Specifications Compared
| Spec | GTX-1080 | MI325X |
|---|---|---|
| TDP | 180W | 750W |
| VRAM | 8-11 GB | 256 GB |
| CUDA Cores | 2,560 | |
| Memory Type | GDDR5X | HBM3e |
| Architecture | Pascal | CDNA 3 |
| Form Factors | PCIe | OAM |
| Interconnect | Infinity Fabric | |
| FP16 Performance | 8.9 TFLOPS | 1,307 TFLOPS |
| FP32 Performance | 8.9 TFLOPS | 1307 TFLOPS |
| Memory Bandwidth | 320 GB/s | 6,000 GB/s |
Performance Analysis
Compute performance differs dramatically between the GTX 1080 and MI325X. The GTX 1080 delivers 8.9 TFLOPS in FP16 and FP32, suitable for basic training or inference on small models. The MI325X achieves 1307 TFLOPS in FP16 and FP32, a 147-fold increase, enabling rapid processing of large-scale deep learning tasks; its 2614 TFLOPS FP8 capability further accelerates inference on quantized models.
Memory specifications profoundly impact real-world usage. With only 8 to 11 GB GDDR5X at 320 GB/s, the GTX 1080 limits batch sizes in training to small datasets and struggles with memory-intensive inference. The MI325X's 256 GB HBM3e and 6000 GB/s bandwidth support massive batch sizes for efficient LLM training and handle high-throughput inference without bottlenecks.
Power and form factors also shape deployment. The GTX 1080's 180W TDP and PCIe interface fit low-power consumer systems, whereas the MI325X's 750W TDP and OAM form with Infinity Fabric suit datacenter-scale clusters for sustained high-performance computing.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
GTX 1080
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 LeaderGPU | 4×NVIDIA GeForce GTX 1080 8GB VRAM | 8GB | 0 vCPU 64GB RAM 480GB Storage | Netherlands | $0.30/GPU/hr $1.20/hr total (4×) | Available | ||
LeaderGPU | 8×NVIDIA GeForce GTX 1080 Ti 11GB VRAM | 11GB | 0 vCPU 128GB RAM 480GB Storage | Netherlands | $0.60/GPU/hr $4.80/hr total (8×) | Available |
When to Choose the GTX 1080
The GTX 1080 excels in cost-sensitive, low-demand scenarios. At $0.30 per hour, it provides accessible cloud access for hobbyist AI experiments, lightweight inference on models under 8 GB, or legacy gaming and rendering tasks. Its 180W TDP and PCIe compatibility enable easy integration into desktops or small servers without high power infrastructure.
Choose the GTX 1080 for prototyping small neural networks or Stable Diffusion runs where 8.9 TFLOPS suffices and 320 GB/s bandwidth handles modest data flows.
When to Choose the MI325X
The MI325X dominates professional AI workloads requiring extreme scale. Its 256 GB HBM3e VRAM accommodates full-parameter fine-tuning of large language models, while 6000 GB/s bandwidth sustains large batch sizes in training. The 1307 TFLOPS FP16/FP32 and 2614 TFLOPS FP8 performance accelerate enterprise inference pipelines.
Opt for the MI325X in datacenters leveraging Infinity Fabric for multi-GPU setups and 750W TDP tolerance, ideal for scientific simulations or production LLM serving.
Use Cases
The MI325X's 256 GB HBM3e VRAM and 1307 TFLOPS FP16 performance support training massive LLMs with large batch sizes. The GTX 1080's 8 to 11 GB VRAM limits it to tiny models.
MI325X delivers 2614 TFLOPS FP8 for high-throughput quantized inference on large models, backed by 6000 GB/s bandwidth. GTX 1080's 8.9 TFLOPS FP16 restricts scale.
With 256 GB VRAM, MI325X handles full fine-tuning of billion-parameter models efficiently via 1307 TFLOPS compute. GTX 1080 suits only parameter-efficient methods on small models.
GTX 1080's 8.9 TFLOPS and 8 to 11 GB VRAM generate images adequately at low cost. MI325X accelerates batch generation with superior bandwidth but overkill for casual use.
MI325X's 1307 TFLOPS FP32 and Infinity Fabric enable large-scale simulations. GTX 1080's 8.9 TFLOPS limits complex computations.
Frequently Asked Questions
Which GPU has more VRAM?▾
The MI325X provides 256 GB HBM3e VRAM compared to the GTX 1080's 8 to 11 GB GDDR5X. This enables the MI325X to load much larger models. The difference supports enterprise AI versus consumer tasks.
What is the FP32 performance comparison?▾
MI325X offers 1307 TFLOPS FP32, while GTX 1080 delivers 8.9 TFLOPS. This 147 times gap favors MI325X for compute-intensive workloads. FP32 equality within each GPU simplifies mixed-precision coding.
How do memory bandwidths differ?▾
MI325X achieves 6000 GB/s with HBM3e versus GTX 1080's 320 GB/s GDDR5X. Higher bandwidth reduces data bottlenecks in training. It directly impacts large batch size feasibility.
What are the power requirements?▾
GTX 1080 has a 180W TDP, suitable for low-power setups. MI325X requires 750W, demanding datacenter cooling. This influences deployment costs beyond raw pricing.
Is cloud pricing available for both?▾
GTX 1080 starts at $0.30 per hour average across one live offer. MI325X has no live offers currently. Pricing reflects consumer versus enterprise maturity.
What architectures do they use?▾
GTX 1080 employs Pascal from 2016, while MI325X uses CDNA 3 from 2024. The eight-year evolution yields MI325X's AI optimizations like FP8 support at 2614 TFLOPS. Pascal suits general compute.
Which is cheaper to rent, the GTX 1080 or the MI325X?▾
Cloud rental prices for both the GTX 1080 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 GTX 1080 have compared to the MI325X?▾
The GTX 1080 has 8 to 11 GB of GDDR5X memory. The MI325X has 256 GB of HBM3e memory.
Can I find GTX 1080 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 GTX 1080 and the MI325X?▾
The GTX 1080 uses the Pascal architecture (2016) while the MI325X uses CDNA 3 (2024). The MI325X delivers 146.9x the FP16 throughput and 18.8x the memory bandwidth of the GTX 1080.