because:

M60 = two GPUs on one board

Linux sees:

GPU0 8GB
GPU1 8GB

not:

16GB unified

So you cannot run a 16GB model on one M60 GPU.

P100:

one GPU
16GB HBM2

Much simpler.

6. Price/value (used market)

Typical used prices:

Tesla M60

~$50-100

Good for:

virtualization
CUDA learning
cheap GPU experiments

Tesla P100 16GB

~$100-200

Better for:

PyTorch
CUDA experiments
small LLM inference
HPC

My ranking for an AI home lab

RTX 3090 24GB
        |
        | huge gap
        |
RTX 4070 12GB
        |
Tesla P100 16GB
        |
Tesla M60 16GB

For your use case (training GPT-style models, agents, local LLMs):

I would choose:

RTX 3090 24GB → best cheap AI card
Tesla P100 16GB HBM2 → fun cheap AI accelerator
Tesla M60 → only if extremely cheap (<$80)

The P100 is a “mini old A100 ancestor”; the M60 is more like a “server GTX 980 with ECC”. ([developer.nvidia.com][4])

[1]: https://images.nvidia.com/content/tesla/pdf/nvidia-tesla-p100-PCIe-datasheet.pdf?utm_source=chatgpt.com “NVIDIA ® TESLA ® P100 GPU ACCELERATOR World’s mo” [2]: https://www.nvidia.com/en-au/data-center/tesla-p100/?utm_source=chatgpt.com “Tesla P100 Data Center Accelerator | NVIDIA” [3]: https://www.reddit.com/r/MachineLearning/comments/193t4w8?utm_source=chatgpt.com “[D] Anyone Tried a Tesla P100 for Fine-Tuning LLMs?” [4]: https://developer.nvidia.com/blog/inside-pascal/?utm_source=chatgpt.com “Inside Pascal: NVIDIA’s Newest Computing Platform | NVIDIA Technical Blog”

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