Tesla P40 48GB Dual Setup

Tesla P40 48GB Dual Setup | Generated by AI

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Question: Tesla P40 24GB ×2 = 48GB GPU setup intro?

Answer:

The NVIDIA Tesla P40 24GB is an old but interesting AI card. Two cards give you 48GB VRAM, which is why people buy them for cheap local LLM inference. (NVIDIA)

Tesla P40 specs

Item	Tesla P40
Architecture	Pascal (2016)
GPU chip	GP102 (same family as GTX 1080 Ti / Titan Xp)
CUDA cores	3,840
VRAM	24GB GDDR5 ECC
Memory bandwidth	~346 GB/s
FP32	~12 TFLOPS
INT8	~47 TOPS
Power	250W
PCIe	PCIe 3.0 x16
Display output	None
Cooling	Passive server card

(NVIDIA)

Why 2×P40 = 48GB is attractive

For LLM inference, VRAM capacity matters more than raw speed.

Example:

7B model

FP16:

7B × 2 bytes ≈ 14GB

One P40 can run it.

13B model

FP16:

13B × 2 ≈ 26GB

One P40 cannot fit.

Two P40:

24GB + 24GB = 48GB

can run:

13B FP16
30B-ish quantized GGUF
some 70B 4-bit experiments (slow)

Compared with RTX 3090

	2×Tesla P40	RTX 3090
VRAM	48GB	24GB
CUDA	7680 cores	10496 cores
Architecture	Pascal	Ampere
Tensor cores	No	Yes
FP16 speed	poor	excellent
LLM inference	OK	much faster
Training	weak	much better

A 3090 is much faster, but P40 wins on VRAM/$.

For your use case (nanoGPT / local LLM)

Your recent GPT-2 124M training on RTX 4070:

P40 is actually worse for training.

Reasons:

No Tensor Cores

Modern PyTorch training relies heavily on:

FP16/BF16 Tensor Core

P40 only has Pascal CUDA cores.

Slow memory

GDDR5:

346 GB/s

RTX 4070:

~504 GB/s

No BF16 acceleration

For training transformers:

RTX 4070 >>> P40

But for inference:

vLLM / llama.cpp / GGUF

P40 becomes interesting.

Example:

2 × P40

VRAM:
48GB

Cost:
cheap

Use:
local Llama/Qwen inference

Many homelab users use exactly this idea. (Reddit)

The annoying parts

1. No display

You need:

RTX 4070
    |
    +-- display

Tesla P40
    |
    +-- CUDA only

2. Cooling

P40 is designed for a server airflow tunnel.

Desktop:

P40
 |
 | 250W heat
 |
fan needed

Without airflow it overheats. (Reddit)

3. Power

Two cards:

250W × 2 = 500W

Real system:

CPU 100W
+
GPU 500W
+
others

≈700W PSU recommended

4. Software support

Pascal is old.

CUDA support is becoming limited compared with RTX 20/30/40 series. (Reddit)

My evaluation for you

Given you already have:

RTX 4070
MI50 16GB experiment
interest in local LLM / agents

I would rank:

RTX 4070
★★★★★
training / development

Tesla P40 ×2
★★★☆☆
cheap VRAM server

MI50
★★★☆☆
interesting HBM2 experiment

RTX 3090
★★★★★
best used AI card

A 2×P40 machine is like a “poor man’s 48GB AI server”.

Good for:

✅ Qwen/Llama inference ✅ llama.cpp GGUF ✅ embedding servers ✅ RAG experiments ✅ agent backend

Not good for:

❌ serious training ❌ CUDA-heavy research ❌ modern PyTorch optimization

For an OPL (one-person lab), it is actually a very interesting “VRAM box”: keep RTX 4070 for development/training, keep 2×P40 as always-on inference server.

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