Shadeform primary logo

H100 vs B300

Explore a head to head comparison of specifications, performance, and pricing.

H100

The NVIDIA H100 is a Hopper-based GPU that provides exceptional performance, scalability, and economics for AI, deep learning, and HPC workloads.

ManufacturerNVIDIA
GPU ArchitectureHopper
Average Price$10.12/hr
GPU VRAM80 GB
Cloud Availability13 clouds
System Memory1920 GB
CPU Cores252
Storage31.3 TB

B300

The NVIDIA B300 delivers high-performance computing capabilities for AI, machine learning, and data science applications.

ManufacturerNVIDIA
GPU Architecture
Average Price$27.24/hr
GPU VRAM288 GB
Cloud Availability1 clouds
System Memory2200 GB
CPU Cores240
Storage6.0 TB

H100 vs B300: Which Should You Choose?

The B300 offers 288 GB of VRAM — 4× the 80 GB on the H100 — making it better suited for large model workloads that require holding more parameters in GPU memory. On FP16 throughput, the H100 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 H100, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the H100 is built on Hopper while the B300 uses Blackwell Ultra, reflecting different generational capabilities and optimizations. On Shadeform, the H100 starts from $1.66/hr versus $7.40/hr for the B300 — 346% more expensive — reflecting the performance premium. The H100 is available across 13 cloud providers on Shadeform compared to 1 for the B300, giving more options for region and pricing flexibility.

H100 — 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 H100 when:

  • 80 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 H100 & B300 compare

Compare detailed hardware specifications and average pricing for the H100 and B300.

Compare Hardware Specifications

H100B300
GPU Type
H100
B300
VRAM per GPU
80 GB
288 GB
Manufacturer
NVIDIA
NVIDIA
Architecture
Hopper
Blackwell Ultra
Interconnect
PCIe Gen5 or SXM5
SXM6
Memory Bandwidth
3.35 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
1365 MHz
1665 MHz
Boost Clock
1785 MHz
2032 MHz
TDP
350-700W
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

H100B300
1 GPU
$2.85 /hr
$7.40 /hr
2 GPUs
$5.19 /hr
$14.80 /hr
4 GPUs
$9.79 /hr
$29.20 /hr
8 GPUs
$19.35 /hr
$57.56 /hr

Frequently Asked Questions: H100 vs B300

The main differences are VRAM (80 GB vs 288 GB), FP16 throughput (267.6 vs 1 TFLOPS), architecture (Hopper vs Blackwell Ultra). The H100 uses the Hopper architecture while the B300 is based on Blackwell Ultra, giving each GPU different generational capabilities.

The H100 is generally better for large language model training due to its higher throughput and 80 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 H100 is available from $1.66/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 80 GB on the H100. 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 H100 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 H100 is currently available across 13 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 H100 or B300. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.

Explore H100 & B300 Instances

Browse available instances with H100 and B300 GPUs. Filter by provider, availability, and more to find the perfect instance for your needs.

Explore more GPU comparisons

Select any two GPUs to compare their specifications and explore pricing across providers.

Manage 30+ GPU clouds in one platform