Specifications Compared
| Spec | A30 | B300 |
|---|---|---|
| TDP | 165W | 1200W |
| 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 disparities define real-world impacts: the B300's 2250 TFLOPS FP16 dwarfs the A30's 10.3 TFLOPS, accelerating deep learning training by over 200 times in tensor operations. FP32 shows B300 at 90 TFLOPS against A30's 10.3 TFLOPS, benefiting simulations and graphics. The FP16/FP32 delta on B300 favors mixed-precision training, reducing time for large language models while A30 suits equal-precision legacy codes. Memory bandwidth of 12000 GB/s on B300 versus 933 GB/s on A30 supports batch sizes 12 times larger, minimizing data starvation in inference pipelines. VRAM jumps from 24 GB to 288 GB enable loading models over 200 billion parameters on B300 without sharding, unlike A30 limited to smaller datasets. Higher TDP of 1200W on B300 demands robust cooling, but yields density for hyperscale clusters.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
B300 SXM6
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA B300 SXM6 262GB VRAM | 262GB | 0 vCPU 0GB RAM | 🌍global | $7.39/GPU/hr | |||
 VERDA | NVIDIA B300 SXM6 262GB VRAM | 262GB | 30 vCPU 255GB RAM | Helsinki | $7.50/GPU/hr | Available | ||
VERDA | 2×NVIDIA B300 SXM6 262GB VRAM | 262GB | 60 vCPU 510GB RAM | Helsinki | $7.50/GPU/hr $15.00/hr total (2×) | Available | ||
VERDA | 8×NVIDIA B300 SXM6 262GB VRAM | 262GB | 240 vCPU 2040GB RAM | Helsinki | $7.50/GPU/hr $60.00/hr total (8×) | Available | ||
 Scaleway | 8×NVIDIA B300 SXM6 262GB VRAM | 262GB | 224 vCPU 3840GB RAM 22352GB Storage | Paris | $8.73/GPU/hr $69.84/hr total (8×) | Available |
When to Choose the A30
The A30 fits low-power, cost-sensitive deployments with its 165W TDP and PCIe form factor. It excels in on-premises setups lacking SXM support or high-density racks. For workloads under 24 GB VRAM like fine-tuning small models at 10.3 TFLOPS FP32, A30 avoids B300's rental costs starting at $2.45 per hour.
When to Choose the B300 SXM6
The B300 SXM6 dominates large-scale AI with 288 GB HBM3e and 12000 GB/s bandwidth. It suits training massive LLMs at 2250 TFLOPS FP16 or inference at 4500 TFLOPS FP8. Cloud users access it from $2.45 per hour across seven providers, ideal for time-critical projects outpacing A30 by orders of magnitude.
Use Cases
B300's 2250 TFLOPS FP16 and 288 GB VRAM handle massive datasets and parameters far beyond A30's 10.3 TFLOPS and 24 GB. Bandwidth at 12000 GB/s supports huge batches without bottlenecks.
B300 delivers 4500 TFLOPS FP8 for high-throughput serving of large models on 288 GB HBM3e. A30's 933 GB/s bandwidth limits scale for production inference.
A30 suffices for small models under 24 GB at 10.3 TFLOPS FP32 with low 165W power. B300 accelerates larger fine-tunes via 90 TFLOPS FP32 but at higher cost.
B300's 2250 TFLOPS FP16 generates images faster with bigger batches on 12000 GB/s bandwidth. A30 manages basic diffusion but stalls on high-res or batch workloads.
B300's 90 TFLOPS FP32 and NVSwitch excel in simulations needing high precision and multi-GPU scale. A30's NVLink works for modest HPC but lacks bandwidth depth.
Frequently Asked Questions
How much VRAM do the A30 and B300 have?▾
The A30 provides 24 GB HBM2 VRAM. The B300 offers 288 GB HBM3e, a 12-fold increase for larger models. This gap affects model capacity in AI tasks.
What are the FP16 performance figures?▾
A30 achieves 10.3 TFLOPS in FP16. B300 reaches 2250 TFLOPS, over 218 times higher for training acceleration. FP8 on B300 adds 4500 TFLOPS for inference.
What is the power consumption difference?▾
A30 draws 165W TDP in PCIe form. B300 requires 1200W in SXM, suiting dense data centers. Lower power favors A30 for edge or small clusters.
What are the cloud prices for B300?▾
NVIDIA B300 SXM6 starts at $2.45 per hour, averaging $6.44 across seven providers. A30 has no live cloud offers currently. Pricing reflects B300's superior specs.
Which has higher memory bandwidth?▾
B300 delivers 12000 GB/s with HBM3e. A30 offers 933 GB/s on HBM2, limiting batch sizes. Bandwidth boost enables B300 for large-scale data movement.
What architectures power these GPUs?▾
A30 uses Ampere from 2021 with NVLink. B300 employs Blackwell Ultra from 2025 with NVSwitch and NVLink. The jump drives B300's compute dominance.
Which is cheaper to rent, the A30 or the B300?▾
Cloud rental prices for both the A30 and B300 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 B300?▾
The A30 has 24 GB of HBM2 memory. The B300 has 288 GB of HBM3e memory.
Can I find A30 and B300 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 B300?▾
The A30 uses the Ampere architecture (2021) while the B300 uses Blackwell Ultra (2025). The B300 delivers 218.4x the FP16 throughput and 12.9x the memory bandwidth of the A30.