Single Workstation for Two 4070 GPUs

Single Workstation for Two 4070 GPUs | Generated by AI

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Question: For two RTX 4070 GPUs, should I use one workstation or two separate machines? Does NVLink matter?

Answer:

For 2× RTX 4070, the answer is similar but even clearer:

Use one workstation if your main goal is AI training/inference. Do not expect NVLink — RTX 4070 does not support NVLink.

RTX 4070:

Ada Lovelace architecture
12GB GDDR6X VRAM
PCIe 4.0 x16 interface
No NVLink
No GPU memory pooling

So:

RTX 4070 #1      RTX 4070 #2
12GB VRAM        12GB VRAM

       cannot become

          24GB VRAM

They remain two independent 12GB GPUs.

Option A: One high-end workstation (recommended)

Example:

CPU: Ryzen 7950X / i9-13900K
RAM: 128GB DDR5
SSD: 2TB+ NVMe

PCIe slot 1:
RTX 4070

PCIe slot 2:
RTX 4070

Software view:

CUDA:

cuda:0 -> RTX 4070 12GB
cuda:1 -> RTX 4070 12GB

PyTorch:

torch.cuda.device_count()

# 2

Training:

                 model
                   |
        ---------------------
        |                   |
      GPU0                GPU1
      12GB                12GB

        gradient synchronization

Good for:

GPT-2 style training
LoRA fine-tuning
DDP experiments
inference serving
multiple agents

But there is one important issue: VRAM

Two 4070s do not help with a single model that needs >12GB VRAM.

Example:

Qwen 14B:

FP16:
14B × 2 bytes ≈ 28GB

One 4070:

12GB ❌

Two 4070:

12GB + 12GB

still not simple 24GB ❌

You need:

model parallelism
DeepSpeed ZeRO
tensor parallelism

Example:

layer 1-20
   GPU0

layer 21-40
   GPU1

Possible, but slower.

Option B: Two separate 4070 machines

Example:

Machine A

RTX 4070
64GB RAM


Machine B

RTX 4070
64GB RAM

Better for:

Multiple agents

Example:

Machine A:
Claude-like coding agent
+ local model inference

Machine B:
training experiments
+ embedding service

or:

GPU0:
vLLM server

GPU1:
fine tuning

Training speed comparison

Single machine:

GPU0
 |
PCIe
 |
CPU
 |
PCIe
 |
GPU1

NCCL

Usually:

fast

Two machines:

GPU0
 |
10Gb ethernet
 |
GPU1

Communication:

100x slower than local GPU memory

Distributed training suffers.

Motherboard matters

For dual RTX 4070, avoid small mATX boards.

Better:

ATX motherboard

slot spacing:

GPU0
|
3-slot gap
|
GPU1

Because RTX 4070 cards are usually:

2.5-3 slots thick

Example:

PCIe x16
RTX4070

(empty)

PCIe x16/x8
RTX4070

You need airflow.

PSU

Two RTX 4070:

Power:

RTX 4070:
~200W each

GPU total:
400W

CPU:
150W

system:
100W

---------------
~650W

Recommended:

850W Gold PSU

My recommendation for your AI experiments

Given your previous GPT-2 / nanoGPT training:

I would choose:

Ryzen 7950X
128GB RAM
2TB NVMe

2 × RTX 4070 12GB

850W PSU
ATX board

Why?

You get:

24GB total GPU compute
46 TFLOPS FP32-ish
2 CUDA devices
excellent DDP learning platform

Then later:

replace one RTX4070

with RTX3090 24GB

and you have:

RTX3090 24GB
+
RTX4070 12GB

which is actually more useful for LLM work.

For LLMs, VRAM capacity beats GPU count most of the time. A used RTX 3090 24GB is often a better AI card than two RTX 4070 12GB cards.

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