The AI landscape continues to evolve at breakneck speed, demanding increasingly powerful hardware to support massive language models, complex simulations, and real-time inference workloads. NVIDIA has consistently led this charge, delivering GPUs that push the boundaries of what's computationally possible.
The NVIDIA H100, launched in 2022 with the Hopper architecture, revolutionized AI training and inference with its fourth-generation Tensor Cores, Transformer Engine, and substantial memory bandwidth improvements. It quickly became the gold standard for enterprise AI workloads, powering everything from large language model training to high-performance computing applications.
In 2024, NVIDIA unveiled the B200, built on the groundbreaking Blackwell architecture. This next-generation GPU promises unprecedented performance gains—up to 2.5× faster training and 15× better inference performance compared to the H100—while introducing revolutionary features like dual-chip design, FP4 precision support, and massive memory capacity increases.
This comprehensive comparison explores the architectural evolution from Hopper to Blackwell, analyzing core specifications, performance benchmarks, and real-world applications, and also compares both GPUs running the GPT-OSS-120B model to help you determine which best suits your AI infrastructure needs.
The transition from NVIDIA's Hopper to Blackwell architectures represents one of the most significant generational leaps in GPU design, driven by the explosive growth in AI model complexity and the need for more efficient inference at scale.
Released in 2022, the H100 was purpose-built for the transformer era of AI. Built on a 5nm process with 80 billion transistors, the Hopper architecture introduced several breakthrough technologies that defined modern AI computing.
The H100's fourth-generation Tensor Cores brought native support for the Transformer Engine with FP8 precision, enabling faster training and inference for transformer-based models without accuracy loss. This was crucial as large language models began scaling beyond 100 billion parameters.
Key innovations included second-generation Multi-Instance GPU (MIG) technology, tripling compute capacity per instance compared to the A100, and fourth-generation NVLink providing 900 GB/s of GPU-to-GPU bandwidth. The H100 also introduced Confidential Computing capabilities, enabling secure processing of sensitive data in multi-tenant environments.
With 16,896 CUDA cores, 528 Tensor Cores, and up to 80GB of HBM3 memory delivering 3.35 TB/s of bandwidth, the H100 established new performance standards for AI workloads while maintaining compatibility with existing software ecosystems.
Launched in 2024, the B200 represents NVIDIA's most ambitious architectural redesign to date. Built on an advanced process node, the Blackwell architecture packs 208 billion transistors—2.6× more than the H100—in a revolutionary dual-chip design that functions as a single, unified GPU.
The B200 introduces fifth-generation Tensor Cores with native FP4 precision support alongside enhanced FP8 and FP6 capabilities. The second-generation Transformer Engine has been optimized specifically for mixture-of-experts (MoE) models and extremely long-context applications, addressing the growing demands of next-generation AI systems.
Blackwell's dual-chip design connects two GPU dies with an ultra-high-bandwidth, low-latency interconnect that appears as a single device to software. This approach allows NVIDIA to deliver massive performance scaling while maintaining software compatibility and programmability.
The architecture also features dramatically improved inference engines, specialized decompression units for handling compressed model formats, and enhanced security features for enterprise deployments. Memory capacity scales to 192GB of HBM3e with 8 TB/s of bandwidth—more than double the H100's capabilities.
| Feature | NVIDIA H100 (Hopper) | NVIDIA B200 (Blackwell) |
|---|---|---|
| Architecture Name | Hopper | Blackwell |
| Release Year | 2022 | 2024 |
| Transistor Count | 80 billion | 208 billion |
| Die Design | Single chip | Dual-chip unified |
| Tensor Cores Generation | 4th Generation | 5th Generation |
| Transformer Engine | 1st Generation (FP8) | 2nd Generation (FP4/FP6/FP8) |
| MoE Optimization | Limited | Native support |
| Decompression Units | No | Yes |
| Process Node | 5nm | Advanced node |
| Max Memory | 96GB HBM3 | 192GB HBM3e |
The specifications comparison between the H100 and B200 reveals the substantial improvements Blackwell brings across every major subsystem, from compute cores to memory architecture.
The H100 utilizes NVIDIA's mature Hopper architecture on a 5nm process node, packing 80 billion transistors in a proven, single-die design. The B200 takes a bold architectural leap with its dual-chip Blackwell design, integrating 208 billion transistors across two dies connected by an ultra-high-bandwidth interconnect that appears as a single GPU to applications.
