Introduction
The AI panorama continues to evolve at breakneck velocity, demanding more and more highly effective {hardware} to assist large language fashions, advanced simulations, and real-time inference workloads. NVIDIA has persistently led this cost, delivering GPUs that push the boundaries of what is computationally attainable.
The NVIDIA H100, launched in 2022 with the Hopper structure, revolutionized AI coaching and inference with its fourth-generation Tensor Cores, Transformer Engine, and substantial reminiscence bandwidth enhancements. It shortly turned the gold normal for enterprise AI workloads, powering all the things from giant language mannequin coaching to high-performance computing purposes.
In 2024, NVIDIA unveiled the B200, constructed on the groundbreaking Blackwell structure. This next-generation GPU guarantees unprecedented efficiency beneficial properties—as much as 2.5× quicker coaching and 15× higher inference efficiency in comparison with the H100—whereas introducing revolutionary options like dual-chip design, FP4 precision assist, and large reminiscence capability will increase.
This complete comparability explores the architectural evolution from Hopper to Blackwell, analyzing core specs, efficiency benchmarks, and real-world purposes, and likewise compares each GPUs working the GPT-OSS-120B mannequin that will help you decide which most accurately fits your AI infrastructure wants.
Architectural Evolution: Hopper to Blackwell
The transition from NVIDIA’s Hopper to Blackwell architectures represents one of the crucial vital generational leaps in GPU design, pushed by the explosive progress in AI mannequin complexity and the necessity for extra environment friendly inference at scale.
NVIDIA H100 (Hopper Structure)
Launched in 2022, the H100 was purpose-built for the transformer period of AI. Constructed on a 5nm course of with 80 billion transistors, the Hopper structure launched a number of breakthrough applied sciences that outlined fashionable AI computing.
The H100’s fourth-generation Tensor Cores introduced native assist for the Transformer Engine with FP8 precision, enabling quicker coaching and inference for transformer-based fashions with out accuracy loss. This was essential as giant language fashions started scaling past 100 billion parameters.
Key improvements included second-generation Multi-Occasion GPU (MIG) expertise, tripling compute capability per occasion in comparison with the A100, and fourth-generation NVLink offering 900 GB/s of GPU-to-GPU bandwidth. The H100 additionally launched Confidential Computing capabilities, enabling safe processing of delicate information in multi-tenant environments.
With 16,896 CUDA cores, 528 Tensor Cores, and as much as 80GB of HBM3 reminiscence delivering 3.35 TB/s of bandwidth, the H100 established new efficiency requirements for AI workloads whereas sustaining compatibility with current software program ecosystems.
NVIDIA B200 (Blackwell Structure)
Launched in 2024, the B200 represents NVIDIA’s most bold architectural redesign so far. Constructed on a sophisticated course of node, the Blackwell structure packs 208 billion transistors—2.6× greater than the H100—in a revolutionary dual-chip design that capabilities as a single, unified GPU.
The B200 introduces fifth-generation Tensor Cores with native FP4 precision assist alongside enhanced FP8 and FP6 capabilities. The second-generation Transformer Engine has been optimized particularly for mixture-of-experts (MoE) fashions and very long-context purposes, addressing the rising calls for of next-generation AI techniques.
Blackwell’s dual-chip design connects two GPU dies with an ultra-high-bandwidth, low-latency interconnect that seems as a single machine to software program. This strategy permits NVIDIA to ship large efficiency scaling whereas sustaining software program compatibility and programmability.
The structure additionally options dramatically improved inference engines, specialised decompression items for dealing with compressed mannequin codecs, and enhanced safety features for enterprise deployments. Reminiscence capability scales to 192GB of HBM3e with 8 TB/s of bandwidth—greater than double the H100’s capabilities.
Architectural Variations (H100 vs. B200)
| Characteristic | NVIDIA H100 (Hopper) | NVIDIA B200 (Blackwell) |
|---|---|---|
| Structure Title | Hopper | Blackwell |
| Launch 12 months | 2022 | 2024 |
| Transistor Rely | 80 billion | 208 billion |
| Die Design | Single chip | Twin-chip unified |
| Tensor Cores Era | 4th Era | fifth Era |
| Transformer Engine | 1st Era (FP8) | 2nd Era (FP4/FP6/FP8) |
| MoE Optimization | Restricted | Native assist |
| Decompression Items | No | Sure |
| Course of Node | 5nm | Superior node |
| Max Reminiscence | 96GB HBM3 | 192GB HBM3e |
Core Specs: A Detailed Comparability
The specs comparability between the H100 and B200 reveals the substantial enhancements Blackwell brings throughout each main subsystem, from compute cores to reminiscence structure.
