A100, 4090, RTX 6000 Ada, RTX 4000 Ada, which one is the real good card in the era of AI reasoning?

If you are working on artificial intelligence, whether you want to train a large language model or let an AI agent do something for you, choosing a suitable GPU is very important. It is just like choosing a car, whether you choose a high-powered sports car for extreme performance or an economical family car for daily transportation, it all depends on your needs and budget.

Are there so many GPUs on the market that you may be a little dazzled? Don't worry, today I will take you through several of the most popular NVIDIA GPUs on the market: the A100, the big brother of the data center, the RTX 4090, the performance monster in the consumer market, and the two major players in the professional workstation field - RTX 6000 Ada and RTX 4000 Ada. We will start with the official core parameters, using a clear table to let you see at a glance, and then talk in depth about their performance when running large models like DeepSeek, and finally analyze their respective uses in the field of AI. I will try to use plain language, hoping to help you clear the fog and find the "core" that suits you best.

1. Head-to-head competition: core parameter comparison

When choosing a GPU, you must first look at its "foundation". The core parameters of a GPU are its "hardware conditions". We looked up these data from NVIDIA's official website and compiled them into the following table for your horizontal comparison.

Friendly reminder:

• The A100's video memory bandwidth is far ahead, using HBM2e, which is a great help for tasks that require frequent reading and writing of large amounts of data.
• The RTX 4090's CUDA core count and FP32 single-precision floating-point performance are very impressive, making it unique among consumer-grade cards.
• As professional cards, RTX 6000 Ada and RTX 4000 Ada are equipped with ECC video memory, which can correct errors. This is very important for professional applications that require high data accuracy, such as scientific computing and financial modeling. Of course, the stability of AI training and reasoning also benefits.
• In terms of power consumption, the RTX 4090 is a "power hog", while the RTX 4000 Ada is very "gentle".

The prices in the table are the GPU server prices of the DigitalOcean cloud platform. Compared with first-tier manufacturers, DigitalOcean's prices are more affordable and transparent. For more details, please scan the QR code at the end of the article to consult DigitalOcean's exclusive strategic partner in China, Zhuopu Cloud.

After reading these dry numbers, you may still be a little confused. Don't worry, numbers are just the basis, and the real competition depends on actual performance.

2. Actual combat exercise: Who is better when running the DeepSeek model?

Let's take a look at what these GPUs are like when running AI models, especially language models like DeepSeek. As an excellent open source large model, DeepSeek has versions with different parameter scales, such as DeepSeek Coder, DeepSeek LLM 67B, etc. They are quite demanding and put a lot of pressure on the GPU's computing power and video memory.

Although it is difficult to find unified and accurate third-party horizontal evaluation data for these four cards and all DeepSeek model versions (after all, the evaluation environment, configuration, and optimization may be different), we can make a reasonable inference and analysis based on their architectural characteristics, core parameters, and some public performance reports for similar large language models (LLM).

1. A100: The stabilizing force on the training ground

Although A100 is a card of the previous generation Ampere architecture, its position in the field of AI training is still solid, especially in large-scale cluster training. Why?

• Large video memory is king
  80GB HBM2e video memory is critical when training large models with tens of billions or even hundreds of billions of parameters. Think about it, model parameters, activation values, gradients, and data all have to be stuffed into the video memory. If the video memory is not enough, you have to find ways to move it, such as using CPU memory, which will slow down the speed, just like when you cook, the ingredients have to be taken from the deep end of the refrigerator, which is definitely inefficient. Models like DeepSeek LLM 67B require a lot of video memory during training. The large video memory of A100 can make the training process smoother and reduce training interruptions or inefficiencies caused by video memory bottlenecks.
• NVLink  high-speed interconnect
  Multi-GPU parallel training is the norm. A100 supports high-speed NVLink, with inter-GPU communication bandwidth up to 600GB/s. This is like building a data highway, allowing smoother information transmission when various GPUs work together. For models with huge parameter volumes such as DeepSeek, when training with multiple GPUs, the efficiency of inter-GPU communication directly affects the overall training speed.
• TF32 precision support
  A100 supports TF32, a special computing format, which can provide computing speed close to FP16 with almost no loss of precision, and is faster than FP32. This is a very practical feature for training LLM and can effectively improve training efficiency.

