When deploying the full-power version of the DeepSeek large language model, hardware selection is key to unlocking its full potential.From NVIDIA’s H200 and H100 series to domestic options such as Biren Technology’s BR100, Muxi Integrated Circuit’s MXGPU-100, Haiguang’s DCU, and Ascend 910, different hardware platforms deliver diverse performance outcomes during deployment due to their unique architectures, GPU memory configurations, and computational capabilities.
NVIDIA Series
H200: The Ultimate Expression of Cutting-Edge Technology
The H200 represents the cutting edge of NVIDIA’s GPU technology, featuring an advanced architecture and up to 1.5TB of HBM3e memory, with computational power surging to 1.92 PetaFLOPS at FP8 precision.In a fully optimized DeepSeek deployment, when handling ultra-long text generation or complex multi-turn dialogue reasoning, the H200 ensures seamless data transfer between the memory and the GPU core thanks to its massive memory capacity and ultra-high bandwidth.A single card can easily handle 50–80 concurrent requests per second and process 1,500–2,000 tokens per request with ease, achieving a token processing rate of 5,000–8,000 tokens per second. Multi-card clusters, leveraging efficient distributed computing technology, can meet the large-scale, high-concurrency application demands of massive cloud services or research institutions for DeepSeek.
H100: A Reliable Flagship for Peak Performance
Based on the Hopper architecture, the H100 features 800 GB/s of HBM3 memory bandwidth, with memory capacities of 80 GB or 40 GB, and delivers up to 624 TFLOPS of FP8 performance.In DeepSeek deployment scenarios, when handling standard natural language processing tasks—assuming each request processes 1,000–1,500 tokens—a single card can handle 35–50 concurrent requests per second.Its powerful computing power and memory bandwidth enable rapid loading and computation of DeepSeek’s numerous model parameters, with a single card capable of processing 3,500–5,000 tokens per second. When multiple cards collaborate, through optimal resource scheduling, an H100 cluster can provide stable and efficient inference services for large-scale users, making it suitable for performance-critical commercial applications such as intelligent customer service systems for large enterprises.
H20: A Cost-Effective Model Optimized for Inference
Specifically optimized for generative AI inference, the H20 adopts the Grace Hopper architecture and is available in 96GB and 141GB HBM3 memory variants, delivering 48 TFLOPS of FP8 performance.The 96GB memory version handles DeepSeek-related tasks. In natural language processing scenarios, when processing 800–1,200 tokens per request, a single card can handle approximately 20–30 concurrent requests per second, with a total token throughput of 2,000–3,000 tokens per second.The 141GB VRAM version offers significant advantages when handling concurrent inference requests. In cloud service scenarios, a single card processes 800–1,200 tokens per request, handling 30–40 concurrent requests per second, with a token throughput of 3,000–4,000 tokens per second.The H20 is suitable for cost-sensitive small-to-medium-scale applications with certain performance requirements, such as text generation services for small businesses.
A100: The Classic Architecture Continues to Deliver
The Ampere-based A100 features 40GB or 80GB of HBM2 memory and delivers 312 TFLOPS of FP8 performance, having proven itself time and again in the field of deep learning.When deploying the full-power version of DeepSeek, for common natural language processing tasks, if each request processes 500–1,000 tokens, a single card can handle 25–35 concurrent requests per second, with a token throughput of 2,500–3,500 tokens per second.Multi-card cluster deployments, with optimized communication and resource management, provide reliable computing power for medium-scale applications and are suitable for model inference in medium-scale research projects.
A800: A Practical Choice with Optimized Adaptation
The A800 is optimized based on the A100, utilizing the same Ampere architecture and similar GPU memory configurations, but with more targeted hardware and software co-optimization. In DeepSeek deployments, its performance is comparable to the A100, with distinct advantages in specific scenarios.When handling general natural language processing tasks with 500–1,000 tokens per request, a single card can process 20–30 concurrent requests per second, with a token throughput of 2,000–3,000 tokens per second. The A800 is suitable for cost-constrained scenarios with hardware compatibility requirements, such as AI operations at internet companies with strict cost controls.
