Comparing NVIDIA's B200 and H100: A Deep Dive into Next-Gen AI Performance

The explosion of AI, machine learning (ML), and deep learning (DL) workloads over the past decade has driven an insatiable demand for ever-more-powerful GPUs. From training massive transformer models to serving real-time inference at scale, developers and enterprises require hardware that can deliver groundbreaking throughput and efficiency. NVIDIA has long been at the forefront of GPU innovation, continually pushing the boundaries of what's possible in data-center and AI acceleration.

In 2022, NVIDIA's Hopper-based H100 Tensor Core GPU set a new bar for AI performance, delivering record-setting results across MLPerf benchmarks and enabling faster training and inference for large language models, recommender systems, and scientific simulations. Now, built on the next-generation Blackwell architecture, the B200 emerges as the H100's successor, featuring a chiplet design, doubled memory capacity, next-level precision support, and massive bandwidth gains.

Understanding the differences between the H100 and B200 is critical for architects, researchers, and IT leaders who must choose the right GPU for their workloads—whether that's training foundation models, running high-throughput inference, or tackling HPC simulations. In this deep dive, we'll compare the two GPUs across key dimensions—compute cores, memory, bandwidth, sparsity, multi-instance flexibility, benchmarks, and more—to help you make an informed decision for your next AI deployment.

An Overview of the NVIDIA GPU Range

NVIDIA's recent GPU lineup can be viewed as three generations of innovation:

Ampere (2020): A100 Tensor Core GPUs introduced HBM2e memory, the first generation of third-generation Tensor Cores, and set new records in mixed-precision training.

Hopper (2022): H100 Tensor Core GPUs built on TSMC's 4N process, added fourth-generation Tensor Cores with FP8 support, and introduced the Transformer Engine for optimized transformer workloads.

Blackwell (2025): B200 Tensor Core GPUs leverage a chiplet design on TSMC's 4NP node, double the memory capacity with HBM3e, support ultra-low precisions (FP4/FP6), and offer fifth-generation NVLink interconnect.

Each generation has brought substantial leaps in throughput and efficiency, but Blackwell represents NVIDIA's boldest architectural shift yet, scaling performance by doubling dies and memory while pushing into sub-FP8 precisions.

The Hopper Legacy: H100's Impact on AI

When NVIDIA unveiled the H100 GPU in 2022, it marked a pivotal moment for AI compute:

• Transformer Engine: By mixing FP16 and FP8 precision, H100's Transformer Engine dramatically accelerated large-model training and inference, reducing memory footprint without sacrificing accuracy.
• Fourth-Generation Tensor Cores: H100 delivered up to 990 TFLOPS of FP16 Tensor throughput (495 TFLOPS sparse) and 1.98 PFLOPS of INT8 performance (3.96 PFLOPS sparse), doubling A100's capabilities.
• HBM3 Memory: With 80 GB of HBM3 and 3.35 TB/s bandwidth, H100 alleviated memory bottlenecks for large-scale model workloads.
• Multi-Instance GPU (MIG): H100 supported up to seven isolated GPU instances, enabling cloud providers to securely partition a single GPU across multiple tenants.
• MLPerf Dominance: H100 set records across MLPerf Training and Inference v3.0, delivering up to 4.5× more inference performance than A100 and dominating every workload tested.

These advances cemented H100's status as the go-to accelerator for cutting-edge AI research and enterprise deployments. Yet as model sizes and dataset complexities continue to grow, even H100's impressive capabilities face the challenge of next-generation demands.

Enter Blackwell: What the B200 Brings to the Table

Building on Hopper's successes, NVIDIA's Blackwell architecture makes several bold moves:

• Chiplet Design: B200 packages two unified "Blackwell GPU" dies connected via a high-bandwidth NV-High Bandwidth Interface (10 TB/s per die), treating them as a single GPU.
• Massive Transistor Count: Each B200 module packs 208 billion transistors (104 B per die), over 2.5× the transistor budget of H100.
• HBM3e Memory: B200 doubles H100's capacity with 192 GB of HBM3e (24 GB per stack × 8 stacks) and 8 TB/s aggregate bandwidth—2.4× H100's bandwidth.
• Ultra-Low Precision: Beyond FP8, the B200 adds support for FP6 and FP4, enabling up to 20 PFLOPS of sparse FP4 throughput for inference—surpassing the H100, which lacks support for these formats.
• NVLink 5: Fifth-generation NVLink doubles per-link signaling to 200 Gbps, delivering 1.8 TB/s per GPU—twice H100's interconnect bandwidth.
• Higher TDP: At 1,000 W, B200 requires robust cooling (air or liquid) compared to H100's 700 W, reflecting its higher performance envelope.

These enhancements translate into targeted goals: up to 4× faster training and 30× faster inference than H100, all while improving energy efficiency by 25× for inference workloads.

