B300 vs B200
Explore a head to head comparison of specifications, performance, and pricing.
B300
The NVIDIA B300 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
B200
The NVIDIA B200 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
B300 vs B200: Which Should You Choose?
The B300 offers 288 GB of VRAM — 1.5× the 192 GB on the B200 — making it better suited for large model workloads that require holding more parameters in GPU memory. Architecturally, the B300 is built on Blackwell Ultra while the B200 uses Blackwell, reflecting different generational capabilities and optimizations. On Shadeform, the B200 starts from $5.29/hr versus $7.40/hr for the B300 — 40% more expensive — reflecting the performance premium. The B200 is available across 4 cloud providers on Shadeform compared to 1 for the B300, giving more options for region and pricing flexibility.
B300 — Best Use Cases
- •Next-generation LLM pre-training at scale
- •Trillion-parameter model inference
- •Ultra-high-throughput AI workloads
- •Advanced HPC and scientific computing
Choose B300 when:
- ✓You need 288 GB+ VRAM for large models or long context windows
- ✓Maximum performance justifies the higher cost
- ✓Your preferred provider already has availability
B200 — Best Use Cases
- •Next-generation LLM pre-training at scale
- •Trillion-parameter model inference
- •Ultra-high-throughput AI workloads
- •Advanced HPC and scientific computing
Choose B200 when:
- ✓192 GB VRAM is sufficient for your workload
- ✓Cost efficiency is your primary concern
- ✓You need flexibility across multiple cloud providers or regions
See how the B300 & B200 compare
Compare detailed hardware specifications and average pricing for the B300 and B200.
Compare Hardware Specifications
| B300 | B200 | |
|---|---|---|
| GPU Type | B300 | B200 |
| VRAM per GPU | 288 GB | 192 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Blackwell Ultra | Blackwell |
| Interconnect | SXM6 | SXM6 |
| Memory Bandwidth | 8 TB/s | 8 TB/s |
| FP16 TFLOPS | 1,231.8 TFLOPS (16:1) | 1,191.2 TFLOPS (16:1) |
| CUDA Cores | 20480 | 20480 |
| Tensor Cores | 640 (5th Gen) | 640 (5th Gen) |
| Base Clock | 1665 MHz | 700 MHz |
| Boost Clock | 2032 MHz | 1965 MHz |
| TDP | 1000W | 1000W |
| Process Node | TSMC 4NP | TSMC 4NP |
| Data Formats | FP4, FP6, FP8, INT8, BF16, FP16, TF32, FP32, FP64 | FP4, FP6, FP8, INT8, BF16, FP16, TF32, FP32, FP64 |
Compare Average On-Demand Pricing
| B300 | B200 | |
|---|---|---|
| 1 GPU | $7.40 /hr | $5.29 /hr |
| 2 GPUs | $14.80 /hr | $10.49 /hr |
| 4 GPUs | $29.20 /hr | $20.78 /hr |
| 8 GPUs | $57.56 /hr | $36.68 /hr |
Frequently Asked Questions: B300 vs B200
The main differences are VRAM (288 GB vs 192 GB), architecture (Blackwell Ultra vs Blackwell). The B300 uses the Blackwell Ultra architecture while the B200 is based on Blackwell, giving each GPU different generational capabilities.
The B300 is generally better for large language model training due to its higher throughput and 288 GB of VRAM, which allows fitting larger models or larger batch sizes in a single pass. For smaller models or fine-tuning tasks where cost matters more, both GPUs can be effective.
On Shadeform, the B200 is available from $5.29/hr. The B300 starts from $7.40/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.
The B300 has more VRAM at 288 GB, compared to 192 GB on the B200. Higher VRAM allows you to run larger models without quantization, use longer context windows, and process larger batch sizes — all of which improve throughput and reduce latency for memory-bound workloads.
Based on TFLOPS per dollar, the B200 offers better raw compute value at current Shadeform on-demand rates. However, the best choice depends on your specific workload — if you need the extra VRAM or throughput of the B300, paying the premium may be justified by faster job completion and lower total cost.
The B200 is currently available across 4 cloud providers on Shadeform's network, compared to 1 for the B300. Shadeform lets you deploy either GPU across all available providers from a single platform, so you can always find available capacity without manually checking each cloud.
Mixing different GPU types in a single training cluster is generally not recommended, as it creates performance bottlenecks where faster GPUs wait for slower ones. For best results, use a homogeneous cluster of either B300 or B200. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
Explore B300 & B200 Instances
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