CDNA 2
AMD Instinct MI250X
wins
4
The AMD Instinct MI250X is AMD's CDNA 2 data center GPU.
Ada Lovelace
NVIDIA RTX 4060 8GB
wins
1
The RTX 4060 is the most power-efficient Ada Lovelace GPU at 115W TDP. 8GB GDDR6 limits it to small quantized models (up to ~7B at INT4).
Performance comparison
Visual bars — winner highlighted
AMD Instinct MI250X
Metric
NVIDIA RTX 4060 8GB
FP16 TFLOPS
Bandwidth
VRAM
TDP (lower=better)
TFLOPS/watt
GB/s per watt
Full specification table
| Spec | AMD Instinct MI250X | NVIDIA RTX 4060 8GB |
|---|---|---|
| Architecture | CDNA 2 | Ada Lovelace |
| VRAM | 128 GB HBM2e | 8 GB GDDR6 |
| VRAM type | HBM2e | GDDR6 |
| Memory bandwidth | 3,277 GB/s | 272 GB/s |
| FP16 TFLOPS | 383 TFLOPS | 30.2 TFLOPS |
| TDP | 560 W | 115 W |
| TFLOPS/watt | 0.68 T/W | 0.26 T/W |
| GB/s per watt | 5.85 GB/s/W | 2.37 GB/s/W |
| NVLink | No | No |
| Release year | 2021 | 2023 |
Live cloud pricing
On-demand pricing across providers — updated in real time
Power efficiency analysis
TFLOPS/watt and GB/s/watt — critical for data center TCO
AMD Instinct MI250X
NVIDIA RTX 4060 8GB
TFLOPS/watt measures compute efficiency — how much AI throughput you get per watt of power consumed. For data centers with PUE of 1.2–1.5, a 10% improvement in TFLOPS/watt translates directly to lower electricity costs and cooling requirements. GB/s/watt measures memory bandwidth efficiency, which is the binding constraint for memory-bound LLM inference workloads.
When to choose each GPU
Choose AMD Instinct MI250X for:
- HPC workloads
- Large model inference
- AMD ROCm deployments
Weaknesses / Limitations
- Older CDNA 2 architecture — lower FP16 TFLOPS than MI300X or H100
- ROCm software ecosystem less mature than CUDA
- 560W TDP — high power draw for its performance tier
Choose NVIDIA RTX 4060 8GB for:
- Ultra-budget inference
- Small model experimentation
- Entry-level Ada AI
Weaknesses / Limitations
- 8 GB VRAM — only fits quantized models up to ~7B parameters
- Very low bandwidth (272 GB/s) — severe bottleneck for inference throughput
- Not viable for fine-tuning beyond tiny models
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