Match resources around models
Model size, calculation accuracy, and context length are evaluated together, taking into account model weight, runtime overhead, and GPU memory required for concurrent requests.
AI COMPUTINGStart with the model.
AI Compute
Start with the model.
Plan the compute it needs.
Focusing on model inference, knowledge base retrieval and image generation, comprehensively evaluate GPU memory, computing resources and service concurrency, and formulate a deployment plan suitable for the business stage.
AI ComputeResource architecture
- 01Application layerAssistants · Knowledge bases · Generation
- 02service layerAuthentication·Retrieval·Inference Scheduling
- 03Computing power layerGPU · CPU / Memory · Storage
Provide model version, context length and target concurrency, and evaluate configuration and delivery based on actual available resources.
PRODUCT OVERVIEW
What is AI Compute?
- GPU resource assessment
- Model inference
- Application deployment
AI computing power is a combination of resources that carry model calculations and application services. The GPU is responsible for model operations suitable for parallel execution, and the CPU, memory, storage, and network jointly support data preparation, request scheduling, and result delivery. The selection starts with the model, tasks and access load, and then determines the resource configuration and deployment method.
What to evaluate
First establish whether the model fits in memory, acceptable response times and concurrent user demand, then size the GPU and supporting resources.
Use business metrics to judge performance
Online Q&A focuses on the waiting time for the first result and generation speed; batch tasks focus on completion time and throughput. Test with representative data and avoid comparing only hardware peaks.
Plan the complete application architecture
In addition to the inference node, application APIs, vector retrieval, file storage and access portals are considered simultaneously to coordinate resource expansion with application release.
INSIDE THE ARCHITECTURE
Plan compute as part of the complete AI service
An intelligent question and answer will go through identity verification, data retrieval, model queuing and result generation. Only by locating each period of waiting can we judge whether to increase computing power, optimize applications, or improve the network.
- 01
Application entry
The API completes identity verification, usage control and request verification, and then hands over valid tasks to the downstream.
- 02
Prepare the input
Retrieve knowledge bases, assemble context, or preprocess input files as business needs dictate.
- 03
Model inference
The model processes input and generates output, and GPU memory, concurrency scheduling, and computing power all affect performance.
- 04
Deliver results
Returned as streaming or task results, recording latency, completion status, and resource usage.
GPU memory holds more than model weights
Model weights are only part of the memory requirements. Longer contexts and more simultaneous requests will expand the inference cache; additional space is also required to run the framework. Quantification can reduce some resource consumption, but requires re-verification of model quality, framework support and actual performance.
Higher concurrency does not necessarily make each request faster
Batching and request scheduling can help improve overall throughput, but may increase latency. Interactive question and answer and offline batch tasks can use different queuing and concurrency strategies to avoid background tasks from crowding out online services.
Supporting resources can also become bottlenecks
Model loading relies on storage and network, and document processing and retrieval rely on CPU, memory and data layer. Observe the inference service and business API separately, first confirm the location of the bottleneck, and then decide whether to add GPUs.
When selecting, clearly identify the GPU model and memory, resource usage, software environment, network, and operation and maintenance responsibilities, and complete deployment verification based on business samples.
PERFORMANCE & CAPACITY
Use metrics to locate the bottleneck
After fixing the model version, accuracy, input length, and output length, compare different configurations. Interactive experience and total processing power should be measured separately.
| Metric | Application impact | How to assess |
|---|---|---|
| GPU memory and capacity | Successful loading of the model does not mean there is still enough space during peak concurrency. | Covers short question and answer, long context and peak concurrency, and records memory peaks and failed requests. |
| Time to first result | User waiting includes network, authentication, retrieval, queuing and input processing. | Split the time consumption of each link and observe the first Token delay under normal and peak loads. |
| Generation speed and throughput | The output speed of a single request is a different indicator from the total output of the service. | At the same time, the output speed of each request, total token throughput and actual concurrency are recorded. |
| Queueing and tail latency | Average elapsed time may mask congestion caused by a small number of long requests. | Observe wait queues, P95 delays, timeouts and cancellation ratios. |
| CPU, storage and network | Model loading, knowledge base retrieval, or data transfer can slow down the entire link. | Measure cold start, retrieval and result delivery separately, and determine bottlenecks by correlating GPU utilization. |
Metrics and terminology
- Token
- The unit of text processed by the model, the number is related to the text, language and word segmenter, and cannot be directly equated to the number of words.
- Time to first token
- The waiting time from the time a request is sent to receiving the first generated Token. The end-to-end measurement includes network and application overhead.
- KV Cache
- Partially generated models maintain a key-value cache of processed contexts, whose occupancy varies with sequence length, concurrency, and implementation.
- Quantization
- Techniques that use lower precision to represent model data need to simultaneously verify resource consumption, output quality, and runtime compatibility.
BEFORE YOU CHOOSE
Define requirements before choosing resources
Bring these four types of information to make the selection discussion more concrete.
- Models and tasks
- Model name and version, inference or fine-tuning requirements, accuracy and running framework.
