Specifications Compared
| Spec | A100 | MI325X |
|---|---|---|
| TDP | 400W | 750W |
| VRAM | 40-80 GB | 256 GB |
| CUDA Cores | 6,912 | |
| Memory Type | HBM2e | HBM3e |
| Architecture | Ampere | CDNA 3 |
| Form Factors | SXM4, PCIe | OAM |
| Interconnect | NVLink, PCIe 4.0, InfiniBand | Infinity Fabric |
| Tensor Cores | 432 | |
| FP16 Performance | 312 TFLOPS | 1,307 TFLOPS |
| FP32 Performance | 19.5 TFLOPS | 1307 TFLOPS |
| FP64 Performance | 9.7 TFLOPS | 40.9 TFLOPS |
| INT8 Performance | 624 TOPS | 2,614 TOPS |
| Memory Bandwidth | 2,039 GB/s | 6,000 GB/s |
Performance Analysis
Memory specifications define key advantages: the MI325X's 256 GB HBM3e VRAM dwarfs the A100's 40 to 80 GB HBM2e, enabling larger models or batch sizes without swapping. Its 6000 GB/s bandwidth, versus 2039 GB/s, accelerates data transfers, reducing bottlenecks in memory-bound tasks like LLM training.
Compute differences highlight architecture shifts. The A100's FP16 at 312 TFLOPS suits mixed-precision training, but its FP32 at 19.5 TFLOPS limits single-precision workloads. The MI325X balances both at 1307 TFLOPS and adds FP8 at 2614 TFLOPS, optimizing inference for quantized models. This balance supports versatile AI pipelines.
Power demands reflect performance: the MI325X's 750W TDP exceeds the A100's 400W, implying higher infrastructure costs but superior throughput per watt in high-end scenarios.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
A100
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Vast.ai | NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 256 vCPU 63GB RAM 2826GB Storage | Slovenia | $0.73/GPU/hr | Available | ||
Vast.ai | 2×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 256 vCPU 126GB RAM 794GB Storage | Slovenia | $0.73/GPU/hr $1.47/hr total (2×) | Available | ||
 LeaderGPU | 8×NVIDIA A100 PCIe 80GB 80GB VRAM | 80GB | 64 vCPU 384GB RAM 2000GB Storage | Netherlands | $0.90/GPU/hr $7.20/hr total (8×) | Available | ||
Vast.ai | NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 64 vCPU 63GB RAM 646GB Storage | Czechia | $1.07/GPU/hr | Available | ||
 Denvr | 8×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 128 vCPU 1024GB RAM 15200GB Storage | Virginia | $1.15/GPU/hr $9.20/hr total (8×) |
When to Choose the A100
The A100 excels in production environments requiring immediate availability. With 59 live cloud offers from $0.45 per hour, it enables rapid scaling for LLM fine-tuning or inference without delays. Its 400W TDP fits power-constrained clusters, and mature NVLink support ensures reliable multi-GPU training.
Legacy workflows benefit from the A100's ecosystem, including optimized CUDA software stacks absent in the newer MI325X.
When to Choose the MI325X
The MI325X suits memory-intensive applications like training massive LLMs. Its 256 GB VRAM handles models exceeding 80 GB, while 6000 GB/s bandwidth supports enormous batch sizes. Balanced 1307 TFLOPS FP16 and FP32 performance accelerates both training and precision-sensitive simulations.
Forward-looking deployments favor the MI325X for its FP8 capabilities at 2614 TFLOPS, ideal for efficient inference at scale.
Use Cases
The MI325X's 256 GB VRAM and 6000 GB/s bandwidth handle massive datasets and large batch sizes better than the A100's 80 GB maximum. Its 1307 TFLOPS FP16 outperforms the A100's 312 TFLOPS for accelerated training.
FP8 performance at 2614 TFLOPS on the MI325X optimizes quantized inference for high throughput. The 256 GB VRAM supports serving larger models without fragmentation issues seen in the A100.
A100's availability at $0.45 per hour suits quick iterations, while MI325X's higher bandwidth aids larger fine-tuning batches. Choice depends on model size and urgency.
MI325X's 1307 TFLOPS FP32 matches its FP16 for diffusion model generation, surpassing A100's 19.5 TFLOPS FP32. Vast VRAM enables high-resolution image batches.
Balanced FP32 at 1307 TFLOPS on MI325X excels in simulations requiring precision, far beyond A100's 19.5 TFLOPS. Infinity Fabric aids multi-node scaling.
Frequently Asked Questions
Which has more VRAM: A100 or MI325X?▾
The MI325X provides 256 GB HBM3e VRAM, compared to the A100's 40 to 80 GB HBM2e. This difference allows the MI325X to load much larger AI models in memory.
What is the memory bandwidth difference between A100 and MI325X?▾
MI325X offers 6000 GB/s, nearly triple the A100's 2039 GB/s. Higher bandwidth reduces data movement latency for training and inference.
Is MI325X available on cloud providers?▾
No live cloud offers exist for MI325X currently. A100 has 59 offers from $0.45 per hour averaging $1.91 per hour.
How do FP16 performances compare?▾
MI325X achieves 1307 TFLOPS FP16, over four times the A100's 312 TFLOPS. This boosts mixed-precision AI training speeds.
What are the power requirements?▾
A100 has a 400W TDP, while MI325X requires 750W. Higher TDP correlates with MI325X's superior compute and memory specs.
Which is better for LLM inference?▾
MI325X leads with FP8 at 2614 TFLOPS and 256 GB VRAM for quantized large models. A100 remains viable for smaller deployments due to availability.
Which is cheaper to rent, the A100 or the MI325X?▾
Cloud rental prices for both the A100 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 A100 have compared to the MI325X?▾
The A100 has 40 to 80 GB of HBM2e memory. The MI325X has 256 GB of HBM3e memory.
Can I find A100 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 A100 and the MI325X?▾
The A100 uses the Ampere architecture (2020) while the MI325X uses CDNA 3 (2024). The MI325X delivers 4.2x the FP16 throughput and 2.9x the memory bandwidth of the A100.