Key Specifications at a Glance

CUDA Cores and Tensor Cores

Core Counts

The B200 features a moderate increase in CUDA core count, rising from 14,592 in the H100 to 16,896, providing enhanced parallel compute capabilities for general-purpose workloads.Tensor core counts rise from 456 to 528, boosting matrix-multiply throughput across precisions.

Architectural Differences

H100's fourth-generation Tensor Cores introduced FP8 and TF32 acceleration. Blackwell's fifth-generation Tensor Cores extend support down to FP4 and FP6, offering even finer-grained compute for inference.

Impact on Workloads

• AI Training: Higher FP16 and TF32 throughput on B200 accelerates backpropagation steps, reducing time-to-train for large models.
• Inference: FP4 and FP6 support dramatically increase inference throughput (up to 20 PFLOPS sparse FP4), ideal for latency-sensitive applications.
• General Compute: More CUDA cores translate to better performance on HPC kernels and non-AI workloads, such as molecular dynamics and fluid simulations.

Memory Type and Size

HBM Generations

• H100: 80 GB of HBM3 at 5.23 Gbps per pin.
• B200: 192 GB of HBM3e at 8 Gbps per pin.

Capacity Differences

B200's 192 GB capacity (24 GB per stack × 8 stacks) is 2.4× larger than H100's 80 GB, enabling training of much larger models on a single GPU and reducing off-GPU communication.

Relevance for LLMs and HPC

• Large Language Models: More on-GPU memory minimizes data sharding and communication overhead when training multi-billion-parameter models.
• High-Performance Computing: Larger datasets and higher-resolution simulations can be contained entirely in GPU memory, improving performance and simplifying code.

Memory Bandwidth

Bandwidth Specs

• H100: 3.35 TB/s aggregate.
• B200: 8 TB/s aggregate, 2.4× the bandwidth of H100.

Effect on Data Movement

Higher bandwidth ensures that Tensor Cores stay fed with data, preventing memory stalls and maximizing sustained throughput, critical for both training and inference of memory-bound models.

Large-Scale Model Performance

For LLMs with massive embeddings and activations, increased bandwidth on B200 translates directly to faster forward and backward passes, especially when working with lower-precision data formats that pack more values per byte.

Sparsity Support

H100: Introduced structured sparsity (2:4 ratio) to double effective throughput for compatible workloads.

B200: Enhances sparsity support across FP8 and FP4 formats, offering up to 4.5 PFLOPS dense FP8 and 9 PFLOPS sparse FP8, plus 9 PFLOPS dense FP4 and 18 PFLOPS sparse FP4 formats that extend beyond what the H100 supports.

Workload Benefits

Sparsity accelerates inference for transformer models by exploiting zero weights or activations, reducing compute and memory overhead. B200's expanded sparsity formats further boost performance for next-gen inference pipelines.

MIG Capability

Both GPUs support NVIDIA's Multi-Instance GPU (MIG) technology, partitioning a single physical GPU into up to seven fully isolated instances—each with dedicated memory, cache, and compute cores.

MIG flexibility is invaluable in cloud and shared environments, enabling granular resource allocation and guaranteed quality of service.

Performance Benchmark

MLPerf Training & Inference

H100 Achievements:

• Set world records across MLPerf Training v3.0 and Inference v3.1, delivering up to 4.5× more inference performance than A100.
• Achieved 0.82 minutes to train 3D U-Net on 432 GPUs, improving per-accelerator performance by 8.2% over previous submissions.

B200 Gains:

• In MLPerf Training submissions, B200-based systems delivered up to 2.2× the training performance of H100 systems, including 2.27× higher peak throughput across FP8, FP16/BF16, and TF32.
• In MLPerf Inference tests, B200 achieved up to 4× inference uplift over Hopper, thanks to FP4/FP8 sparsity and doubled memory bandwidth.

These real-world benchmarks underscore B200's ability to accelerate both training and inference workloads well beyond H100's capabilities.

Which GPU Is Right for You?

Below is a recommendation matrix for common AI and HPC use cases:

Summary

NVIDIA's H100 ushered in the Hopper era, delivering record-setting performance for AI training and inference. Yet, as models scale and inference demands intensify, the Blackwell-based B200 raises the bar even higher, offering:

• Twice the memory (192 GB vs. 80 GB) and 2.4× the bandwidth (8 TB/s vs. 3.35 TB/s)
• 2.2× training and 4× inference performance over H100 in MLPerf benchmarks
• FP4/FP6 support for ultra-low precision inference
• 3.5× FP64 vector performance for HPC
• Fifth-generation NVLink, 208 B transistors, and MIG for maximum flexibility

For developers and enterprises aiming to train the largest models, serve massive inference workloads, or build future-proof AI infrastructure, the B200 Blackwell GPU stands out as the clear choice. Ready to harness next-generation performance? Explore the B200 Blackwell GPU on Civo for seamless deployment, or browse the full Civo AI GPU range to find the perfect fit for your needs.