Therefore, if you have a large model like DeepSeek that needs to be trained from scratch (pre-training) or fine-tuned on a large scale (fine-tuning), the A100 cluster is still the first choice for many large research institutions and enterprises. It is like an experienced veteran. Although it may not be the most powerful in a single fight, its stability and coordination ability are very reliable in large-scale battles. (If you want to know how to do DeepSeek fine-tuning, you can read our previous  DeepSeek fine-tuning practice . We also wrote an article specifically introducing  how to choose a GPU for DeepSeek fine-tuning ).

2. RTX 4090: Consumer flagship, dark horse for inference and lightweight training

The RTX 4090 card was originally designed for gamers, but its powerful raw computing power (82.58 TFLOPS FP32) and 24GB GDDR6X video memory make it a "cross-border star" in the field of AI.

• Inference speed is amazing
  For inference of the trained DeepSeek model, such as using it to generate code and answer questions, the RTX 4090 performs very well. Its high clock speed and large number of CUDA cores have obvious advantages in handling parallel computing tasks. Imagine that you ask DeepSeek a question, and the 4090 is like a super-fast student who can quickly organize language to give an answer.
• Single card fine-tuning potential stocks
  Although 24GB of video memory is not enough for training a model of the size of DeepSeek 67B from scratch, RTX 4090 can be very efficient with its powerful computing power for fine-tuning DeepSeek models with smaller parameters (such as 7B or 13B), or for performing some specific optimizations on large models with controllable video memory requirements (such as LoRA, QLoRA, etc.). Many individual developers and small teams like to use 4090 to "refine the elixir", and the cost performance is very outstanding.
• Price advantage
  Compared with professional cards and data center cards, the price of RTX 4090 is much more affordable. Of course, its price has risen for various reasons, but overall, the cost per unit of computing power is still relatively low.

However, the 4090 also has its "little problems". For example, it does not support NVLink (only SLI, but it is not very useful for AI), and the efficiency of multi-card parallel operation is far less than that of professional cards. The power consumption is also relatively high, and you need to equip it with a good power supply. And as a consumer-grade card, its driver may not have special optimization and stability guarantees for AI applications like professional cards. It is fine for short-term use, but you should think twice before long-term use.

3. RTX 6000  Ada  Generation: The king of professional workstations ,  an all-rounder in AI development

RTX 6000 Ada is the professional flagship under the Ada Lovelace architecture. You can think of it as a "professional upgrade" of RTX 4090. It is more balanced and powerful in all aspects and is designed for demanding professional applications. AI is naturally one of its main battlefields.

• 48 GB  large video memory  + ECC
  48GB GDDR6 ECC video memory, which is almost twice that of consumer-grade cards. This video memory capacity is much more comfortable for running and fine-tuning large models like DeepSeek. You can load larger models or use larger batch sizes when fine-tuning to improve training efficiency and model effects. The error correction function of ECC video memory also ensures the stability and data reliability of long-term AI tasks, which is like buying an insurance policy for commercial projects and important research.
• Powerful comprehensive performance
  18176 CUDA cores, 91.1 TFLOPS of FP32 performance, and fourth-generation Tensor Cores make RTX 6000 Ada capable of handling complex AI calculations. Whether it is model training, large-scale reasoning, or AI-assisted content creation, it can provide strong power. Running the DeepSeek model, whether it is reasoning or fine-tuning a medium-scale model, the experience will be very smooth.
• NVLink  Interconnect
  It supports NVLink. Although the bandwidth (80 GB/s bidirectional) is not as good as A100, it is worse than nothing for building a dual-card workstation for more complex model training or processing larger data sets, and can effectively improve the efficiency of dual-card collaborative work.
• Professionally driven and certified
  NVIDIA provides optimized Studio drivers and enterprise-level drivers for professional cards. These drivers are more guaranteed in terms of stability and compatibility, which is very important for scenarios that require AI applications to run stably for a long time.

Of course, the price of RTX 6000 Ada is also there, which is much more expensive than RTX 4090. It is more suitable for professionals, research institutions or enterprises who have a strong demand for stability, reliability, large video memory and sufficient budget.

4. RTX 4000  Ada  Generation: Professional card, the best choice for AI  beginners and small- and medium-scale deployment

RTX 4000 Ada can be seen as the "youth version" of RTX 6000 Ada. While retaining the advanced features of the Ada Lovelace architecture, it has made some reductions in core size and video memory to adapt to the more mainstream professional market and budget.

