DeepSeek R1 Private Deployment GPU Selection Guide (NVIDIA A100, H100, A800, H800, H20 Series)

With the popularization of large language models, especially the emergence of DeepSeek R1, the demand for private deployment of large language models in various industries is continuing to rise.

For most enterprises and institutions, the most urgent thing at present is not to train a model of their own, but to quickly deploy and implement business through methods such as RAG and fine-tuning.

This article will introduce the differences between the various and the choice of GPU. I hope it will be helpful to you.

1. Application scenarios of various versions of DeepSeek R1

The various versions of DeepSeek R1 are shown in the following table:

Application scenarios of each version of DeepSeek R1:

• 1.5B:  Suitable for simple task scenarios that are cost-sensitive and seek efficiency, such as some basic text classification and simple information extraction tasks.

• 7B & 8B:  General models for tasks of medium complexity in multiple scenarios. The 8B version has improved accuracy and is suitable for scenarios with higher requirements for output quality. For example, it can be applied to content creation, translation, coding problems, and as an AI assistant.

• 14B:  Capable of handling more complex tasks, especially in areas such as code generation.

• 32B & 70B:  These two large-parameter versions are targeted at professional and high-quality tasks. They are capable of handling complex tasks that require extremely high precision, such as text generation in professional fields, deep code analysis, and high-difficulty question answering that requires large-scale knowledge and reasoning.

• Zero (671B):  Full version. Able to handle complex problems that require deep thinking and iteration. This version of the model is also more focused on research purposes, such as exploring the model's deep thinking process and solving logical puzzles.

  
  

2. NVIDIA GPU

NVIDIA A100 80GB

• Architecture :  Ampere

• Memory :  80GB HBM2e

• FP32 performance :  19.5 TFLOPS

• NVLink :  Bandwidth 600 GB/s (version 3)

• Price :  Approximately $20,000

• Features :

  Designed for data centers and high-performance computing, it supports large-scale AI training and reasoning. High-bandwidth memory and NVLink 3.0 enable it to perform well in multi-GPU interconnection scenarios, making it suitable for scientific computing and deep learning tasks that require high throughput.

NVIDIA H100 80GB

• Architecture :  Hopper

• Memory :  80GB HBM2e

• FP32 performance :  67 TFLOPS (nearly 1.5 times higher than A100)

• NVLink :  bandwidth 900 GB/s (version 4)

• Price :  $30,000–40,000

• Features :

  The flagship model of the Hopper architecture significantly improves computing density and energy efficiency. NVLink 4.0 doubles bandwidth, suitable for ultra-large-scale AI model (such as GPT-4 level) training and real-time data analysis, and is an ideal choice for next-generation data centers.

NVIDIA A800 80GB

• Architecture :  Ampere (limited version)

• Memory :  80GB HBM2e

• FP32 performance :  19.5 TFLOPS (same as A100)

• NVLink :  Bandwidth 400 GB/s (version 3, limited)

• Price :  Approximately $20,000

•  Features :

  Export-restricted version of A100, NVLink bandwidth reduced from 600 GB/s to 400 GB/s, possibly for specific regional markets (such as China). The performance is the same as A100, but the efficiency of multi-card interconnection is reduced, suitable for single card or low bandwidth demand scenarios.

NVIDIA H800 80GB

• Architecture :  Hopper (limited version)

• Memory :  80GB HBM2e

• FP32 performance :  67 TFLOPS (same as H100)

• NVLink :  bandwidth 400 GB/s (version 4, limited)

• Price :  $30,000–40,000

• Features :

  The restricted version of H100 has a significantly reduced NVLink bandwidth of 400 GB/s, which may also be aimed at markets subject to export restrictions. The computing performance is not reduced, but the multi-GPU scalability is limited, which is suitable for single-card high-performance requirements or small-scale clusters.

NVIDIA H20 (unreleased)

• Architecture : Hopper (limited version)

• Memory : 96GB HBM3 (first model with HBM3)

• FP32 performance : 44 TFLOPS (lower than H100)

• NVLink : Bandwidth 900 GB/s (version 4, limited)

• Price : Estimated $12,000–15,000

• Features :

  Aiming at the cost-effective market, although the FP32 performance is only 65% ​​of H100, it is equipped with larger HBM3 memory and full NVLink bandwidth, which is suitable for memory-intensive tasks (such as large language model reasoning). The price advantage is obvious, and it may be positioned for mid-to-high-end enterprise applications.

3. Model memory requirement assessment

The memory requirements of the model mainly include weight memory, KV cache and activation memory.

• Weight Memory: Weight memory is used to store model parameters (such as neural network weights and biases) and is the statically occupied portion of the model loaded into the video memory. During training and inference, weights must reside in the video memory for calculation.

• KV Cache (Key-Value Cache): In the self-attention mechanism of the Transformer model , the KV cache is used to store the Key and Value vectors of each position to avoid repeated calculations (especially in generative tasks). For example, when generating text, it is necessary to cache the KV values ​​of historical sequences to speed up subsequent predictions.

• Activation Memory: Activation memory is used to store intermediate calculation results in forward propagation (such as the output of each layer). These values ​​need to be retained during training to calculate gradients. Some of them can be discarded during inference, but complex models (such as those with residual connections) still need to retain some activation values.

  
  

4. Model scale and hardware adaptation suggestions

• Small Models (1.5B–8B)

• Total memory: 3.44–18.36GB

• Compatible hardware : A single consumer-grade GPU (such as RTX 4090 24GB) can run, no need for multiple cards.

• Medium Models (14B–32B)

• Total memory: 32.12–72.96GB

• Compatible hardware : A single high-performance computing card is required (such as A100 80GB or H100 80GB).

• Large Model (70B)

• Total memory: 159.6GB

• Compatible hardware : Multiple cards must be used in parallel (such as 2×H100 80GB or 4×A100 80GB).

• Ultra-large scale model (671B)

• Total memory: 1530GB

• Suitable hardware : A large-scale cluster is required (such as 20×H100 80GB or a distributed training framework).

  
  

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