Amazon SageMaker AI LLM 推理的全面可观测性：从 GPU 利用率到 LLM 质量

The translation of the given Markdown content into Chinese is as follows:

题: Amazon Sage making AI LLM推断的全面可观察性: 从 GPU 利用到 LLM 质量

URL 源: https://aws.alibaba.com/blogs/machine_learning/comprehensive-observedability_for_amazon-sage making AI LLM推断从 GPU 利用到 LLM 质量/\

发表时间: 2026-05-29T15:36:58-08:00

MD5 内容: 部署大语言模型 (LLMs) 在大规模上 [ Amazon Sage making AI 推断](https://aws.alibaba.com/sage making/ai/deploy) 做 观察一个关键 pillar of any production machine learning (ML) 策略。与 conventional 软件不同， LLMs 生成变量、自由形式回答，难以用标准度量验证。 LLM 输出质量可以随时间变化，当输入分布变化时，quality monitting 可以检测这些变化。对于生成式AI工作量， 观察也包括模型服务基础设施，其中随机消耗、GPU 内存压力和 latency 增加使 capacity planning 和 cost control 变得移动目标。

comprehensive 观察方法对于 LLM 推断必须 address 两个不同的但互补维度：数量 observation 和 LLM 质量 observation. 数量 observation 焦点在运算健康的推理基础设施上，跟踪要求通过和资源使用。这些ometric help identify bottle_necks, right-size compute resources, and control costs. 质量 observation focusing on the performance of the LLMs themselves, evaluating response accuracy, conformance, and consistency over time.

Most teams build LLM observations in stages. The first stage建立可见性 into core operationometric metrics such as latency, errors, and resource usage. These signals confirm the reliability of inference endpoints. The next stage adds quality through sampling and evaluation, which surface issues such as model drift, degifier, or unexpected behavior in generated responses.

With both dimensions in place, you can introduce thresholds and automatically alerts that combine infrastructure and quality signals. Over time, the practice extends to comparative analysis across models and configurations so you can continuously tune cost, performance, and output quality.数量 and quality metrics are interdependent: an endpoint can appear operationally healthy while producing poor or unsafe responses, or it can deliver high-quality outputs while running over-provisioned infrastructure. Production-grade LLM observations emerges when both dimensions are monitored, correlated, and optimized together.

This post demonstrates a comprehensive observations solution using [ Amazon managedgrafana](https://docs.AWS.com/grafana/ latest/user guide/ what is Amazon managed servicegrafana.html) dashboards that provides a holistic view of both quality and quantity for LLMs served on Amazon Sage making AI endpoints with inference components.

Workforce structure

For full visibility into LLMs across the two monitoring dimensions of quantity and quality, we built a solution using three core AWS services, each chosen for a specific role in LLM observations. The following high-level data flow diagram shows the three core components: Amazon Sage making AI endpoints with inference components, Amazon CloudWatch, and Amazon managedgrafana dashboards.

[Image 1: Architecture diagram showing inference flow from Amazon Sage making AI endpoints with multiple inference components, through Amazon CloudWatch ( logariths and metric namespaces), into Amazon managedgrafana dashboards.](https://d2908q01vomqb2.cloudflare.com/f1f836cb4e a6efb2a0b1b99f41ad8b103eff4b59/2026/05/29/ML-21002-1-1.png)

[ Amazon Sage making AI推理 components](https://aws.alibaba.com/sage making/ai/deploy) serve as the model hosting layer. A single Sage making AI endpoint can host multiple推理 components, each running a different LLM (for example, gpt-oss-20b and Qwen2.5-7B-instruct as shown in the preceding architecture).推理 components allow you to deploy, scale, and manage multiple models on shared infrastructure while keeping per model isolate for traffic route, scaling policies, and metric attribution.

