Cheap AMD GPU for Newer ROCm

Cheap AMD GPU for Newer ROCm | Generated by AI

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Question: What cheap AMD GPU on Xianyu/used market is similar to Radeon Pro VII but supports newer ROCm?

Answer:

If your goal is AI / PyTorch / ROCm experiments, not gaming, the sweet spot is different from normal GPU recommendations.

Your current Radeon Pro VII is special because:

Vega 20
16GB HBM2
ECC
good FP64
workstation card

but ROCm support is basically old generation (ROCm 5.7 era). For newer ROCm, you want CDNA or RDNA2+. AMD’s current ROCm compatibility lists newer accelerator families and Radeon support varies by stack/version. (ROCm Documentation)

My ranking for cheap second-hand AI use:

GPU	Architecture	VRAM	ROCm situation	Used price idea	Recommendation
Radeon RX 6800 16GB	RDNA2	16GB GDDR6	Good ROCm target	¥1500-2200	⭐ Best cheap Radeon
Radeon RX 6700 XT 12GB	RDNA2	12GB	Works but less VRAM	¥1000-1500	OK
Radeon RX 6900 XT 16GB	RDNA2	16GB	Good	¥2000-3000	Faster
Radeon Pro W6800	RDNA2	32GB GDDR6 ECC	Professional	expensive	Excellent if cheap
MI100	CDNA1	32GB HBM2	Real AI accelerator	¥3000-5000	Very interesting
MI210	CDNA2	64GB HBM2e	Modern ROCm	¥6000+	Best value if found

1. Radeon RX 6800 16GB — probably your best Xianyu target

AMD Radeon RX 6800

Why:

16GB VRAM
RDNA2
PCIe normal desktop card
cheap
no weird workstation firmware

For LLM:

Qwen2.5-7B
Llama 8B
Mistral 7B

OK

For your nanoGPT experiments:

GPT-2 124M
GPT-2 355M
small SFT

OK

The downside:

no HBM
FP64 is much weaker than Vega/MI cards

2. MI100 — closest spiritual successor to Radeon Pro VII

AMD Instinct MI100

This is basically:

Radeon Pro VII
      |
      v
CDNA
      |
      v
MI100

Specs:

32GB HBM2
4096-bit memory bus
ECC
AI accelerator

Much better ROCm experience.

Compared with Radeon Pro VII:

	Pro VII	MI100
Architecture	Vega20	CDNA1
VRAM	16GB HBM2	32GB HBM2
ECC	yes	yes
ROCm future	weak	much better
AI	OK	much better

For LLM training, MI100 is much more interesting.

3. Radeon Pro W6800 32GB

AMD Radeon Pro W6800

This is the “modern Radeon Pro VII”.

Specs:

RDNA2
32GB GDDR6 ECC
PCIe 4.0

Problem:

Used price is often too high because workstation users still buy them.

If you find:

W6800 < ¥3000

then it is interesting.

4. Avoid these for ROCm AI

Radeon VII

Your card.

Amazing hardware:

16GB HBM2
1TB/s bandwidth
FP64 monster

but software future is limited.

Vega 56 / Vega 64

Cheap:

¥400-800

but ROCm pain.

You will spend more time patching than training.

RX 5000 series

Example:

RX 5700 XT

Cheap, but RDNA1 support is not ideal.

My personal shortlist for your situation

Since you already have:

RTX 4070 12GB
MI50 16GB
Radeon Pro VII 16GB

I would look for:

Option A — cheapest experiment

RX 6800 16GB
≈ ¥1500

Use:

ROCm
PyTorch
llama.cpp
vLLM experiments

Option B — real AI accelerator

MI100 32GB HBM2
≈ ¥3000-5000

Much closer to your MI300X learning path.

Option C — dream cheap find

MI210 64GB HBM2e

If someone sells enterprise liquidation:

buy immediately.

For your “one-person AI lab” direction, I would not buy another gaming Radeon. I would move toward:

RTX 4070
      |
      |
MI50
      |
      |
MI100 / MI210
      |
      |
MI300X cloud

because the software stack and hardware architecture become much closer. (arXiv)

References:

AMD ROCm Compatibility Matrix (ROCm Documentation)
AMD ROCm Compatibility Matrix ROCm 7.x (ROCm Documentation)
AMD Radeon Pro W6800 specifications

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