4090: A Cross-Industry Boost from Consumer-Grade Hardware
Based on the Ada Lovelace architecture, the 4090 features 24GB of GDDR6X VRAM and delivers 45 TFLOPS of single-precision floating-point performance. Although designed for the consumer and professional workstation markets, it plays a role in deep learning inference. Due to its relatively small VRAM capacity, it is suitable for lightweight or cost-sensitive DeepSeek deployments.For simple text generation tasks processing 500–800 tokens per request, a single card can handle 10–15 concurrent requests per second, processing approximately 1,500–2,000 tokens per second. This provides cost-effective computing power for small-team research, testing, and lightweight application development.
5090: An Exploration of Enhanced Performance
Although information on the 5090 is limited, it is speculated to feature upgrades in architecture and performance, with superior VRAM capacity, bandwidth, and computational power compared to the 4090. When deploying DeepSeek, it is expected to deliver outstanding performance for medium-scale tasks.It is estimated that when processing 600–900 tokens per request, a single card can handle 15–20 concurrent requests per second, with a total token throughput of approximately 2,000–2,500 tokens per second. This offers users seeking performance on a limited budget an option that bridges the gap between consumer-grade and professional-grade solutions.
Domestic Series
Biren Technology BR100: A Pioneer of Independent Innovation
Biren Technology’s BR100 utilizes a 7nm manufacturing process and achieves 30 TFLOPS of single-precision floating-point performance, making it a standout among domestic GPUs. When deploying the full-featured version of DeepSeek, for small-to-medium-scale natural language processing tasks—processing 300–500 tokens per request—a single card can handle 10–15 concurrent requests per second.A single card can process approximately 1,000–2,000 tokens per second. Although it falls short of NVIDIA’s high-end products, its performance is expected to gradually improve with the continuous advancement of domestic technology and software optimization. It is suitable for scenarios where cost is a primary concern and top-tier performance is not required, such as foundational language model research at local scientific research institutions.
Muxi Integrated Circuit MXGPU-100: The Potential of an Emerging Player
Muxi Integrated Circuit’s MXGPU-100 has achieved breakthroughs in architectural design and computational performance, demonstrating the capability to meet the demands of certain deep learning scenarios. In a DeepSeek deployment scenario, for simple text tasks processing 300–400 tokens per request, a single card can handle 8–12 concurrent requests per second, with a token processing rate of approximately 1,000 tokens per second.As Muxi continues to advance its technology and refine its ecosystem, the MXGPU-100 is expected to play a greater role in more application scenarios, injecting new momentum into the development of domestic computing power.
Haiguang DCU (Taking ShenSuan-1 as an example): Robust Development Through Ecosystem Integration
Based on the x86 architecture, the Haiguang DCU—taking the DeepSeek-1 as an example—possesses strong double-precision and single-precision floating-point computing capabilities. Through software stack optimization and algorithm adaptation, it is well-suited for deep learning tasks.When deploying DeepSeek, a single card handles text tasks, processing 400–600 tokens per request and handling 8–12 concurrent requests per second, with a single card capable of processing approximately 1,200–1,500 tokens per second. With technological iterations and ecosystem development, the Haiguang DCU will continue to expand its scope of application in the domestic computing power sector.
Ascend 910: A Solid Foundation for Powerful Tensor Computing
The Ascend 910 adopts the Da Vinci architecture, delivering up to 256 TFLOPS of half-precision (FP16) computing power. It features robust tensor computing capabilities and provides extensive support for large-scale neural network training and inference.In DeepSeek inference tasks within natural language processing scenarios, when processing 600–1,000 tokens per request, a single card can handle 15–20 concurrent requests per second, with a token processing rate of approximately 2,000–2,500 tokens per second.Through optimizations in the Ascend AI software stack, multi-card cluster deployments can significantly enhance overall concurrent processing capacity and tokens processed per second, meeting the demands of large enterprises’ internal AI application platforms for large-scale DeepSeek deployment and driving the widespread adoption of domestic computing power in the commercial sector.
Different mainstream hardware models have their own advantages and disadvantages when deploying the full-power version of DeepSeek. In practical applications, factors such as task scale, budget constraints, and application scenarios must be comprehensively considered to carefully select the most suitable hardware configuration and maximize the performance of the full-power version of DeepSeek.