MSI X870E Multi-GPU Limits Explained | Generated by AI
Question: MSI X870E board — how many RTX 4070-class GPUs can I plug in?
Answer:
MSI X870E is a very high-end AM5 motherboard platform (Ryzen 7000/8000/9000). It is good, but it is not a multi-GPU AI workstation board like server boards. (MSI USA)
Typical MSI X870E layout:
CPU (Ryzen 9000)
|
+-- PCIe 5.0 x16 ---> GPU #1 (RTX 4070)
|
+-- PCIe 5.0 x4/x8 ---> GPU #2 (depends on model)
|
+-- PCIe 4.0 x4 ---> GPU #3 (possible, but slower)
For example, MSI MPG X870E Carbon has:
- PCIe 5.0 x16 primary slot
- PCIe 5.0 secondary slot (bandwidth sharing)
- PCIe 4.0 x4 third slot (MSI USA)
So physically:
- 1 × RTX 4070 → perfect
- 2 × RTX 4070 → possible
- 3 × RTX 4070 → possible on some models, but not ideal
- 4 × RTX 4070 → practically no (slot space + PCIe lanes + power limitations)
(MSI)
For AI workloads:
1 GPU
RTX 4070 12GB
PCIe 5.0 x16
Excellent.
2 GPUs
Example:
RTX 4070 12GB
RTX 4070 12GB
Total VRAM:
24GB
But no NVLink, so PyTorch distributed training uses PCIe communication.
3 GPUs
RTX 4070
RTX 4070
RTX 4070
=
36GB VRAM
Possible, but:
- third card only PCIe x4
- physical cooling becomes difficult
- motherboard lanes become bottleneck
Better AI build
For LLM experiments, I would not choose X870E. I would choose:
Used workstation/server board:
Threadripper Pro
EPYC
Xeon W
because you get:
PCIe lanes:
CPU
|
+-- GPU x16
+-- GPU x16
+-- GPU x16
+-- GPU x16
Example:
4 × RTX 3090 24GB
= 96GB VRAM
which is much more interesting for local LLM.
X870E is more like:
Ryzen 9950X
+
RTX 5090 / RTX 4090
+
fast NVMe
+
gaming + AI workstation
not:
4 GPU training machine
For your use case (GPT training, ROCm experiments, local LLM), I would rank:
EPYC / Threadripper Pro >>> X870E >>> normal B650/B760
X870E is a great enthusiast board, but the AM5 platform simply does not have enough PCIe lanes for a serious multi-GPU lab. (MSI)
References:
- MSI PCIe lane sharing explanation (MSI)
- MSI MPG X870E Carbon specifications (MSI USA)
- MSI PRO X870E-P specifications (MSI USA)