• 20GB  ECC video memory
  Although not as "generous" as the 6000 Ada, 20GB of ECC video memory is enough for many AI applications. For example, running some quantized and optimized DeepSeek models for inference, or fine-tuning and experimenting with some small and medium-sized models (such as those with parameters between 1B and 7B), the RTX 4000 Ada can handle it.
• Excellent energy efficiency ratio
  The power consumption of 130W is very low among professional cards. For some AI applications that are sensitive to power consumption or need to be deployed on edge devices or small servers, the low power consumption and compact single-slot or dual-slot design (depending on the version of different manufacturers) of RTX 4000 Ada are very advantageous.
• Professional feature inheritance
  It also has professional card features such as ECC video memory and professional driver support, ensuring work stability and reliability.
• Relatively moderate price
  In the professional card series, the RTX 4000 Ada is more affordable and is a good starting point for entering the field of professional AI development.

For models like DeepSeek, RTX 4000 Ada may not be suitable for large-scale training from scratch, but in terms of inference, especially the version optimized for model size and computing requirements, it should provide good performance. For users with limited budgets who need the stability and features of professional cards, such as start-up AI companies, university laboratories, or scenarios where AI inference capabilities need to be deployed on multiple terminals, RTX 4000 Ada is an option worth considering.

To summarize the speculation on the performance of the DeepSeek model:

• Large-scale training of DeepSeek (such as 67B Pre-training/Fine-tuning):
  A100 (cluster) > RTX 6000 Ada (single/dual card, video memory is the main bottleneck) > RTX 4090 (very difficult, almost impossible to complete and efficiently train) > RTX 4000 Ada (not applicable)
• Medium-scale DeepSeek fine-tuning (such as 7B-13B Fine-tuning):
  RTX 6000 Ada > RTX 4090 (strong computing power but video memory may reach bottleneck first) > A100 (single card computing power is not as good as the new architecture, but video memory still has advantages) > RTX 4000 Ada (feasible, but limited in speed and batch size)
• DeepSeek model inference:
  RTX 4090 (single card raw reasoning speed may be the fastest) ≈ RTX 6000 Ada (professional optimization and stability bonus) > A100 (inference performance density is not as good as the new card) > RTX 4000 Ada (sufficient performance, high energy efficiency ratio)

Please remember that these are just inferences based on parameters and known information. Actual performance will also be affected by many factors such as software optimization, driver version, specific model implementation, etc. The best way is to find targeted reviews or try it yourself (you can also test the following on DigitalOcean using the above GPU instance, the platform supports per-second billing).

3. Each one shows its unique ability: Analysis of AI  industry scenario applications

After talking about the parameters and approximate performance, let’s take a look at what roles these “players” are good at playing in different tracks of the AI ​​industry.

1. Training Large Models

This is the most expensive and hardware-intensive part of AI. Just like building a skyscraper, you need a strong enough construction team and heavy machinery (GPU cluster) to build it.

• Protagonist: NVIDIA A100
• Why it?
  As mentioned earlier, the 80GB HBM2e large video memory, high-speed NVLink interconnect, mature ecosystem and software stack make A100 still the mainstay in large-scale distributed training. Training a model with a huge number of parameters like GPT-3 or DeepSeek 67B often requires hundreds or thousands of A100s to work in parallel for several months. The video memory capacity of a single card determines how large the model shards or batches can be, and the communication speed between cards determines the training efficiency of the entire cluster. A100 has a good balance in this regard.
• Lifestyle scenes:
   Imagine that we want to compile a super encyclopedia (big model), which requires many editors (GPUs) to work simultaneously. Each editor's desk (video memory) must be large enough to spread out enough information (model parameters, data). Editors must also communicate and discuss frequently (NVLink). If communication is not smooth, the overall progress will be slowed down. A100 is a team of senior editors equipped with large desks and high-speed internal communication systems.
• Challenger: RTX 6000  Ada  Generation
• What is the potential?
   Although 48GB of video memory is less than A100, it is still top-level among current professional cards. Single or dual RTX 6000 Ada cards are very suitable for training medium-sized models or fine-tuning large models for a long time. Its raw computing power is stronger than A100, and the Tensor Core of Ada architecture is more efficient. For research teams or enterprises that do not have the budget and scale to require large-scale A100 clusters, but want to control the training process themselves, RTX 6000 Ada is a good choice.
• Lifestyle scenes:
   If A100 is the compilation team of a national library, RTX 6000 Ada is like a well-equipped university research institute. They may not pursue compiling the largest and most comprehensive encyclopedia, but they have high research and output capabilities for monographs in their own fields (medium-scale models or fine-tuning in specific fields).
• The role of RTX 4090 and RTX 4000  Ada  in large model training:
• The 24GB video memory of RTX 4090 is basically not enough for real "large model" training. It is more suitable for individual developers to learn, experiment, or do some small-scale pre-training and fine-tuning of specific tasks (such as LoRA).
• The same goes for the RTX 4000 Ada's 20GB of video memory, which is more geared toward learning and experimentation.