This dual-chip approach allows NVIDIA to effectively double the silicon area while maintaining high yields and thermal efficiency. The result is significantly more compute resources and memory capacity within the same form factor constraints.
The H100 ships with 80GB of HBM3 memory in standard configurations, with select models offering 96GB. Memory bandwidth reaches 3.35 TB/s, which was groundbreaking at launch and remains competitive for most current workloads.
The B200 dramatically expands memory capacity to 192GB of HBM3e—2.4× more than the H100's standard configuration. More importantly, memory bandwidth jumps to 8 TB/s, providing 2.4× the data throughput. This massive bandwidth increase is crucial for handling the largest language models and enabling efficient inference with long context lengths.
The increased memory capacity allows the B200 to handle models with up to 200+ billion parameters natively without model sharding, while the higher bandwidth reduces memory bottlenecks that can limit utilization in inference workloads.
Both GPUs feature advanced NVLink technology, but with significant generational improvements. The H100's fourth-generation NVLink provides 900 GB/s of GPU-to-GPU bandwidth, enabling efficient multi-GPU scaling for training large models.
The B200 advances to fifth-generation NVLink, though specific bandwidth figures vary by configuration. More importantly, Blackwell introduces new interconnect topologies optimized for inference scaling, enabling more efficient deployment of models across multiple GPUs with reduced latency overhead.
The H100 features 16,896 CUDA cores and 528 fourth-generation Tensor Cores, along with a 50MB L2 cache. This configuration provides excellent balance for both training and inference workloads across a wide range of model sizes.
The B200's dual-chip design effectively doubles many compute resources, though exact core counts vary by configuration. The fifth-generation Tensor Cores introduce support for new data types including FP4, enabling higher throughput for inference workloads where maximum precision isn't required.
The B200 also integrates specialized decompression engines that can handle compressed model formats on-the-fly, reducing memory bandwidth requirements and enabling larger effective model capacity.
The H100 operates at 700W TDP, representing a significant but manageable power requirement for most data center deployments. Its performance-per-watt represented a major improvement over previous generations.
The B200 increases power consumption to 1000W TDP, reflecting the dual-chip design and increased compute density. However, the performance gains far exceed the power increase, resulting in better overall efficiency for most AI workloads. The higher power requirement does necessitate enhanced cooling solutions and power infrastructure planning.
Both GPUs are available in multiple form factors. The H100 comes in PCIe and SXM configurations, with SXM variants providing higher performance and better scaling characteristics.
The B200 maintains similar form factor options, with particular emphasis on liquid-cooled configurations to handle the increased thermal output. NVIDIA has designed compatibility layers to ease migration from H100-based systems, though the increased power requirements may necessitate infrastructure upgrades.
Our research team performed detailed benchmarks of the GPT-OSS-120B model across multiple inference frameworks including vLLM, SGLang, and TensorRT-LLM on both NVIDIA B200 and H100 GPUs. The tests simulated real-world deployment scenarios with concurrency levels ranging from single-request queries to high-throughput production workloads. Results indicate that in several configurations a single B200 GPU delivers higher performance than two H100 GPUs, showing a significant increase in efficiency per GPU.
Model: GPT-OSS-120B
Input tokens: 1000
Output tokens: 1000
Generation method: Stream output tokens
Hardware Comparison: 2× H100 GPUs vs 1× B200 GPU
Frameworks tested: vLLM, SGLang, TensorRT-LLM
Concurrency levels: 1, 10, 50, 100 requests
For individual requests, the time-to-first-token (TTFT) and per-token latency reveal differences between GPU architectures and framework implementations. Across these measurements, B200 running TensorRT-LLM achieves the fastest initial response at 0.023 seconds, while per-token latency remains comparable across most configurations, ranging from 0.004 to 0.005 seconds.
| Configuration | TTFT (s) | Per-Token Latency (s) |
|---|---|---|
| B200 + TRT-LLM | 0.023 | 0.005 |
| B200 + SGLang | 0.093 | 0.004 |
| 2× H100 + vLLM | 0.053 | 0.005 |
| 2× H100 + SGLang | 0.125 | 0.004 |
| 2× H100 + TRT-LLM | 0.177 | 0.004 |
When handling 10 concurrent requests, the performance differences between GPU configurations and frameworks become more pronounced. B200 running TensorRT-LLM maintains the lowest time-to-first-token at 0.072 seconds while keeping per-token latency competitive at 0.004 seconds. In contrast, the H100 configurations show higher TTFT values, ranging from 1.155 to 2.496 seconds, and slightly higher per-token latencies, indicating that B200 delivers faster initial responses and efficient token processing under moderate concurrency.
