The DeepSeek R1-671B full-blooded version solution was implemented within a million, and the 48G solution = 2 units eliminate 4 units; the operation and maintenance costs were cut in half!
DeepSeek R1-671B full-blooded solution breaks through the computing bottleneck and significantly reduces operation and maintenance costs.
Core content:
1. DeepSeek R1-671B solution solves the server busy problem and improves enterprise efficiency
2. Hard-core data and market potential analysis of 8-card RTX 4090-48G configuration
3. Technical specification comparison of 48G solution and 24G solution, with obvious cost-effectiveness advantage
More than two months have passed since the release of DeepSeek . Although the number of available times has increased , the frequent appearance of "Server busy, please try again later" has made people crazy! Many companies are thinking, such a useful tool, why not just rub one and run it within the company? By the way, the boss I know used 8 RTX 4090-48G cards to run DeepSeek R1-671B and delivered it to the customer. However, as a technical person, I suggest that it is better to use two in parallel for production. The two configurations not only meet the computing power requirements, but also have the best cost-effectiveness under the current conditions . Friends with ample space can consider matching four units to bring a new experience. Next, let us fully reveal the hard-core data and market potential of this GPU server!
Let’s take a look at the real data behind this GPU server : In the era of explosive computing power, the 8-card RTX 4090-48G configuration not only breaks through the waiting dilemma, but also hits the pain points with top performance, allowing enterprises to truly get rid of the bottleneck caused by "busy servers"!
[ Analysis of the core technical parameters of the two interconnection solutions ]
The performance list of the two interconnected devices:
Floating-point computing performance
• Highlights : Adopting cutting-edge GPU architecture, supporting FP16/FP32/FP64 multi-precision high-speed computing.
• Data support : The FP32 vector addition test takes only about 6.8ms on average, easily handling large-scale computing tasks.Memory size and bandwidth
• Highlights : 48GB GDDR6X memory per card , H2D bandwidth is stable at 4.09GB/s, meeting the needs of large model training.
• Advantages : Double the memory per card, making deployment easier, only one server (8 cards with a total of 384GB) is required vs. two servers (16 cards) for the 24G solution.Heat dissipation and power consumption management
• Highlights : 450W per card, total power consumption of the whole machine is about 4300W; equipped with air cooling and high-speed fans to ensure stable operation under high load.Ecosystem and Developer Support
• Highlights : The mature NVIDIA ecosystem and rich development resources enable efficient implementation of applications such as large language models, AI reasoning, and image and video analysis.
The powerful performance of this 8-card RTX 4090-48G achieves a perfect balance between computing power and video memory. The data fully proves that it performs well under multi-precision computing and high-load operation, providing a solid guarantee for large-scale AI training and real-time reasoning in enterprises!The above data demonstrates the core competitiveness of the product. Next, we will compare the technical specifications to understand why the 48G solution has a more cost-effective advantage in the market.
⚙️ (671B version configuration recommendation) Technical specification comparison section: 4090-24G vs 4090-48G
| Specifications | 4090-24G Solution | 4090-48G Solution |
|---|---|---|
| Single card memory capacity | 24GB GDDR6X | 48GB GDDR6X |
| Total video memory capacity | 16 cards × 24GB = 384GB | 8 cards × 48GB = 384GB |
| Server Deployment | 4 servers (8 cards each) | 2 servers (8-card configuration) |
| Single card price | Lower (base price) | 15% higher (approximately base price + 15%) |
| Deployment and electricity costs | Four servers, 16 cards, high electricity and maintenance costs | Two servers and eight cards, significantly saving electricity and operating costs |
Although the price of a single 4090-48G card is 15% more expensive, with the advantage of doubled video memory, only two servers are needed to achieve a total video memory of 768GB, greatly reducing deployment complexity, electricity costs and operation and maintenance costs. The overall cost-effectiveness is far better than the 4090-24G solution, providing a more cost-effective solution for enterprise applications! The two configurations are as follows:
? In the current AI hardware storm, major manufacturers are rushing to deploy high-performance GPU servers. The full-blooded version of DeepSeek R1 has become mainstream, tailored for running 671B large models. Today, hardware costs have dropped significantly compared to the initial stage. Enterprises no longer choose 70B or 32B versions to make up the numbers, but tend to prefer the full-blooded version of DeepSeek R1 to obtain higher computing power and efficiency. Data shows that the 4090-48G solution only requires two servers to deploy the full-blooded 671B model, while the 24G solution requires four servers and 32 graphics cards to achieve the same video memory, which greatly increases electricity costs, maintenance and deployment complexity.
The underlying logic is that the hardware dividend of cost reduction and efficiency improvement has created a strong demand for high-performance GPUs, and companies have shifted their deployment strategies to fully intelligent solutions to cope with increasingly fierce market competition. Data shows that high video memory, high bandwidth, and high parallelism are becoming future trends, and companies will rely more on this solution to break through computing power bottlenecks and seize market opportunities. Because of this, major companies are striving to achieve technological breakthroughs in the competition and move towards a new era of intelligent production! As mentioned earlier, this trend will have a profound impact on the future AI industry landscape, and next, we will continue to analyze how each key technology drives performance improvements and reveal the future technology code for you.