GPU Structure and Course of
The H100 makes use of NVIDIA’s mature Hopper structure on a 5nm course of node, packing 80 billion transistors in a confirmed, single-die design. The B200 takes a daring architectural leap with its dual-chip Blackwell design, integrating 208 billion transistors throughout two dies related by an ultra-high-bandwidth interconnect that seems as a single GPU to purposes.
This dual-chip strategy permits NVIDIA to successfully double the silicon space whereas sustaining excessive yields and thermal effectivity. The result’s considerably extra compute assets and reminiscence capability inside the similar kind issue constraints.
GPU Reminiscence and Bandwidth
The H100 ships with 80GB of HBM3 reminiscence in normal configurations, with choose fashions providing 96GB. Reminiscence bandwidth reaches 3.35 TB/s, which was groundbreaking at launch and stays aggressive for many present workloads.
The B200 dramatically expands reminiscence capability to 192GB of HBM3e—2.4× greater than the H100’s normal configuration. Extra importantly, reminiscence bandwidth jumps to eight TB/s, offering 2.4× the info throughput. This large bandwidth enhance is essential for dealing with the most important language fashions and enabling environment friendly inference with lengthy context lengths.
The elevated reminiscence capability permits the B200 to deal with fashions with as much as 200+ billion parameters natively with out mannequin sharding, whereas the upper bandwidth reduces reminiscence bottlenecks that may restrict utilization in inference workloads.
Interconnect Know-how
Each GPUs function superior NVLink expertise, however with vital generational enhancements. The H100’s fourth-generation NVLink offers 900 GB/s of GPU-to-GPU bandwidth, enabling environment friendly multi-GPU scaling for coaching giant fashions.
The B200 advances to fifth-generation NVLink, although particular bandwidth figures fluctuate by configuration. Extra importantly, Blackwell introduces new interconnect topologies optimized for inference scaling, enabling extra environment friendly deployment of fashions throughout a number of GPUs with lowered latency overhead.
Compute Items
The H100 options 16,896 CUDA cores and 528 fourth-generation Tensor Cores, together with a 50MB L2 cache. This configuration offers wonderful steadiness for each coaching and inference workloads throughout a variety of mannequin sizes.
The B200’s dual-chip design successfully doubles many compute assets, although precise core counts fluctuate by configuration. The fifth-generation Tensor Cores introduce assist for brand spanking new information sorts together with FP4, enabling greater throughput for inference workloads the place most precision is not required.
The B200 additionally integrates specialised decompression engines that may deal with compressed mannequin codecs on-the-fly, decreasing reminiscence bandwidth necessities and enabling bigger efficient mannequin capability.
Energy Consumption (TDP)
The H100 operates at 700W TDP, representing a major however manageable energy requirement for many information heart deployments. Its performance-per-watt represented a significant enchancment over earlier generations.
The B200 will increase energy consumption to 1000W TDP, reflecting the dual-chip design and elevated compute density. Nevertheless, the efficiency beneficial properties far exceed the facility enhance, leading to higher general effectivity for many AI workloads. The upper energy requirement does necessitate enhanced cooling options and energy infrastructure planning.
Kind Components and Compatibility
Each GPUs can be found in a number of kind components. The H100 is available in PCIe and SXM configurations, with SXM variants offering greater efficiency and higher scaling traits.
The B200 maintains related kind issue choices, with specific emphasis on liquid-cooled configurations to deal with the elevated thermal output. NVIDIA has designed compatibility layers to ease migration from H100-based techniques, although the elevated energy necessities could necessitate infrastructure upgrades.
Efficiency Benchmarks: GPT-OSS-120B Inference Evaluation on H100 and B200
Complete Comparability Throughout SGLang, vLLM, and TensorRT-LLM Frameworks
Our analysis workforce carried out detailed benchmarks of the GPT-OSS-120B mannequin throughout a number of inference frameworks together with vLLM, SGLang, and TensorRT-LLM on each NVIDIA B200 and H100 GPUs. The checks simulated real-world deployment situations with concurrency ranges starting from single-request queries to high-throughput manufacturing workloads. Outcomes point out that in a number of configurations a single B200 GPU delivers greater efficiency than two H100 GPUs, displaying a major enhance in effectivity per GPU.