| Configuration | TTFT (s) | Per-Token Latency (s) |
|---|---|---|
| B200 + TRT-LLM | 0.072 | 0.004 |
| B200 + SGLang | 0.776 | 0.008 |
| 2× H100 + vLLM | 1.91 | 0.011 |
| 2× H100 + SGLang | 1.155 | 0.010 |
| 2× H100 + TRT-LLM | 2.496 | 0.009 |
At 50 concurrent requests, differences in GPU and framework performance become more evident. B200 running TensorRT-LLM delivers the fastest time-to-first-token at 0.080 seconds, maintains the lowest per-token latency at 0.009 seconds, and achieves the highest overall throughput at 4,360 tokens per second. Other configurations, including dual H100 setups, show higher TTFT and lower throughput, indicating that B200 sustains both responsiveness and processing efficiency under high concurrency.
| Configuration | Latency per token (s) | TTFT (s) | Overall Throughput (tokens/sec) |
|---|---|---|---|
| B200 + TRT-LLM | 0.009 | 0.080 | 4,360 |
| B200 + SGLang | 0.010 | 1.667 | 4,075 |
| 2× H100 + SGLang | 0.015 | 3.08 | 3,109 |
| 2× H100 + TRT-LLM | 0.018 | 4.14 | 2,163 |
| 2× H100 + vLLM | 0.021 | 7.546 | 2,212 |
Under maximum concurrency with 100 simultaneous requests, performance differences become even more pronounced. B200 running TensorRT-LLM maintains the fastest time-to-first-token at 0.234 seconds and achieves the highest overall throughput at 7,236 tokens per second. In comparison, the dual H100 configurations show higher TTFT and lower throughput, indicating that a single B200 can sustain higher performance while using fewer GPUs, demonstrating its efficiency in large-scale inference workloads.
| Configuration | TTFT (s) | Overall Throughput (tokens/sec) |
|---|---|---|
| B200 + TRT-LLM | 0.234 | 7,236 |
| B200 + SGLang | 2.584 | 6,303 |
| 2× H100 + vLLM | 1.87 | 4,741 |
| 2× H100 + SGLang | 8.991 | 4,493 |
| 2× H100 + TRT-LLM | 5.467 | 1,943 |
vLLM: Balanced performance on H100, limited availability on B200 in our tests.
SGLang: Consistent performance across hardware; B200 scales efficiently with concurrency.
TensorRT-LLM: Significant performance gains on B200, especially for TTFT and throughput.
Performance efficiency: The NVIDIA B200 GPU delivers roughly 2.2 times the training performance and up to 4 times the inference performance of a single H100 according to MLPerf benchmarks. In some real-world workloads, it has been reported to achieve up to 3 times faster training and as much as 15 times faster inference. In our testing with GPT-OSS-120B, a single B200 GPU can replace two H100 GPUs for equivalent or higher performance in most scenarios, reducing total GPU requirements, power consumption, and infrastructure complexity.
Cost considerations: Using fewer GPUs lowers procurement and operational costs, including power, cooling, and maintenance, while supporting higher performance density per rack or server.
Recommended use cases for B200: Suitable for production inference where latency and throughput are critical, interactive applications requiring sub-100ms time-to-first-token, and high-throughput services that demand maximum tokens per second per GPU.
Situations where H100 may still be relevant: When there are existing H100 investments or software dependencies, or if B200 availability is limited.
The choice between the H100 and B200 depends on your workload requirements, infrastructure readiness, and budget.
The H100 is ideal for established AI pipelines and workloads up to 70–100B parameters, offering mature software support, broad ecosystem compatibility, and lower power requirements (700W). It’s a proven, reliable option for many deployments.
The B200 pushes AI acceleration to the next level with massive memory capacity, breakthrough FP4 inference performance, and the ability to serve extreme context lengths and the largest models. It delivers meaningful training gains over the H100 but truly shines in inference, with 10–15× performance boosts that can redefine AI economics. Its 1000W power draw demands infrastructure upgrades but yields unmatched performance for next-gen AI applications.
For developers and enterprises focused on training large models, handling high-volume inference, or building scalable AI infrastructure, the B200 Blackwell GPU offers significant performance advantages. Users can evaluate the B200 or H100 on Clarifai for deployment, or explore the full range of Clarifai AI GPU range to identify the configuration that best meets their requirements.
