Specifications Compared
| Spec | MI300X | RTX-3070 |
|---|---|---|
| TDP | 750W | 220W |
| VRAM | 192 GB | 8 GB |
| Memory Type | HBM3 | GDDR6 |
| Architecture | CDNA 3 | Ampere |
| Form Factors | OAM | PCIe |
| Interconnect | Infinity Fabric, PCIe 5.0 | |
| FP8 Performance | 2,614 TFLOPS | |
| FP16 Performance | 1,307 TFLOPS | 20.3 TFLOPS |
| FP32 Performance | 163 TFLOPS | 20.3 TFLOPS |
| FP64 Performance | 81.7 TFLOPS | |
| INT8 Performance | 2,614 TOPS | |
| Memory Bandwidth | 5,300 GB/s | 448 GB/s |
Performance Analysis
The MI300X dominates in compute throughput: its 1307 TFLOPS FP16 rating supports accelerated AI training on massive datasets, while the RTX 3070's 20.3 TFLOPS limits it to modest scales. The FP16 to FP32 ratio underscores specialization; the MI300X drops to 163 TFLOPS in FP32 from 1307 TFLOPS FP16, indicating tuning for low-precision tensor operations common in deep learning training. The RTX 3070 maintains parity at 20.3 TFLOPS across both, better suiting graphics or general-purpose floating-point tasks.
Memory capacity proves decisive for real-world workloads: 192 GB HBM3 on the MI300X enables training models with billions of parameters or inference at high batch sizes, whereas 8 GB GDDR6 on the RTX 3070 restricts users to smaller models or low-batch inference. Bandwidth amplifies this gap; 5300 GB/s on the MI300X sustains data flow for large transformer models, reducing bottlenecks, compared to 448 GB/s on the RTX 3070 which hampers throughput in memory-intensive scenarios.
Power draw reflects efficiency profiles: the MI300X's 750W TDP demands robust cooling for sustained datacenter runs, while the RTX 3070's 220W fits edge or desktop setups. These specs translate to the MI300X handling enterprise inference 10-50x faster on large LLMs.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
MI300X
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | AMD Instinct MI300X 192GB VRAM | 192GB | 24 vCPU 256GB RAM | 🌍global | $1.99/GPU/hr | |||
 Hot Aisle | AMD Instinct MI300X 192GB VRAM | 192GB | 8 vCPU 224GB RAM 12288GB Storage | Michigan | $1.99/GPU/hr | Available | ||
 Cirrascale | 8×AMD Instinct MI300X 192GB VRAM | 192GB | 192 vCPU 2355GB RAM 44538GB Storage | United States | $3.08/GPU/hr $24.64/hr total (8×) | |||
 Crusoe | AMD Instinct MI300X 192GB VRAM | 192GB | 0 vCPU 0GB RAM | United States | $3.45/GPU/hr | |||
Cirrascale | 8×AMD Instinct MI300X 192GB VRAM | 192GB | 192 vCPU 2355GB RAM 44538GB Storage | United States | $3.47/GPU/hr $27.76/hr total (8×) |
When to Choose the MI300X
Opt for the MI300X in large-scale AI training or inference: its 192 GB HBM3 VRAM accommodates models exceeding 100 billion parameters, and 5300 GB/s bandwidth supports batch sizes impractical on consumer cards. Scientific simulations benefit from 1307 TFLOPS FP16 and Infinity Fabric interconnect for multi-GPU scaling.
Cloud deployments at $0.50 per hour justify it for production workloads where time-to-result trumps cost.
When to Choose the RTX 3070
Select the RTX 3070 for cost-sensitive prototyping or gaming-integrated compute: at $0.04 per hour, it delivers 20.3 TFLOPS FP16 for fine-tuning small models or running Stable Diffusion locally. Its 220W TDP and PCIe form factor suit single-node development without datacenter infrastructure.
Hobbyists or startups testing ideas find 8 GB GDDR6 sufficient for inference on models under 7 billion parameters.
Use Cases
The MI300X's 1307 TFLOPS FP16 and 192 GB HBM3 handle massive datasets and parameters. The RTX 3070's 8 GB VRAM cannot support equivalent scales.
192 GB VRAM and 5300 GB/s bandwidth enable high-batch inference on large models. RTX 3070 limits to small models due to 8 GB capacity.
RTX 3070 suffices for models under 7B parameters at low cost. MI300X excels for larger fine-tuning with 1307 TFLOPS FP16.
RTX 3070's 20.3 TFLOPS and GDDR6 optimize image generation at $0.04 per hour. MI300X overkill for typical diffusion tasks.
MI300X's 163 TFLOPS FP32 and PCIe 5.0 suit simulations. Vast VRAM outperforms RTX 3070's constraints.
Frequently Asked Questions
Which GPU has more VRAM, MI300X or RTX 3070?▾
The MI300X provides 192 GB HBM3 VRAM. The RTX 3070 offers 8 GB GDDR6. This 24x difference favors large model handling on the MI300X.
How do FP16 performances compare between MI300X and RTX 3070?▾
MI300X achieves 1307 TFLOPS in FP16. RTX 3070 reaches 20.3 TFLOPS. The MI300X exceeds by 64 times for AI acceleration.
What is the memory bandwidth of the MI300X versus RTX 3070?▾
MI300X delivers 5300 GB/s. RTX 3070 provides 448 GB/s. Higher bandwidth on MI300X supports faster data transfers in training.
Which GPU is cheaper in the cloud?▾
RTX 3070 starts at $0.04 per hour, averaging $0.08. MI300X begins at $0.50 per hour, averaging $2.63. RTX 3070 suits budgets.
What are the TDPs for these GPUs?▾
MI300X requires 750W TDP. RTX 3070 uses 220W. Lower TDP makes RTX 3070 easier for non-datacenter setups.
Is MI300X better for AI training than RTX 3070?▾
Yes, MI300X's 1307 TFLOPS FP16 and 192 GB VRAM dominate training. RTX 3070's specs limit it to small-scale work.
Which is cheaper to rent, the MI300X or the RTX 3070?▾
Cloud rental prices for both the MI300X and RTX 3070 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 MI300X have compared to the RTX 3070?▾
The MI300X has 192 GB of HBM3 memory. The RTX 3070 has 8 GB of GDDR6 memory.
Can I find MI300X and RTX 3070 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 MI300X and the RTX 3070?▾
The MI300X uses the CDNA 3 architecture (2023) while the RTX 3070 uses Ampere (2020). The MI300X delivers 64.4x the FP16 throughput and 11.8x the memory bandwidth of the RTX 3070.