Specifications Compared
| Spec | A30 | MI325X |
|---|---|---|
| TDP | 165W | 750W |
| VRAM | 24 GB | 256 GB |
| CUDA Cores | 3,584 | |
| Memory Type | HBM2 | HBM3e |
| Architecture | Ampere | CDNA 3 |
| Form Factors | PCIe | OAM |
| Interconnect | NVLink | Infinity Fabric |
| Tensor Cores | 224 | |
| FP16 Performance | 10.3 TFLOPS | 1,307 TFLOPS |
| FP32 Performance | 10.3 TFLOPS | 1307 TFLOPS |
| FP64 Performance | 5.2 TFLOPS | 40.9 TFLOPS |
| INT8 Performance | 165 TOPS | 2,614 TOPS |
| Memory Bandwidth | 933 GB/s | 6,000 GB/s |
Performance Analysis
Compute performance reveals stark differences: the MI325X achieves 1307 TFLOPS in FP16 and FP32, compared to the A30's 10.3 TFLOPS, yielding approximately 127 times higher throughput for half-precision training and inference tasks. This delta translates to faster convergence in deep learning models, where FP16 dominates neural network operations; the MI325X can process workloads in minutes that take hours on the A30. The FP8 capability at 2614 TFLOPS further accelerates inference for quantized large language models.
Memory specifications dominate real-world viability: 256 GB HBM3e on the MI325X versus 24 GB HBM2 on the A30 enables handling models exceeding 100 billion parameters without multi-GPU sharding. Bandwidth at 6000 GB/s on the MI325X, over six times the A30's 933 GB/s, supports larger batch sizes in training, reducing iterations and wall-clock time by minimizing data bottlenecks. For inference, higher bandwidth sustains high query throughput under variable loads.
Power efficiency shifts with scale: the A30's 165W TDP offers 62.4 TFLOPS per kilowatt in FP16, while the MI325X's 750W yields 1742.7 TFLOPS per kilowatt, proving superior density for dense clusters despite higher absolute draw.
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When to Choose the A30
The A30 excels in power-limited or legacy environments requiring 165W TDP and PCIe compatibility. It suits small-scale inference for models fitting within 24 GB VRAM, such as computer vision tasks at 933 GB/s bandwidth, where integration ease trumps raw speed.
Budget-conscious deployments favor the A30 for fine-tuning mid-sized models up to 10 billion parameters, leveraging NVLink for modest multi-GPU setups without the MI325X's 750W demands.
When to Choose the MI325X
The MI325X dominates large-scale AI training with 256 GB HBM3e VRAM and 1307 TFLOPS FP16 performance. It handles trillion-parameter LLMs and massive datasets at 6000 GB/s bandwidth, ideal for research labs pushing model frontiers.
Inference-heavy production workloads benefit from 2614 TFLOPS FP8 and Infinity Fabric scaling, enabling high-throughput serving that the A30's 10.3 TFLOPS cannot match.
Use Cases
MI325X's 256 GB HBM3e VRAM and 1307 TFLOPS FP16 handle massive datasets and parameters infeasible on A30's 24 GB. Bandwidth at 6000 GB/s supports large batches for faster convergence.
2614 TFLOPS FP8 on MI325X accelerates quantized serving at scale. 6000 GB/s bandwidth sustains high throughput versus A30's 933 GB/s limit.
MI325X's 1307 TFLOPS FP16 outperforms A30's 10.3 TFLOPS for efficient adaptation of large models. Extra 256 GB VRAM avoids memory constraints.
A30's 24 GB suffices for standard resolutions at 10.3 TFLOPS FP16. MI325X's superior specs enable higher resolutions or batches but overkill for basics.
MI325X's 1307 TFLOPS FP32 and 6000 GB/s bandwidth excel in simulations with large arrays. A30's 10.3 TFLOPS limits complex workloads.
Frequently Asked Questions
What is the VRAM difference between A30 and MI325X?▾
The A30 has 24 GB HBM2 VRAM, while the MI325X offers 256 GB HBM3e. This 10-fold increase allows MI325X to manage much larger models without partitioning.
How do FP16 performances compare?▾
A30 delivers 10.3 TFLOPS FP16, versus MI325X's 1307 TFLOPS. MI325X provides about 127 times the half-precision compute for AI training.
What are the power requirements?▾
A30 operates at 165W TDP in PCIe form, suiting low-power setups. MI325X requires 750W in OAM, demanding robust cooling for datacenters.
Which has higher memory bandwidth?▾
MI325X achieves 6000 GB/s with HBM3e, over six times the A30's 933 GB/s HBM2. This boosts batch sizes in memory-bound tasks.
Do they support the same precisions?▾
Both offer FP16 and FP32 at equal rates internally: 10.3 TFLOPS on A30, 1307 TFLOPS on MI325X. MI325X adds 2614 TFLOPS FP8 for inference.
What interconnects do they use?▾
A30 employs NVLink for NVIDIA scaling. MI325X uses Infinity Fabric for AMD clusters, both enabling multi-GPU communication.
Which is cheaper to rent, the A30 or the MI325X?▾
Cloud rental prices for both the A30 and MI325X vary by provider, configuration, and availability. This page shows live pricing from 25+ providers updated every 60 seconds. Scroll to the Live Cloud Pricing section to compare current rates.
How much VRAM does the A30 have compared to the MI325X?▾
The A30 has 24 GB of HBM2 memory. The MI325X has 256 GB of HBM3e memory.
Can I find A30 and MI325X GPUs available to rent right now?▾
Yes. This page shows real-time availability across 25+ cloud GPU providers. The Live Cloud Pricing section displays only in-stock offers with current pricing.
What is the main difference between the A30 and the MI325X?▾
The A30 uses the Ampere architecture (2021) while the MI325X uses CDNA 3 (2024). The MI325X delivers 126.9x the FP16 throughput and 6.4x the memory bandwidth of the A30.