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›Blog›NVIDIA B300 vs H200: GPU Specs & Performance Analysis

NVIDIA B300 vs H200:<br>GPU Specs & Performance Analysis

Inside NVIDIA’s B300: 288GB Memory, 14 PFLOPS of FP4 Compute, and the Liquid-Cooled Infrastructure Trade-Offs Ahead

By Marketing/June 17, 2026<br>By Marketing<br>June 17, 2026

›Blog›NVIDIA B300 vs H200: GPU Specs & Performance Analysis

As large language models (LLMs) rapidly gain traction across industries, GPU selection has become one of the most critical technical decisions for AI enterprises. Scheduled for official release in early 2026, the NVIDIA B300 (Blackwell Ultra) — featuring 288GB of HBM3e memory and exceptional inference performance — is emerging as a top choice for enterprises deploying large-scale models. This article provides a comprehensive breakdown of the B300\'s technical specifications and performance improvements over its predecessors.

I. What Revolutionary Improvements Does B300 Bring?<br>The NVIDIA B300, built on the Blackwell Ultra architecture and officially shipping in January 2026, stands as the most powerful single-GPU computing platform NVIDIA has ever released. Compared to the previous-generation Hopper architecture, B300 delivers a qualitative leap across multiple key metrics.<br>From an architectural standpoint, Blackwell Ultra is far more than a simple process node upgrade — it is a deep optimization by NVIDIA specifically for large language model (LLM) inference. With 14 petaFLOPS of sparse FP4 compute, 288GB of HBM3e memory , and 8 TB/s of memory bandwidth , these figures translate into the ability for a single GPU to host models with vastly larger parameter counts while driving significantly higher inference throughput.<br>For AI enterprises evaluating GPU options, the arrival of B300 brings several critical changes:<br>• Larger models on a single card : 288GB of memory means a single B300 can load a 70B-parameter model (at FP16 precision) while leaving over 100GB available for KV Cache.<br>• Significantly reduced inference costs : Compared to the H100, B300 achieves an 11–15× increase in inference throughput.<br>• Support for longer contexts : The expanded memory capacity allows the full KV Cache of long-text sequences to be retained, avoiding performance degradation due to memory constraints.

II. What Are the Specifications of the NVIDIA B300 GPU?<br>B300's Core Compute Capabilities

GPUArchitectureFP8 (Dense) ComputeMemoryMemory BandwidthNVLinkB300Blackwell Ultra7000 TFLOPS288GB HBM3e8 TB/s1.8 TB/sB200Blackwell4500 TFLOPS192GB HBM3e8 TB/s1.8 TB/sH200Hopper756 TFLOPS141GB HBM3e4.8 TB/s900 GB/sH100Hopper756 TFLOPS80GB HBM3e3.35 TB/s900 GB/s

According to NVIDIA's official technical documentation, the B300 offers 2× the memory capacity of the H200 and 3.6× that of the H100 ; the B200 delivers roughly 6× the FP8 inference performance compared to the H200. This massive generational leap is primarily driven by the dual optimization of compute density and memory subsystems in the Blackwell architecture.<br>B300 Power Consumption and Cooling

For enterprises considering purchasing B300 GPUs to build on-premises data centers, a critical factor to note is the B300's TDP (Thermal Design Power) of 1,400W , which mandates direct liquid cooling (DLC) for production deployment. Compared to the air-cooled solutions of the H200 and H100, this adds infrastructure complexity, but for enterprise deployments pursuing peak performance, it is a necessary reality.<br>An 8-GPU DGX B300 system has a peak power draw of approximately 14kW — equivalent to the consumption of two H100 DGX systems. Enterprises must fully account for power and cooling capacity when planning their facilities. Therefore, rather than purchasing hardware outright, many businesses may prefer to access B300 GPUs through cloud services, delegating power and thermal challenges to the cloud provider and saving significant operational overhead.<br>Conclusion

• The NVIDIA B300 GPU features 288GB of HBM3e memory.<br>• It delivers up to 7,000 TFLOPS of FP8 compute performance.<br>• Memory capacity is increased by 2× compared to the H200.<br>• Memory capacity is increased by 3.6× compared to the H100.

III. Blackwell Ultra vs. Hopper: Generational Performance Comparison<br>MetricB300 vs. H200Prefill Throughput (ISL=2k)8×Short Output Throughput (ISL=2k, OSL=128)20×

These figures indicate that, for typical online inference scenarios, the B300 delivers significantly higher concurrency than the H200. Under the same service level agreement (SLA), enterprises can handle equivalent traffic volumes with far fewer GPU resources, thereby substantially reducing inference costs.

IV. B300 Use Cases and Challenges<br>Optimal Application Scenarios

The B300 is particularly well-suited for the following scenarios:<br>1. Large-scale inference services: Online inference for 70B+ parameter models, with single-GPU throughput reaching 100,000+ tokens/s .<br>2....