GH200 vs B300
Explore a head to head comparison of specifications, performance, and pricing.
GH200
The NVIDIA GH200 is an advanced Hopper-based GPU that significantly boosts performance for generative AI, LLM, and HPC workloads with enhanced memory and bandwidth.
B300
The NVIDIA B300 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
GH200 vs B300: Which Should You Choose?
The B300 offers 288 GB of VRAM — 3× the 96 GB on the GH200 — making it better suited for large model workloads that require holding more parameters in GPU memory. On FP16 throughput, the GH200 delivers 267.6 TFLOPS versus 1 TFLOPS on the B300 — 268× faster for mixed-precision training and inference. Memory bandwidth favors the B300 at 0.01 TB/s compared to 0.00 TB/s on the GH200, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the GH200 is built on Hopper while the B300 uses Blackwell Ultra, reflecting different generational capabilities and optimizations. On Shadeform, the GH200 starts from $1.49/hr versus $7.40/hr for the B300 — 397% more expensive — reflecting the performance premium. The GH200 is available across 2 cloud providers on Shadeform compared to 1 for the B300, giving more options for region and pricing flexibility.
GH200 — Best Use Cases
- •Training large language models (7B–405B parameters)
- •High-throughput LLM inference
- •Mixture-of-experts and transformer workloads
- •Distributed multi-GPU training runs
Choose GH200 when:
- ✓96 GB VRAM is sufficient for your workload
- ✓Cost efficiency is your primary concern
- ✓You are training large models or running high-throughput inference
- ✓You need flexibility across multiple cloud providers or regions
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 workload does not require peak FP16 throughput
- ✓Your preferred provider already has availability
See how the GH200 & B300 compare
Compare detailed hardware specifications and average pricing for the GH200 and B300.
Compare Hardware Specifications
| GH200 | B300 | |
|---|---|---|
| GPU Type | GH200 | B300 |
| VRAM per GPU | 96 GB | 288 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Hopper | Blackwell Ultra |
| Interconnect | NVLink-C2C | SXM6 |
| Memory Bandwidth | 4 TB/s or 4.9 TB/s | 8 TB/s |
| FP16 TFLOPS | 267.6 TFLOPS (4:1) | 1,231.8 TFLOPS (16:1) |
| CUDA Cores | 16896 | 20480 |
| Tensor Cores | 528 (4th Gen) | 640 (5th Gen) |
| Base Clock | 1500 MHz | 1665 MHz |
| Boost Clock | 1980 MHz | 2032 MHz |
| TDP | 900W-1000W | 1000W |
| Process Node | TSMC 4N | TSMC 4NP |
| Data Formats | FP8, INT8, BF16, FP16, TF32, FP32, FP64 | FP4, FP6, FP8, INT8, BF16, FP16, TF32, FP32, FP64 |
Compare Average On-Demand Pricing
| GH200 | B300 | |
|---|---|---|
| 1 GPU | $2.86 /hr | $7.40 /hr |
| 2 GPUs | N/A | $14.80 /hr |
| 4 GPUs | N/A | $29.20 /hr |
| 8 GPUs | N/A | $57.56 /hr |
Frequently Asked Questions: GH200 vs B300
The main differences are VRAM (96 GB vs 288 GB), FP16 throughput (267.6 vs 1 TFLOPS), architecture (Hopper vs Blackwell Ultra). The GH200 uses the Hopper architecture while the B300 is based on Blackwell Ultra, giving each GPU different generational capabilities.
The GH200 is generally better for large language model training due to its higher throughput and 96 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 GH200 is available from $1.49/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 96 GB on the GH200. 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 GH200 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 GH200 is currently available across 2 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 GH200 or B300. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
Explore GH200 & B300 Instances
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