2. Model Inference

After the model is trained, it has to be used, right? The process of letting the AI ​​model give results based on new input is called reasoning. For example, when you use a voice assistant, it converts what you say into text and then understands your intention; or when you use AI to draw, it generates pictures based on your text description. These are all reasoning. Reasoning pursues speed, accuracy, and economy.

• The best price/performance ratio: RTX 4090
• Why it?
   The powerful raw computing power allows the RTX 4090 to process single inference requests at a very fast speed. For many AI applications that require real-time response, such as AI chatbots and real-time image recognition, the 4090 can provide a very good experience. Although it is a consumer card, as long as the application scenario does not require extreme stability (such as allowing occasional service restarts), it is very cost-effective. Many small and medium-sized enterprises or individual developers will use it to deploy inference services.
• Lifestyle scenes:
   The RTX 4090 is like a responsive simultaneous interpreter. As soon as you finish speaking, it can translate it accurately and efficiently.
• Professional and stable choice: RTX 6000  Ada  / RTX 4000 Ada
• What are the advantages?
   These two professional cards also perform well in inference. With larger video memory and higher computing power, RTX 6000 Ada can handle more inference requests at the same time or run more complex, less optimized models. RTX 4000 Ada, with its excellent energy efficiency and low power consumption, is very suitable for deployment in scenarios with power consumption and space requirements, such as edge computing devices or embedded systems. ECC video memory and professional drivers also provide guarantees for long-term stable operation.
• Lifestyle scenes:
   RTX 6000 Ada is like a super customer service representative in a large call center, able to handle a large number of inquiries at the same time and ensure the quality of service. RTX 4000 Ada is like an AI chip installed in a smart security camera, which has low power consumption but can quietly, stably and reliably complete tasks such as face recognition and behavior detection.
• A100's role in reasoning:
• Of course, A100 can also do inference, and it still has its place for those super-large models that require a lot of video memory to run, or cloud inference scenarios that require extremely high throughput. But from the perspective of inference efficiency and cost per unit computing power, new cards with Ada architecture usually have more advantages.

3. AI  Agent and AI Application Development

AI Agent is a very popular concept recently. You can think of it as an intelligent entity that can autonomously understand, plan, and execute complex tasks. To develop AI Agent or other AI-driven applications, you need a GPU environment that can run experiments, make prototypes, and support daily development.

• All-round development platform: RTX 6000  Ada
• Why recommended?
   The 48GB large video memory allows developers to easily debug and run models of various sizes, whether fine-tuning the model themselves or processing data locally after calling third-party APIs. Powerful computing power can accelerate code compilation, model loading, and iteration speed of small experiments. The stability of professional drivers also makes the development process more worry-free. For professional AI developers or small AI teams, this is a "Swiss Army Knife" that can significantly improve productivity.
• Lifestyle scenes:
   RTX 6000 Ada is like a fully equipped and spacious personal studio where you can create and experiment to your heart's content. Various tools (software) can run smoothly, and the environment is stable without any problems.
• The most efficient and flexible choice: RTX 4090
• What's the attraction?
   For many individual developers, researchers, or startup teams with limited budgets, the RTX 4090 is a very attractive choice. Its high computing power and relatively low price make it possible to quickly iterate and verify ideas. Although the video memory and professional features are not as good as the RTX 6000 Ada, in many AI Agent development scenarios, such as secondary development based on existing large model APIs, building knowledge bases, running some medium-sized local models, etc., 24GB of video memory combined with its powerful computing power can already provide very good support.
• Lifestyle scenes:
   The RTX 4090 is like a high-performance personal computer. You can program, design, and run various software on it smoothly. Although it may not be suitable for super-large projects, it is more than enough for most personal creations and exploratory projects, and it is very powerful.
• Getting Started and Specific Scenario Development: RTX 4000  Ada
• Who is it for?
   If your development needs are mainly focused on inference applications of small and medium-sized models, integration of AI-assisted tools, or stricter control of power consumption and cost, RTX 4000 Ada is a solid choice. Its 20GB ECC video memory and professional features provide guarantees for entry-level AI development and development of specific embedded AI applications.
• Lifestyle scenes:
   RTX 4000 Ada is like a fully functional but more compact workstation. It may not be as luxurious as a top-end studio, but it can complete daily design, programming and testing tasks without any problems. It is also more energy-efficient and economical.
• A100's role in development:
• Unless your organization directly provides A100 as a development environment (usually a cloud platform or a large laboratory), individuals or small teams generally do not directly purchase A100 for daily development. It is more of a backend training and large-scale deployment role.

