Specifications Compared
| Spec | B300 | H100 |
|---|---|---|
| TDP | 1200W | 700W |
| VRAM | 288 GB | 80-94 GB |
| Memory Type | HBM3e | HBM3 |
| Architecture | Blackwell Ultra | Hopper |
| Form Factors | SXM | SXM5, PCIe, NVL |
| Interconnect | NVSwitch, NVLink | NVLink, PCIe 5.0, InfiniBand |
| FP8 Performance | 4,500 TFLOPS | 3,958 TFLOPS |
| FP16 Performance | 2,250 TFLOPS | 1,979 TFLOPS |
| FP32 Performance | 90 TFLOPS | 67 TFLOPS |
| FP64 Performance | 45 TFLOPS | 34 TFLOPS |
| INT8 Performance | 4,500 TOPS | 3,958 TOPS |
| Memory Bandwidth | 12,000 GB/s | 3,350 GB/s |
Performance Analysis
The B300's FP16 throughput of 2250 TFLOPS outpaces the H100's 1979 TFLOPS: this advantage accelerates LLM training where mixed-precision computations dominate, reducing epochs by handling larger effective batch sizes. FP32 performance at 90 TFLOPS versus 67 TFLOPS benefits scientific computing tasks requiring higher precision, such as fluid dynamics simulations.
Memory capacity defines model scale potential: 288 GB HBM3e on the B300 fits trillion-parameter LLMs in a single GPU, while 80-94 GB HBM3 on the H100 necessitates model parallelism for similar sizes. Bandwidth of 12000 GB/s on the B300 versus 3350 GB/s minimizes stalls during inference, enabling larger batch sizes and higher throughput in serving pipelines.
Power draw impacts deployment density: the B300's 1200W TDP demands advanced cooling, contrasting the H100's 700W for more flexible rack utilization. FP8 rates of 4500 TFLOPS on B300 over 3958 TFLOPS suit quantized inference, amplifying edge in low-precision deployments.
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 | |||
 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 |
H100 SXM5
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Hyperstack | 4×NVIDIA H100 PCIe 80GB VRAM | 80GB | 124 vCPU 720GB RAM 3300GB Storage | Canada | $1.90/GPU/hr $7.60/hr total (4×) | Available | ||
Hyperstack | 2×NVIDIA H100 PCIe 80GB VRAM | 80GB | 60 vCPU 360GB RAM 1600GB Storage | Canada | $1.90/GPU/hr $3.80/hr total (2×) | Available | ||
Hyperstack | 8×NVIDIA H100 PCIe 80GB VRAM | 80GB | 252 vCPU 1440GB RAM 6600GB Storage | Canada | $1.90/GPU/hr $15.20/hr total (8×) | Available | ||
Hyperstack | NVIDIA H100 PCIe 80GB VRAM | 80GB | 28 vCPU 180GB RAM 850GB Storage | Canada | $1.90/GPU/hr | Available | ||
Hyperstack | 8×NVIDIA H100 PCIe 80GB VRAM | 80GB | 252 vCPU 1440GB RAM 6600GB Storage | Canada | $1.95/GPU/hr $15.60/hr total (8×) | Available |
When to Choose the B300 SXM6
Select the B300 SXM6 for frontier AI research involving models exceeding 500B parameters: 288 GB VRAM accommodates full in-GPU loading, and 12000 GB/s bandwidth sustains high-throughput training across NVSwitch domains. It excels in multi-node clusters where FP16 at 2250 TFLOPS cuts time-to-result versus H100 scaling limits.
When to Choose the H100 SXM5
Choose the H100 SXM5 for cost-sensitive production inference or fine-tuning: pricing from $0.80/hr averages $3.50/hr across 36 providers, far below B300's $2.45/hr start. Its 1979 TFLOPS FP16 and 700W TDP suffice for models under 70B parameters in dense cloud fleets with NVLink and PCIe options.
Use Cases
B300's 288 GB VRAM fits massive models without excessive sharding. 2250 TFLOPS FP16 accelerates convergence over H100's 1979 TFLOPS.
12000 GB/s bandwidth supports huge batch sizes for high QPS. 4500 TFLOPS FP8 outperforms H100's 3958 TFLOPS in quantized serving.
H100's 80-94 GB VRAM handles most adapters at lower $3.50/hr average. B300 shines for parameter-efficient methods on giants.
H100's 1979 TFLOPS FP16 suffices for image gen at $0.80/hr entry. B300 overkill unless scaling to video diffusion.
90 TFLOPS FP32 exceeds H100's 67 TFLOPS for simulations. 288 GB VRAM aids large datasets in climate modeling.
Frequently Asked Questions
Which GPU has more VRAM: B300 or H100?▾
The B300 SXM6 provides 288 GB HBM3e VRAM. The H100 SXM5 offers 80-94 GB HBM3. This enables B300 to load much larger models singly.
Is the B300 faster than H100 in FP16?▾
B300 achieves 2250 TFLOPS FP16. H100 reaches 1979 TFLOPS. The gap favors B300 in tensor-heavy training.
What are the cloud prices for B300 vs H100?▾
B300 SXM6 starts at $2.45/hr, averaging $6.44/hr over 7 offers. H100 SXM5 begins at $0.80/hr, averaging $3.50/hr across 36. H100 offers better value density.
B300 power consumption compared to H100?▾
B300 TDP is 1200W. H100 uses 700W. B300 requires stronger infrastructure.
Best GPU for large LLM inference?▾
B300 excels with 12000 GB/s bandwidth and 288 GB VRAM for big batches. H100 works for smaller models at lower cost.
Architecture difference between B300 and H100?▾
B300 uses 2025 Blackwell Ultra. H100 employs 2022 Hopper. B300 brings FP8 at 4500 TFLOPS versus 3958 TFLOPS.
Which is cheaper to rent, the B300 or the H100?▾
Cloud rental prices for both the B300 and H100 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 B300 have compared to the H100?▾
The B300 has 288 GB of HBM3e memory. The H100 has 80 to 94 GB of HBM3 memory.
Can I find B300 and H100 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 B300 and the H100?▾
The B300 uses the Blackwell Ultra architecture (2025) while the H100 uses Hopper (2022). The B300 delivers 1.1x the FP16 throughput and 3.6x the memory bandwidth of the H100.