NVIDIA B200 vs H100: GPU Specs, Use Cases & Performance

NVIDIA's B200 and H100 aren’t rivals - they’re just built for different jobs. While Blackwell architecture has a large memory pool, high bandwidth, and low-precision modes, Hopper architecture remains a workhorse in its own right, when applied to the right use cases.

As AI researchers, startups, and enterprise teams decide between the two, let’s take a technical look at both GPUs, their capabilities, and the workloads they serve best.

NVIDIA H100: The Proven Workhorse

Problem: Many teams working on AI today need reliable hardware that balances strong performance with predictable costs. If the hardware isn’t optimized for efficiency, training and inference at scale will quickly become expensive.

Solution: NVIDIA built the H100 GPU on its Hopper architecture to maximize performance on large language models. It boasts ease-of-use for fine-tuning and inference tasks and is a go-to for startups and enterprises needing high performance compute at a lower price point.

Key Specifications

GPU Memory: 640 GB HBM3

Performance:

• FP8: 32 PFLOPS
• FP16: 16 PFLOPS
• FP32: 540 TFLOPS
• FP64: 270 TFLOPS

Bandwidth: 27 GB/s aggregate GPU bandwidth, 7.2 TB/s total interconnect

NVLink: 4th generation, NVSwitch for 900 GB/s GPU-to-GPU

Use Cases for the H100

Smaller research teams and startups often don’t need frontier-scale compute. They need GPUs to handle fine-tuning, inference, and iterative testing at a sustainable cost. The H100 is strong enough for training large models. It works best for mid-scale tasks and regular experiments. Its efficiency and predictability help keep costs low. Common use cases include:

• Fine-Tuning Models: With 80 GB of memory, the H100 is well-suited for fine-tuning models in the 7B–70B parameter range.
• Inference at Scale: Strong inference performance for GPT-class models under ~70B parameters.
• Experimentation and Iteration: Cost-efficient for many smaller runs, helping teams get results faster without overspending.

NVIDIA B200: The Breakthrough

Problem: As models grow to trillions of parameters, traditional GPUs like the H100 reach limits. They struggle with memory capacity, bandwidth, and raw compute speed. This creates bottlenecks that slow down both training and inference.

Solution: The NVIDIA HGX B200 removes those bottlenecks. It has almost 2.5 times the memory and 2.4 times the bandwidth. It also includes new low-precision modes. This design serves advanced AI research and production tasks.

Key Specifications

GPU Memory: 1.44 TB total (64 TB/s HBM3e memory bandwidth)

Performance:

• FP8: 72 PFLOPS training
• FP4: 144 PFLOPS inference

Bandwidth: 14.4 TB/s NVLink bandwidth (≈2× H100 interconnect)

NVSwitch: 2× NVSwitch chips for scaling

CPU & System: Dual 5th-gen Intel Xeon CPUs, 2–4 TB system memory, advanced networking (up to 400 Gb/s InfiniBand/Ethernet).

Use Cases for the B200

Scaling AI workloads with billions or trillions of parameters requires power, high throughput and controlled costs. Traditional architectures can’t keep up with these demands. The B200’s design lets it handle much larger training runs, longer context windows, and demanding inference pipelines. It thrives where every bottleneck removed means faster results and better economics. Consider the NVIDIA B200 if you need:

• Large-Scale Training: Blackwell’s FP8 + FP4 engines are tuned for massive models (LLMs with hundreds of billions/trillions of parameters). Memory + bandwidth let it feed GPUs efficiently.‍
• High-Throughput Inference at Scale: FP4 acceleration (144 PFLOPS) gives 10×+ speedups for serving giant LLMs in production.‍
• Enterprise AI Factories: Built for continuous training, fine-tuning, and deployment pipelines in a single platform. Best fit for companies scaling frontier models, not just running experiments.

The bottom line is: the NVIDIA HGX H100 is best for experimentation, research, and fine-tuning, with strong FP16/FP32 support and solid scalability.

The NVIDIA HGX B200 was built for large-scale training and high-throughput inference, leveraging FP4/FP8 performance, massive memory bandwidth, and system integration for enterprise AI factories.

Technical Specs: HGX H100s vs HGX B200s

For teams that need clear technical benchmarks to decide what hardware best matches their needs, here is a direct comparison of memory, bandwidth, precision, and interconnects as provided by NVIDIA: 

‍H100 vs B200: Workload Comparison

Not every AI workload requires maximum power. Using the wrong GPU can mean wasted budget on one end, or painfully slow jobs. To give teams clarity on when each GPU makes sense, here’s a listed comparison breaking down how the H100 and B200 each work for fine-tuning, training, and inference.

Fine-Tuning LLMs

• H100 GPU: Best for 7B–70B parameter models, cost-efficient and predictable.
• B200 GPU: Ideal for 100B+ parameters or workloads that need longer context windows and more memory.

Training from Scratch

• H100 GPU: Reliable for training large models but with longer wall-clock times.
• B200 GPU: Cuts training times significantly. Best for frontier-scale research where speed compounds into competitive advantage.

Inference at Scale

• H100 GPU: Efficient for production inference under ~70B parameters.
• B200 GPU: Optimized for trillion-parameter inference. FP4 pipelines deliver up to 30× faster throughput.

Mapping abstract specs into real-world scenarios clarifies when to choose the H100 and when the B200 is the better fit.

The Economics: Why “Expensive” Is Misleading

Many people compare hourly rates and are quick to discount a GPU as too expensive. However, hourly pricing isn’t the only consideration and doesn’t reflect the true cost of training and inference on hardware that isn’t up to those tasks.

The true measure is cost per completed job. Consider this:

• A training run that takes 10 hours on an H100 GPU might take just 5 hours on a B200 GPU.
• Even with a higher hourly rate, the cost per job can drop significantly because it requires less time.
• Faster throughput also reduces time-to-market, an economic advantage not captured by hourly pricing.

How Voltage Park can help

At the end of the day, the choice isn’t about which GPU is “better.” It’s about matching the right tool to the right job.

Whether you are growing your research scope or handling more production tasks, at Voltage Park, our experts excel at matching the right tool to the right job for our customers. Plus, our 24/7 global support is included with both GPU options. Contact us to start choosing and setting up the right hardware for your business.

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