Is FirePro S7150x2 worth ¥380?

Is FirePro S7150x2 worth ¥380? | Generated by AI

Home 2026.07

Question: What is AMD FirePro S7150x2 16GB dual GPU professional card? Is it worth ¥380?

Answer:

The AMD FirePro S7150x2 is a server/workstation professional GPU, not a normal gaming card. It is basically two FirePro S7150 GPUs on one PCB, designed for GPU virtualization (MXGPU), VDI, CAD, rendering farms, and professional workloads.

Key specs:

	FirePro S7150x2
Architecture	AMD GCN (Fiji era)
Release	~2016
GPU cores	2 × 2048 stream processors
VRAM	16GB total (2 × 8GB GDDR5 ECC)
Memory bus	2 × 256-bit
Compute	~5.8 TFLOPS FP32
Power	~265W
Interface	PCIe 3.0 x16
Cooling	Passive server heatsink (usually needs strong airflow)

The important detail:

“16GB” does NOT mean one 16GB GPU.

Internally:

PCIe x16
    |
    +---- GPU A
    |       8GB VRAM
    |
    +---- GPU B
            8GB VRAM

Software sees two GPUs.

Compared with GPUs you have been looking at

FirePro S7150x2 vs Tesla M60

Very similar concept:

	S7150x2	Tesla M60
Year	2016	2015
GPUs	2	2
VRAM	16GB	16GB
Memory	GDDR5 ECC	GDDR5 ECC
FP32	~5.8 TF	~8.1 TF
CUDA	❌	✅
ROCm	❌ basically	❌
Virtualization	Excellent	Excellent

Tesla M60 has CUDA ecosystem advantage, but both are old.

For AI / LLM usage

Not recommended.

Reasons:

1. No CUDA

Most AI stack:

PyTorch
   |
CUDA
   |
NVIDIA GPU

FirePro:

PyTorch
   |
ROCm? (very limited)
   |
GCN Fiji

Modern ROCm does not support Fiji well.

2. VRAM is fragmented

You cannot run:

16GB model

like RTX 3090.

Because:

GPU0: 8GB
GPU1: 8GB

You need model parallelism.

Example:

Layer 0-20 -> GPU0
Layer 21-40 -> GPU1

but consumer frameworks are not optimized for this.

3. Very low performance today

Compare:

GPU	FP32
FirePro S7150x2	~5.8 TF
RTX 3060	~12.7 TF
RTX 4070	~29 TF
RTX 3090	~36 TF

Your RTX 4070 is roughly 5x faster.

Where it is actually interesting

For ¥380, it is a fun card for:

✅ GPU virtualization lab

This is where it shines.

Example:

One physical card

GPU partition:

VM1 -> 4GB
VM2 -> 4GB
VM3 -> 4GB
VM4 -> 4GB

AMD calls this:

MxGPU

Similar idea to NVIDIA vGPU.

✅ OpenCL experiments

Can run:

Blender OpenCL (old)
scientific workloads
compute experiments

✅ Homelab / collection

For a GPU researcher:

RTX 4070
RTX 3090
Tesla M60
FirePro S7150x2
MI50

It is interesting historically.

The ¥380 question

My rating:

Usage	Value
AI training	❌ 2/10
LLM inference	❌ 2/10
Gaming	❌ 1/10
CAD workstation	⚠️ 5/10
Virtualization lab	✅ 8/10
GPU collection	✅ 8/10

At ¥380 (~$50):

As an AI GPU: skip.
As a virtualization toy: quite interesting.
As a collector card: good price.

For your AI experiments (nanoGPT, LLM fine-tuning, agents), a used RTX 3090 24GB is still in another universe:

FirePro S7150x2:
  2 × 8GB old GCN

RTX 3090:
  24GB unified VRAM
  CUDA
  Tensor cores
  FlashAttention support
  modern PyTorch

The S7150x2 is a “server history artifact”; the RTX 3090 is still a practical AI accelerator.

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