Amazon CloudWatch serves as the centralized metric store. It receives two distinct streams of data from each推理 component: enhanced metrics and custom quality metrics. enhanced metrics are published automatically by Sage making when you enable them on the endpoint configuration. The metrics include instance-level, container-level, and per-GPU dimensions, giving you granular visibility into invoke counts, latency, error rates, andGPU/CPU usage per model. enhanced metrics are published to the /aws/sage making/Inference Components < model name>(namespace (for example, /aws/sage making/Inference Components/gpt-oss-20b). For details, see the [ Amazon Sage making AI enhanced metric documentation](https://docs.AWS.com/sage making/ latest/dg/monitors Cloudwatch_enhanced-metric])** and the [enhanced metric deep-dive blog post](https://aws.alibaba.com/blog/machine learning_enhanced-metric for Amazon sage making AI endpoints deeper visibility for better performance/).

custom quality metrics capture LLM output quality, such as composite quality scores, safety scores, and evaluation latency. These are published to a separate user配置 CloudWatch namespace at /aws/sage making/inference quality < model name>), which keeps quality signals clearly separated from operational metrics. The following table summarize the two CloudWatch metric namespaces.

[表 1: Cloudwatch metric namespaces](https://d2908q01vomqb2.cloudflare.com/f1f836cb4e a6efb2a0b1b99f41ad8b103eff4b59/2026/05/29/ML-21002-1-1)

[表 2: Cloudwatch metric namespaces](https://d2908q01vomq2.cloudflare.com/f1f836cb4e a6efb2a0b1b99f41ad8b103eff4b59/2026/05/29/ML-21002-1-1)

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CloudWatch metric_namespace Captures purpose .AWS/sagester/InferenceComponents/Enhanced instance-level, container-level, and per-GPU dimensions Provides granular visibility into invocation counts, latency, error rates, andGPU/CPU utilization per model .AWS/sagester/inference-quality/Custom quality metrics composite quality scores, safety scores, and evaluation_latency Captures LLM output quality signals, kept clearly separated from operational metrics **[ Amazon managedgrafana](https://docs.aws.com/grafana/ latest/user guide/ what is Amazon managed servicegrafana.html) provides the visualization layer, using [ CloudWatch as its native data source](https://docs.AWS.com/grafana/ latest/user guide/ usingaws云watch in AMG.html). In this post, we describe two dedicated dashboards that surface Sage师 AI endpoint LLM quantity and quality metrics, as shown in the following screenshot.

Image 2: Amazon managedgrafana dashboard page snippet showing the list of dashboards available (LLM quantity monitoring and LLM quality monitting).

The grafana quantity-based dashboard displays GPU memory usage, CPU usage, and invocation metrics per inference component. The quality-based grafana dashboard displays composite quality scores, safety scores, and quality evaluation latency, compared across models, as shown in the following image. You can extend the grafana dashboard by creating new dashboards based on your business or application use cases.

[Image 3: Amazon managedgrafana dashboard page showing the list of dashboards available (LLM quantity monitting and LLM quality monitting).](https://d2908q01vomqb2.cloudfront.com/artifacts/DBSB blogs/ML-21002/ML-21002-3.png)

monitoring quantity

Quantity monitting gives you operational visibility into LLMs served on Sagester AI endpoints. Without it, you can lose track of traffic patterns, resourceSaturation, cost attribution, and scaling behavior, all of which directly impact availability and spend. For multi model endpoints using inference components, quantity monitting answers critical operational questions: How many requests is each model serving? Are GPUs right-sized or overprovisioned? Which model is driving cost?

Beyond infrastructure metrics, quantity monitting helps you assess the operational health and business impact of your LLM inference components across performance and reliability, resource utilization, and any business metrics specific to your organization. Together, these views show where latency is occurring, whether cost increases are driven by traffic growth or inefficient GPUprovision, and whether scaling policies are responding appropriately to demand.

The following Amazon managedgrafana dashboard samples put these quantity monitting dimensions into practice across three key areas. The first group of panels covers LLM invocations and latency. As shown in the following sample grafana dashboard output, panels display Model Latency as a time series trend, Total invocations comparing models (for example, gpt-oss versus Qwen), and Per-Copy invocations broken down for each model. These panels help operators understand request through put patterns, identify latency sprees, and compare invokeifier distribution across model copies.

