Selecting The Proper GPU For Your AI Workloads

Introduction

The AI panorama continues to evolve at breakneck pace, demanding more and more highly effective {hardware} to help huge 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 potential.

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 grew to become the gold commonplace for enterprise AI workloads, powering the whole lot from giant language mannequin coaching to high-performance computing functions.

In 2024, NVIDIA unveiled the B200, constructed on the groundbreaking Blackwell structure. This next-generation GPU guarantees unprecedented efficiency features—as much as 2.5× sooner coaching and 15× higher inference efficiency in comparison with the H100—whereas introducing revolutionary options like dual-chip design, FP4 precision help, and big reminiscence capability will increase.

This complete comparability explores the architectural evolution from Hopper to Blackwell, analyzing core specs, efficiency benchmarks, and real-world functions, 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 vital vital generational leaps in GPU design, pushed by the explosive development 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 help for the Transformer Engine with FP8 precision, enabling sooner 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 knowledge 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 up to now. Constructed on a complicated course of node, the Blackwell structure packs 208 billion transistors—2.6× greater than the H100—in a revolutionary dual-chip design that features as a single, unified GPU.

The B200 introduces fifth-generation Tensor Cores with native FP4 precision help 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 functions, addressing the rising calls for of next-generation AI programs.

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 huge efficiency scaling whereas sustaining software program compatibility and programmability.

The structure additionally options dramatically improved inference engines, specialised decompression models 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)

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 linked by an ultra-high-bandwidth interconnect that seems as a single GPU to functions.

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 sources and reminiscence capability inside the identical kind issue constraints.

GPU Reminiscence and Bandwidth

The H100 ships with 80GB of HBM3 reminiscence in commonplace 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 commonplace configuration. Extra importantly, reminiscence bandwidth jumps to eight TB/s, offering 2.4× the info throughput. This huge 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 Expertise

Each GPUs function superior NVLink expertise, however with vital generational enhancements. The H100’s fourth-generation NVLink gives 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 decreased 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 gives wonderful steadiness for each coaching and inference workloads throughout a variety of mannequin sizes.

The B200’s dual-chip design successfully doubles many compute sources, although actual core counts fluctuate by configuration. The fifth-generation Tensor Cores introduce help for brand spanking new knowledge varieties together with FP4, enabling increased 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 knowledge heart deployments. Its performance-per-watt represented a serious enchancment over earlier generations.

The B200 will increase energy consumption to 1000W TDP, reflecting the dual-chip design and elevated compute density. Nonetheless, the efficiency features far exceed the facility enhance, leading to higher total effectivity for many AI workloads. The upper energy requirement does necessitate enhanced cooling options and energy infrastructure planning.

Kind Elements and Compatibility

Each GPUs can be found in a number of kind elements. The H100 is available in PCIe and SXM configurations, with SXM variants offering increased efficiency and higher scaling traits.

The B200 maintains comparable kind issue choices, with explicit emphasis on liquid-cooled configurations to deal with the elevated thermal output. NVIDIA has designed compatibility layers to ease migration from H100-based programs, although the elevated energy necessities might 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 increased efficiency than two H100 GPUs, displaying a major enhance in effectivity per GPU.

Take a look at 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.

Average Load (Concurrency = 10)

When dealing with 10 concurrent requests, the efficiency variations between GPU configurations and frameworks change into extra pronounced. B200 working TensorRT-LLM maintains the bottom time-to-first-token at 0.072 seconds whereas retaining per-token latency aggressive at 0.004 seconds. In distinction, the H100 configurations present increased TTFT values, starting from 1.155 to 2.496 seconds, and barely increased per-token latencies, indicating that B200 delivers sooner preliminary responses and environment friendly token processing beneath average concurrency.

Excessive Concurrency (Concurrency = 50)

At 50 concurrent requests, variations in GPU and framework efficiency change into 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 total throughput at 4,360 tokens per second. Different configurations, together with twin H100 setups, present increased TTFT and decrease throughput, indicating that B200 sustains each responsiveness and processing effectivity beneath excessive concurrency.

Most Load (Concurrency = 100)

Beneath most concurrency with 100 simultaneous requests, efficiency variations change into much more pronounced. B200 working TensorRT-LLM maintains the quickest time-to-first-token at 0.234 seconds and achieves the very best total throughput at 7,236 tokens per second. As compared, the twin H100 configurations present increased TTFT and decrease throughput, indicating {that a} single B200 can maintain increased efficiency whereas utilizing fewer GPUs, demonstrating its effectivity in large-scale inference workloads.

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: Important efficiency features 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 sooner coaching and as a lot as 15 instances sooner inference. In our testing with GPT-OSS-120B, a single B200 GPU can substitute two H100 GPUs for equal or increased efficiency in most situations, decreasing complete GPU necessities, energy consumption, and infrastructure complexity.

• Price concerns: Utilizing fewer GPUs lowers procurement and operational prices, together with energy, cooling, and upkeep, whereas supporting increased efficiency density per rack or server.

• Really helpful use instances for B200: Appropriate for manufacturing inference the place latency and throughput are important, interactive functions requiring sub-100ms time-to-first-token, and high-throughput companies that demand most tokens per second per GPU.

• Conditions the place H100 should still 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 is determined by your workload necessities, infrastructure readiness, and finances.

The H100 is right for established AI pipelines and workloads as much as 70–100B parameters, providing mature software program help, 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 degree with huge reminiscence capability, breakthrough FP4 inference efficiency, and the flexibility to serve excessive context lengths and the most important fashions. It delivers significant coaching features over the H100 however actually 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 functions.

For builders and enterprises centered on coaching giant fashions, dealing with high-volume inference, or constructing scalable AI infrastructure, the B200 Blackwell GPU affords vital efficiency benefits. Customers can consider the B200 or H100 on Clarifai for deployment, or discover the total vary of Clarifai AI GPU vary to determine the configuration that greatest meets their necessities.