4. Selection Suggestions

After talking about so many technical details and application scenarios, I know you may still be a little confused. Don't panic, I will tell you some "real stuff" and hope it can help you sort out your thoughts.

• About price and budget:
   This is the most realistic problem. The prices of A100 and RTX 6000 Ada are relatively high, and they are usually purchased by enterprises or research institutions. Although the price of RTX 4090 has also increased a lot, it is relatively easier for individuals to afford. RTX 4000 Ada is in a middle position and is the entry threshold for professional cards. You have to weigh your wallet first and clarify the upper limit of your budget. Remember, there is no best, only the most suitable.
• “Just enough” vs. “Get it right in one go”:
   If you are just getting started with AI and want to learn and run small projects, then an RTX 4090 or even a lower-end card may be enough for you to toss for a while. But if you are a professional, or your project has clear high requirements for performance, video memory, and stability, then "one-stop" selection of RTX 6000 Ada or consideration of A100 (if it is mainly for training) may help you avoid many detours and save precious time.
• Don’t forget the ecosystem and software:
   NVIDIA's CUDA ecosystem is a huge advantage. Most AI frameworks and libraries have good support for NVIDIA GPUs. When choosing, you should also consider the stability of the driver, the activity of the community, and the compatibility of your commonly used software. Professional cards usually have more stable and more rigorously tested drivers.
• Power consumption and heat dissipation:
   High-performance GPUs are often "power hogs" and "big furnaces". The RTX 4090 has a power consumption of 450W, so you need a sufficiently powerful power supply and good chassis cooling. The power consumption of A100 and RTX 6000 Ada is also not low. RTX 4000 Ada performs best in this regard. These are all practical issues that you need to consider when building or deploying a computer. However, if you are using a GPU cloud service like DigitalOcean, then you don't have to worry about power consumption, because the cloud platform will take care of it.
• Second-hand market and alternatives:
   If your budget is really tight, you can also look at the second-hand market (but be aware of the risks), or consider some previous generation professional cards. However, for AI applications that pursue the latest technology and the best performance, cards with new architectures usually have more advantages.
• Listen to what everyone says:
   In addition to official propaganda and my analysis, you can also visit more technical forums and communities (such as r/MachineLearning, r/LocalLLaMA on Reddit, Zhihu, V2EX, etc. in China) to see the evaluations and usage experiences of real users. Sometimes, some "pitfalls" or "little tricks" can be very valuable.

After all, choosing a  GPU is like finding a capable partner for yourself.

• If you want to conquer the most difficult large-model training fortress , you need   an experienced and capable veteran like A100 , which has a huge "knowledge reserve" (large video memory) and strong "team collaboration capabilities" (NVLink).
• If you are an AI application deployer or individual player who pursues extreme efficiency and cost-effectiveness , and hopes that model reasoning is as fast as lightning, and you can occasionally "make some small pills" by yourself, then  the RTX 4090  , a "young and talented" performance monster, may make you shine, with its fast response and strong power.
• If you are a professional AI developer or content creator and need an "all-round platform" that can be used for research and development, experiments, and stable and reliable work, then  the RTX 6000  Ada  , a "mature, stable and well-equipped" expert, will be your right-hand man. It is thoughtful and comprehensive.
• If you are just entering the professional AI field, or need to deploy AI capabilities in specific scenarios (such as edge computing) and pursue stability and energy efficiency, then  the RTX 4000  Ada  , a "lean, practical, and affordable" professional newcomer, may be able to meet your needs just right.

I hope that our long talk today will give you a clearer and more three-dimensional understanding of these GPUs. The world of AI is changing with each passing day, and hardware iterations are also rapid. But no matter how the technology changes, if you clarify your needs and do your homework, you can always find the one that suits you best. I wish you have fun on the road of AI and make a name for yourself! Including A100, RTX 4000 Ada, RTX 6000 Ada, DigitalOcean can also provide a variety of GPU servers such as H100, H200, A6000, L40S, etc., with low prices, stable performance, and full traffic management, and discounts for long-term use