- input and output
- Context length, generation length, image size, and representative business samples.
- Load and experience
- Peak concurrency, request frequency, allowed wait time, and task completion goals.
- Deployment and delivery
- Region, data size, access method, budget, optional GPU, resource usage and maintenance responsibilities.
APPLICATION SCENARIOS
Workloads to start with
Model inference and intelligent assistant
For online tasks such as question answering, summarization, and content assistance, the context length, generation length, and number of simultaneous requests are evaluated.
Enterprise knowledge base and search enhancement
Plan document processing, vector retrieval and model generation separately, and organize resources based on data size, update frequency and user permissions.
Image generation and asynchronous tasks
Evaluate GPU memory and time consumption based on model, resolution and task batch, and use task queue to manage queuing, progress and result saving.
DEPLOYMENT PLAYBOOK
First establish a measurable inference service, and then gradually increase the volume
Start with a clear model and business sample, verify functions, performance, and access rights before expanding the user base.
- 01
Organize workload
Confirm model sources and licenses, and organize typical inputs, longest contexts, generation lengths, and peak request patterns.
- 02
Check environment and deploy
Check GPU, driver and framework compatibility, fix model and dependency versions, configure persistent storage and service health checks.
- 03
Establish a capacity baseline
Test step by step from low concurrency, record the GPU memory, first token delay, throughput and errors, and determine the reasonable queue and concurrency upper limit.
- 04
Integrate and roll out gradually
Provide authentication and usage control through business API, verify streaming responses, cancel tasks and version rollback, and then gradually access users.
Deployment trade-offs
A single node is easy to get started, but release and failure may affect all requests; multiple nodes need to consider model loading, traffic scheduling and session processing, and reserve operation and maintenance costs.
OPERATE WITH CONFIDENCE
From launch to ongoing operations
Observe user experience and resource utilization
Also track first token latency, generation speed, queuing, errors and memory utilization. A busy GPU does not necessarily mean a good user experience. An idle GPU may also mean that upstream retrieval or request scheduling is blocked.
Manage model and environment versions
Record model weights, inference parameters, dependencies and image versions, and retain available fallback solutions. Re-evaluate quality and performance on the same samples after an upgrade to avoid just verifying that the service is up.
Protect data and service credentials
The inference interface is authenticated by the business service, and the model and database credentials are saved on the server. Record logs on demand, and set permissions and retention periods for input, output, and retrieval documents.
Arrange resources by load
Distinguish between long-term online inference and queuable batch tasks, planning capacity in conjunction with peak and maintenance windows. Incorporate computing, storage, bandwidth, and operations investments into cost assessments.
CHOICES & TRADE-OFFS
Compare product advantages under real conditions
When your business is
Real-time intelligent Q&A for users
Prioritize waiting time and concurrency stability
Based on the end-to-end first token delay, generation speed and peak error rate, avoid pursuing only the total throughput.
When your business is
Document processing, image generation and batch tasks
Priority planning queue and task completion capabilities
Evaluate resources using task deadlines, input sizes, and output requirements, complete with progress query, cancellation, and retry rules.
When your business is
Applications that already have external model APIs
Decide whether you need to host inference yourself
Your business may primarily need application servers, data tiers, and access protection; then evaluate GPU options only when there are clear deployment, cost, or resource control requirements.
Three judgments that are easily overlooked
If the memory is large enough, the response will be fast enough.
GPU memory first determines the model and task size that can be accommodated. Computing power, memory bandwidth, scheduling and application links also affect speed.
Multiple cards can be added directly by quantity
Multi-card deployment depends on model slicing, framework and inter-card communication; compatibility and actual benefits need to be verified.
If the model can be started, it means it can go online.
It is also necessary to verify the actual load, output quality, permissions, current limiting, faults and rollbacks to determine the number of users that the service can accommodate.
QUESTIONS & ANSWERS
Questions before you choose
How to determine how much GPU memory is needed?+
In addition to model weights, leave room for inference caching, activation, and running frameworks. Increased concurrency or longer context may increase GPU memory usage. Please conduct capacity testing using the model, accuracy, and request length you plan to launch.
Is it possible to specify the GPU model?+
You can submit the target model, GPU memory and quantity requirements, and the technical consultant will check the current available resources, region and delivery method, and then formulate a specific configuration and quotation.
Can inference and model training use the same set of configurations?+
The two resources have different focuses. Training and fine-tuning also involve gradients, optimizer status, data reading and multi-card communication, which should be evaluated separately; inference resource requirements cannot be directly used as the basis for the configuration of the training cluster.
How does AI computing power cooperate with acceleration and security?+
Computing power carrying model and application calculation; access acceleration to improve network and resource delivery; application security management of public network entrance, API and data access. They should be designed individually by link and verified together through end-to-end testing.
YOUR NEXT STEP
Plan your AI Compute deployment
Provide business type, access area, peak load, data size and recovery objectives, and work with technical consultants to evaluate configuration, network and delivery solutions.