Comprehensive Comparison of NVIDIA GPUs: A100, A800, H100, H200, H800, B100, B200, and L40S

The article introduces a series of NVIDIA GPU products, explaining that they represent the cutting edge of AI and high‑performance computing (HPC) technology, and lists the various downloadable PDF/PPT resources for deeper study.

A100 vs A800: A100, launched in 2020, is a high‑end GPU designed for large‑scale data processing and AI workloads, while A800 (2022) is a China‑specific variant of A100 with reduced performance and price to meet regional export restrictions.

Core specifications comparison: The article contrasts performance, memory, and price, noting A100’s price around US$15,000 (≈¥108,000) and A800’s lower price of US$12,000 (≈¥87,000).

Application scenarios: A100 targets data centers, AI research, and scientific computing; A800 is suited for general graphics rendering, modest machine‑learning tasks, and smaller enterprises.

H100 vs H200 vs H800: H100 (2022) introduces the Hopper architecture with up to 30× speed‑up for large language models; H200 (2023) upgrades H100 with 141 GB memory and 4.8 TB/s bandwidth, offering roughly double the inference speed; H800 is a China‑specific version of H100.

Core specifications comparison: Detailed tables (omitted here) illustrate the differences in CUDA cores, memory, and bandwidth.

Application scenarios: H100 serves massive AI training, scientific simulation, and cloud data‑center workloads; H200 focuses on the Chinese market with similar use cases; H800 is positioned for regional data‑center and AI tasks.

Pricing and market positioning: H100 costs ¥210,000‑¥290,000 per card, H200 ranges ¥2.45‑¥2.55 million, and H800 is priced around ¥2.7 million as of September 2024.

B100 vs B200: Both are based on the Blackwell architecture; B100 uses a 4 nm process for high performance and low power, while B200 employs a chiplet design that combines two B100 dies for greater scalability.

Core specifications comparison: Tables (omitted) show differences in transistor density, memory bandwidth, and power consumption.

Application scenarios: B100 targets HPC, deep‑learning training, and data analysis; B200 is aimed at ultra‑large data centers and AI/ML workloads requiring extreme compute power.

Pricing: B100 is priced between US$30,000‑US$35,000 (≈¥210,000‑¥245,000); B200‑based DGX systems are quoted at US$515,410 (≈¥3.64 million).

L40S: Launched in 2023, L40S is a powerful GPU for data‑center workloads such as generative AI, LLM inference, 3D rendering, and video processing, offering up to 5× higher inference performance than earlier models and 2× better ray‑tracing capability.

Performance parameters: Includes specifications on CUDA cores, memory, and power efficiency (details omitted).

Application scenarios: Model training, high‑performance inference, generative AI, ray‑tracing, and media processing.

Pricing: Wholesale price in April 2024 is roughly ¥70,000‑¥80,000, down from an earlier ¥120,000.

The article concludes with source attribution to AI洞察局 and author information, and includes promotional notices for bundled technical PDF/PPT resources.