Deploy DeepSeek on Volcano Engine, faster, cheaper and safer
Deploy DeepSeek through Volcano Engine to achieve efficient, low-cost and safe AI reasoning.
Core content:
1. Technical challenges brought by large-scale AI reasoning and limitations of existing IT capabilities
2. Advantages and performance of Volcano Ark calling DeepSeek service
3. Practical steps and optimization effects of Volcano Engine AI cloud native support for DeepSeek deployment
The continued popularity of large models such as DeepSeek has led more and more industries to accept and use generative AI, but large-scale AI reasoning scenarios have also brought new technical challenges.
If most companies continue to use existing IT capabilities to deploy DeepSeek locally, they will face challenges such as long model effect adaptation cycle, difficulty in optimizing model performance, and slow expansion of cluster resource demand. In addition, problems such as high operation and maintenance costs and difficulty in ensuring security risks will also slow down the pace of AI business innovation.Once traffic peaks are encountered, it is necessary to continue to increase investment in GPUs and computing power at the inference layer. When faced with problems such as complex model environment configuration, adaptation and optimization, self-developed acceleration technology is also required, which further increases the investment costs in manpower and financial resources.
Therefore, more and more companies are turning their attention to the cloud. Many companies choose to access the DeepSeek service by calling the Volcano Ark API. Volcano Ark provides ultra-low latency of less than 20ms, an initial flow limit of up to 5 million TPM in the entire network, and the first 5 billion initial offline TPD quota in the entire network.
Today, the maximum configurable output tokens has been upgraded from 8k to 16k. The longest capacity on the entire network supports the generation of ultra-long texts without truncation. In scenarios such as content generation, customer service robots, and scientific research, it makes article writing more coherent, user needs more fully understood, and scientific research reviews and paper processing more efficient.
Third-party evaluations such as Keynote Listening Cloud, superCLUE, and Cyber Zen Heart all agree that the response performance, inference speed, and complete response rate of calling DeepSeek on Volcano Ark are excellent, and its comprehensive capabilities rank first. The outstanding performance of Volcano Ark is not accidental, but the interim result of the Volcano Engine AI Cloud Native's full-stack inference efficiency optimization with the model as the core.
Image source: Keynote Tingyun official account
Volcano Engine AI Cloud Native integrates the advantages of full-stack inference acceleration, best engineering practices, cost-effective resources, security and ease of use, and a good end-to-end experience, providing strong support for Volcano Ark and becoming the preferred cloud infrastructure for enterprises in the AI era.
> Four steps to get started and start the journey of efficient DeepSeek deployment
Based on the practical deployment process of Volcano Ark calling DeepSeek, Volcano Engine summarizes the end-to-end key steps from model selection to resource planning to deployment calling, allowing enterprise customers to enjoy the same AI cloud-native infrastructure as Volcano Ark.
The first step is model selection
The platform should be able to provide full-size models for customers to choose from, and should have the capabilities of model distillation, reinforcement learning, and training and pushing when customizing vertical models.
Step 2: Optimal Resource Planning
Ensure that computing, storage and other resources are both flexible and elastic, and can quickly increase and decrease resources when business is busy or idle to achieve efficient utilization; at the same time, build a flexible deployment model.
Step 3: Inference deployment engineering optimization
It is necessary to support elastic resource scheduling to ensure high utilization and rapid expansion capabilities, while also being able to achieve full-stack acceleration and inference acceleration to make the model run faster.
Step 4: Enterprise-level service call
Focus on the security and privacy protection of data and models, defend against DDoS and prompt word attacks, and support integration requirements such as API docking and IAM identity authentication.
> Model-centric AI cloud native makes DeepSeek deployment faster, more economical and safer
Volcano Engine provides strong technical support at every step of DeepSeek deployment, especially in the key areas of system capacity, inference speed and deployment security, allowing customers to complete deployment easily, efficiently and safely.
Support for rich model selection: Volcano Engine supports full-size DeepSeek models. Customers can achieve flexible on-demand deployment through Volcano Ark, machine learning platform veMLP, and container service VKE. Volcano Engine provides self-developed model distillation framework veTuner and reinforcement learning framework veRL, which support integrated training and push and task priority scheduling, helping customers to customize models in one stop. In addition, Volcano Engine also provides cloud server instances with a variety of GPU memory specifications from 24G to 96G. A single machine supports up to 768G of memory and can support the deployment of large models with more than 600B parameters. Its high-performance computing cluster instance supports high-speed interconnection of multiple machines, with a maximum interconnection bandwidth of up to 3.2Tbps, allowing enterprises to enjoy the full-blooded version of DeepSeek.
Cost-effective resource planning: Volcano Engine has achieved extreme cost-effectiveness through long-term technology-driven development. With its two major advantages of low resource costs and flexible product solutions, it provides the best resource planning for large-scale model application deployment in enterprises in terms of speed, quality and cost-effectiveness, making it the preferred choice for enterprises.
Low resource costs: Through a unified cloud-native infrastructure, Volcano Engine has been integrated with ByteDance's top businesses such as TikTok and Toutiao, which have massive computing resource pools. By leveraging the advantages of scale and self-developed servers, costs have been reduced to a relatively low level in the industry.
Flexible product solutions: Through elastic computing preemptible instances and original elastic reservation instances, it supports minute-level scheduling of 100,000 CPU cores and thousands of GPU cards. The tidal reuse of GPU resources can quickly allocate ByteDance's domestic business idle computing resources to Volcano Engine users during business off-peak periods, with prices up to 80% off.
Full-stack inference acceleration: Volcano Engine has made all-round optimizations at the AI infrastructure layer, providing full-stack, systematic inference acceleration to make the model run faster.
PD separation architecture and affinity deployment reduce the probability of data transmission across switches from a physical level, reduce the "detour" of data transmission, and increase the inference throughput by up to 5 times;
The self-developed KV-Cache cache acceleration product EIC reduces the inference latency to 1/50 and reduces GPU inference consumption by 20%;
Self-developed inference acceleration engine xLLM improves end-to-end large model inference performance by more than 100%;
The self-developed vRDMA network supports low-threshold, non-intrusive deployment, and provides up to 320Gbps vRDMA high-speed interconnection capabilities across GPU resource pools and storage resources.
The model runs safely and reliably: In terms of ensuring stable and safe model operation, Volcano Engine achieves fast model cold start and hot switching through comprehensive monitoring, rapid detection and efficient repair. Problems can be discovered in seconds and repaired in minutes. Single-machine migration tasks can be completed in less than 1 minute.
In addition, Volcano Engine has also developed its own large model application firewall, which can resist DDoS attacks and eliminate the risk of malicious tokens consumption. By preventing prompt word injection attacks, it can reduce the risk of data leakage by 70%, and reduce the incidence of model hallucinations and inaccurate responses by more than 90%, making the content ecosystem healthier.
While enterprises are paying attention to the effective application of large AI models, they should also actively seek AI infrastructure and deployment methods that suit them. Volcano Engine relies on ByteDance's technology accumulation and experience accumulation, and creates cost-effective deployment solutions through long-term technology-driven. The model-centric AI cloud native will continue to help enterprises accelerate AI transformation.
