Specifications Compared
| Spec | A30 | GB300 |
|---|---|---|
| TDP | 165W | 1400W |
| VRAM | 24 GB | 288 GB |
| CUDA Cores | 3,584 | |
| Memory Type | HBM2 | HBM3e |
| Architecture | Ampere | Blackwell Ultra |
| Form Factors | PCIe | SXM |
| Interconnect | NVLink | NVSwitch, NVLink |
| Tensor Cores | 224 | |
| FP16 Performance | 10.3 TFLOPS | 2,250 TFLOPS |
| FP32 Performance | 10.3 TFLOPS | 90 TFLOPS |
| FP64 Performance | 5.2 TFLOPS | 45 TFLOPS |
| INT8 Performance | 165 TOPS | 4,500 TOPS |
| Memory Bandwidth | 933 GB/s | 12,000 GB/s |
Performance Analysis
Compute performance differences define primary use case separations. The A30 delivers symmetric 10.3 TFLOPS in both FP16 and FP32, supporting general-purpose tasks like scientific simulations or moderate AI inference. The GB300 shifts focus to AI acceleration: its 2250 TFLOPS FP16 enables rapid deep learning training, while 90 TFLOPS FP32 maintains strong single-precision compute; the 4500 TFLOPS FP8 variant optimizes low-precision inference for deployment.
Memory specifications profoundly impact real-world scalability. The GB300's 288 GB HBM3e VRAM versus the A30's 24 GB HBM2 allows loading massive models without partitioning, supporting batch sizes up to 12 times larger in LLM training. Coupled with 12000 GB/s bandwidth against 933 GB/s, the GB300 minimizes data bottlenecks, reducing training epochs by orders of magnitude for memory-intensive workloads.
Power and form factors further differentiate efficiency. The A30's 165W TDP fits PCIe slots in dense servers, whereas the GB300's 1400W SXM design demands liquid cooling for sustained peak performance.
Live Cloud Pricing
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When to Choose the A30
The A30 suits legacy or constrained deployments. Its 165W TDP and PCIe form factor integrate into existing servers without power or cooling overhauls, ideal for small-scale inference or fine-tuning models under 24 GB VRAM. Balanced 10.3 TFLOPS FP16 and FP32 performance handles scientific computing or Stable Diffusion at modest scales where upgrade costs outweigh benefits.
When to Choose the GB300 SXM6
The GB300 dominates large-scale AI production. With 288 GB HBM3e VRAM and 12000 GB/s bandwidth, it trains LLMs exceeding 100B parameters in single-GPU setups, leveraging 2250 TFLOPS FP16 for faster iterations. NVSwitch and NVLink enable seamless multi-GPU clusters for enterprise inference at 4500 TFLOPS FP8.
Use Cases
GB300's 288 GB HBM3e VRAM and 2250 TFLOPS FP16 handle massive models without sharding, unlike A30's 24 GB limit. Bandwidth of 12000 GB/s accelerates data loading for large batches.
4500 TFLOPS FP8 on GB300 optimizes high-throughput serving for production LLMs. A30's 10.3 TFLOPS FP16 cannot match speed or scale beyond small models.
GB300 supports full-model fine-tuning with 288 GB VRAM, reducing multi-GPU needs. A30 limits to smaller adapters within 24 GB.
GB300's 2250 TFLOPS FP16 generates images 200 times faster than A30's 10.3 TFLOPS. Higher bandwidth sustains high-resolution batches.
A30's balanced 10.3 TFLOPS FP32 fits modest simulations in PCIe setups. GB300's 90 TFLOPS FP32 excels for complex HPC but requires SXM infrastructure.
Frequently Asked Questions
What is the VRAM difference between A30 and GB300?▾
The A30 has 24 GB HBM2 VRAM, while the GB300 offers 288 GB HBM3e. This 12-fold increase enables GB300 to manage much larger AI models without memory errors.
Which GPU has higher FP16 performance?▾
GB300 achieves 2250 TFLOPS FP16, vastly surpassing A30's 10.3 TFLOPS. This gap accelerates deep learning training by over 200 times.
How do their TDPs compare?▾
A30 consumes 165W, suitable for standard servers. GB300 requires 1400W, demanding advanced cooling for SXM deployments.
What architectures do they use?▾
A30 employs Ampere from 2021 with NVLink. GB300 uses Blackwell Ultra from 2025, adding NVSwitch for superior multi-GPU connectivity.
Is GB300 better for memory bandwidth?▾
GB300 provides 12000 GB/s, 12.8 times higher than A30's 933 GB/s. This supports larger batch sizes in training without slowdowns.
Can A30 handle large LLMs?▾
A30's 24 GB VRAM limits it to models under that threshold. GB300's 288 GB accommodates full LLMs up to hundreds of billions of parameters.
Which is cheaper to rent, the A30 or the GB300?▾
Cloud rental prices for both the A30 and GB300 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 GB300?▾
The A30 has 24 GB of HBM2 memory. The GB300 has 288 GB of HBM3e memory.
Can I find A30 and GB300 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 GB300?▾
The A30 uses the Ampere architecture (2021) while the GB300 uses Blackwell Ultra (2025). The GB300 delivers 218.4x the FP16 throughput and 12.9x the memory bandwidth of the A30.