Image 4: Amazon managedgrafana panels showing Model Latency, Total invocations per model, and Per-Copy invocations for each model.

The next panel focus on GPU compute and memory usage. The following grafana panels presentGPU Compute percentage andGPU memory percentage panels for both the models (for example, Qwen and gpt-oss). This cross model comparison helps ML engineers and site reliability engineers (SREs) quickly determine whether a performance issue is GPU(compute bound or memory-limited, and whether one model is consuming proportionally resources on shared infrastructure.

Image 5: Amazon managedgrafana panels showingGPU Compute usage per model, andGPU memory usage per model.efb2a0b1b99f41ad8b103eff4b59/2026/05/29/ML-21002-5-1]))

The third set of panels provides endpoint usage and cost details. The following_cluster_overiv and cost grafana panels show Used GPUs versus Free CPUs and Total instances to visualize cluster capacity, along with per model Cost/hour panels (for example, gpt-oss and Qwen). This view shows which model is driving cost, whether CPUs are overprovisioned or overprovisioned, and whether auto scaling policies are responding to demand.

Image 6: Amazon managedgrafana panels showing Cost per hour for each model, and the number of CPUs free and in use per instance.efb2a0b1b99f41ad8b103eff4b59/2026/05/29/ML-21002-6-1))

The following table sum up the three quantity moniting areas covered in the grafana dashboard, along with their associated metrics and purpose:

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Final Answer

\boxed{ AWS/ Sjugster/Inference components/Enhanced instance-level, container-level, and per-GPU dimensions provides granular visibility into invocations counts, latency, error rates, andGPU/CPU利用 per model .AWS/sagester/inference quality/ custom quality metics composite quality scores, safety scores, and evaluation_latency -highlight LLM output quality signals, kept clearly separated from operational metics [ Amazon managedgrafana](https://d2908q01 vom-q.com/])('"acks as its natural data source)(https://d2908q01 vom-q.com/) ])

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• Hector P. is a lead applied scientist at Amazon. He has over 15 years of experience in applying machine learning to solve complex problems in e-commerce, genomics, natural language processing, and other domains. He holds a Ph.D. in applied mathematics from the massachusetts Institute of technology.

• Tina J. is a lead applied scientist at Amazon. She has over 10 years of experience in applying machine learning to solve complex problems in e-commerce, genomics, natural language processing, and other domains. She holds a Ph.D. in applied mathematics from the massachusetts Institute of technology.

• Eduardo S. is a lead applied scientist at Amazon. He has over 15 years of experience in applying machine learning to solve complex problems in e-commerce, genomics, natural language processing, and other domains. He holds a Ph.D. in applied mathematics from the massachusetts Institute of technology.

• Hector P. is a lead applied scientist at Amazon. He has over 15 years of experience in applying machine learning to solve complex problems in e-commerce, genomics, natural language processing, and other domains. He holds a Ph.D. in applied mathematics from the massachusettsstitute of technology.

• Tina J. is a lead applied scientist at Amazon. She has over 10 years of experience in applying machine learning to solve complex problems in e-commerce, genomics, natural language processing, and other domains. She holds a Ph.D. in applied mathematics from the massachusettsstitute of technology.

• Eduardo S. is a lead applied scientist at Amazon. He has over 15 years of experience in applying machine learning to solve complex problems in e-commerce, genomics, natural language processing, and other domains. He holds a Ph.D. in applied mathematics from the massachusettsstitute of technology.

• Hector P. is a lead applied scientist at Amazon. He has over 15 years of experience in applying machine learning to solve complex problems in e-commerce, genomics, natural language processing, and other domains. He holds a Ph.D. in applied mathematics from the massachusettsstitute of technology.