Check Configuration
Mannequin: GPT-OSS-120B
Enter tokens: 1000
Output tokens: 1000
Era technique: Stream output tokens
{Hardware} Comparability: 2× H100 GPUs vs 1× B200 GPU
Frameworks examined: vLLM, SGLang, TensorRT-LLM
Concurrency ranges: 1, 10, 50, 100 requests
Single Request Efficiency (Concurrency = 1)
For particular person requests, the time-to-first-token (TTFT) and per-token latency reveal variations between GPU architectures and framework implementations. Throughout these measurements, B200 working TensorRT-LLM achieves the quickest preliminary response at 0.023 seconds, whereas per-token latency stays comparable throughout most configurations, starting 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 |
Reasonable Load (Concurrency = 10)
When dealing with 10 concurrent requests, the efficiency variations between GPU configurations and frameworks turn out to be extra pronounced. B200 working TensorRT-LLM maintains the bottom time-to-first-token at 0.072 seconds whereas protecting per-token latency aggressive at 0.004 seconds. In distinction, the H100 configurations present greater TTFT values, starting from 1.155 to 2.496 seconds, and barely greater per-token latencies, indicating that B200 delivers quicker preliminary responses and environment friendly token processing beneath reasonable 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 |
Excessive Concurrency (Concurrency = 50)
At 50 concurrent requests, variations in GPU and framework efficiency turn out to be extra evident. B200 working TensorRT-LLM delivers the quickest time-to-first-token at 0.080 seconds, maintains the bottom per-token latency at 0.009 seconds, and achieves the very best general throughput at 4,360 tokens per second. Different configurations, together with twin H100 setups, present greater TTFT and decrease throughput, indicating that B200 sustains each responsiveness and processing effectivity beneath excessive concurrency.
| Configuration | Latency per token (s) | TTFT (s) | General 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 |
Most Load (Concurrency = 100)
Below most concurrency with 100 simultaneous requests, efficiency variations turn out to be much more pronounced. B200 working TensorRT-LLM maintains the quickest time-to-first-token at 0.234 seconds and achieves the very best general throughput at 7,236 tokens per second. As compared, the twin H100 configurations present greater TTFT and decrease throughput, indicating {that a} single B200 can maintain greater efficiency whereas utilizing fewer GPUs, demonstrating its effectivity in large-scale inference workloads.
| Configuration | TTFT (s) | General 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 |
Framework Optimization
vLLM: Balanced efficiency on H100, restricted availability on B200 in our checks.
SGLang: Constant efficiency throughout {hardware}; B200 scales effectively with concurrency.
TensorRT-LLM: Vital efficiency beneficial properties on B200, particularly for TTFT and throughput.
Deployment Insights
Efficiency effectivity: The NVIDIA B200 GPU delivers roughly 2.2 instances the coaching efficiency and as much as 4 instances the inference efficiency of a single H100 in line with MLPerf benchmarks. In some real-world workloads, it has been reported to attain as much as 3 instances quicker coaching and as a lot as 15 instances quicker inference. In our testing with GPT-OSS-120B, a single B200 GPU can substitute two H100 GPUs for equal or greater efficiency in most situations, decreasing complete GPU necessities, energy consumption, and infrastructure complexity.
Value issues: Utilizing fewer GPUs lowers procurement and operational prices, together with energy, cooling, and upkeep, whereas supporting greater efficiency density per rack or server.
Really helpful use circumstances for B200: Appropriate for manufacturing inference the place latency and throughput are important, interactive purposes requiring sub-100ms time-to-first-token, and high-throughput companies that demand most tokens per second per GPU.
Conditions the place H100 should be related: When there are current H100 investments or software program dependencies, or if B200 availability is restricted.
Conclusion
The selection between the H100 and B200 relies on your workload necessities, infrastructure readiness, and funds.
The H100 is good for established AI pipelines and workloads as much as 70–100B parameters, providing mature software program assist, broad ecosystem compatibility, and decrease energy necessities (700W). It’s a confirmed, dependable possibility for a lot of deployments.
The B200 pushes AI acceleration to the following stage with large reminiscence capability, breakthrough FP4 inference efficiency, and the power to serve excessive context lengths and the most important fashions. It delivers significant coaching beneficial properties over the H100 however really shines in inference, with 10–15× efficiency boosts that may redefine AI economics. Its 1000W energy draw calls for infrastructure upgrades however yields unmatched efficiency for next-gen AI purposes.
For builders and enterprises centered on coaching giant fashions, dealing with high-volume inference, or constructing scalable AI infrastructure, the B200 Blackwell GPU provides vital efficiency benefits. Customers can consider the B200 or H100 on Clarifai for deployment, or discover the complete vary of Clarifai AI GPU vary to establish the configuration that greatest meets their necessities.