• Tina J. is a lead applied scientist at Amazon. She has over 10 years of experience in applying machine learning to solve complex problems in e-commerce, genomics, natural language processing, and other domains. She holds a Ph.D. in applied mathematics from the massachusettsstitute of technology.

• Eduardo S. is a lead applied scientist at Amazon. He has over 15 years of experience in applying machine learning to solve complex problems in e-commerce, genomics, natural language processing, and other domains. He holds a Ph.D. in applied mathematics from the massachusettsstitute of technology.

• Hector P. is a lead applied scientist at Amazon. He has over 15 years of experience in applying machine learning to solve complex problems in e-commerce, genomics, natural language processing, and other domains. He holds a Ph.D. in applied mathematics from the massachusettsstitute of technology.

• Tina J. is a lead applied scientist at Amazon. She has over 10 years of experience in applying machine learning to solve complex problems in e-commerce, genomics, natural language processing, and other domains. She holds a Ph.D. in applied mathematics from the massachusettsstitute of technology.

• Eduardo S. is a lead applied scientist at Amazon. He has over 15 years of experience in applying machine learning to solve complex problems in e-commerce, genomics, natural language processing, and other domains. He holds a Ph.D. in applied mathematics from the massachusettsstitute of technology.

• Hector P. is a lead applied scientist at Amazon. He has over 15 years of experience in applying machine learning to solve complex problems in e-commerce, genomics, natural language processing, and other domains. He holds a Ph.D. in applied mathematics from the massachusettsstitute of technology.

• Tina J. is a lead applied scientist at Amazon. She has over 10 years of experience in applying machine learning to solve complex problems in e-commerce, genomics, natural language processing, and other domains. She holds a Ph.D. in applied mathematics from the massachusettsstitute of technology.

• Eduardo S. is a lead applied scientist at Amazon. He has over 15 years of experience in applying machine learning to solve complex problems in e-commerce, genics natural language processing, and other domains. He holds a Ph.D. in applied mathematics from the massachusettsstitute of technology.

• Hector P. is a lead applied scientist at Amazon. He has over 15 years of experience in applying machine learning to solve complex problems in e-commerce, genics natural language processing, and other domains. He holds a Ph.D. in applied mathematics from the massachusettsstitute of technology.

• Tina J. is a lead applied scientific at Amazon. She has over 10 years of experience in applying machine learning to solve complex problems in e-commerce, genics natural language processing, and other domains. She holds a Ph.D. in applied mathematics from the massachusettsstitute of technology.

• Eduardo S. is a lead applied scientific at Amazon. He has over 15 years of experience in applying machine learning to solve complex problems in e-commerce, genics natural language processing, and other domains. He holds a Ph.D. in applied mathematics from the massachusettsstitute of technology.

• Hector P. is a lead applied scientific at Amazon. He has over 15 years of experience in applying machine learning to solve complex problems in e-commerce, genics natural language processing, and other domains. He holds a Ph.D. in applied mathematics from the massachusettsstitute of technology.

• Tina J. is a lead applied scientific at Amazon. She has over 10 years of experience in applying machine learning to solve complex problems in e-commerce, genics natural language processing, and other domains. She holds a Ph.D. in applied mathematics from the massachusettsstitute of])**.

• Eduardo S. is a lead applied scientific at Amazon. He has over 15 years of experience in applying machine learning to solve complex problems in e-commerce, genics natural language processing, and other domains. He holds a Ph.D. in applied mathematics from the massachusettsstitute of])**.

• Hector P. is a lead applied scientific at Amazon. He has over 15 years of experience in applying machine learning to solve complex problems in e-commerce, genics natural language processing, and other domains. He holds a Ph.D. in applied mathematics from the massachusettsstitute of])**.

• Tina J. is a lead applied scientific at Amazon. She has over 10 years of experience in applying machine learning to solve complex problems in e-commerce, genics natural language processing, and other domains. She holds a Ph.D. in applied mathematics from the massachusettsstitute of])**.

• Eduardo S. is a lead applied scientific at Amazon. He has over 1)**.

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