{"@context":"https://schema.org","@type":"NewsArticle","generatedAt":"2026-09-21T12:01:17.960Z","headline":"Baseten 工程师解析 LLM 推理的有效边界与两类优化技术","description":"Baseten 工程师 Philip Kiely 撰文解析 LLM 推理的\"有效边界\"框架，将推理技术分为两类：在延迟与吞吐之间做取舍以命中边界上某一点的技术，以及把整条边界外推、创造整体效率提升的技术。","url":"https://www.aioga.com/news/cmtjt4sc8039jroiyz6awpmd5/","mainEntityOfPage":"https://www.aioga.com/news/cmtjt4sc8039jroiyz6awpmd5/","datePublished":"2026-09-02T07:29:52.000Z","dateModified":"2026-09-02T07:29:52.000Z","inLanguage":"zh-CN","publisher":{"@type":"NewsMediaOrganization","name":"Aioga","url":"https://www.aioga.com"},"citation":["https://www.baseten.co/blog/the-efficient-frontier-of-llm-inference","https://aihot.virxact.com/items/cmtjt4sc8039jroiyz6awpmd5"],"canonicalUrl":"https://www.aioga.com/news/cmtjt4sc8039jroiyz6awpmd5/","directAnswer":{"@type":"Answer","text":"Baseten 工程师 Philip Kiely 介绍 LLM 推理的“有效边界”框架，将优化技术分为两类：在延迟与吞吐之间权衡以移动部署位置，或推动整体边界外移以提升效率。","url":"https://www.aioga.com/news/cmtjt4sc8039jroiyz6awpmd5/","dateCreated":"2026-09-02T07:29:52.000Z","author":{"@type":"Organization","@id":"https://www.aioga.com/authors/aioga-editorial/#editorial-team","name":"Aioga Editorial Team","url":"https://www.aioga.com/authors/aioga-editorial/"}},"evidence":[{"@type":"CreativeWork","name":"baseten.co source article","url":"https://www.baseten.co/blog/the-efficient-frontier-of-llm-inference","datePublished":"2026-09-02T07:29:52.000Z","provider":{"@type":"Organization","name":"baseten.co","url":"https://www.baseten.co/blog/the-efficient-frontier-of-llm-inference"}},{"@type":"CreativeWork","name":"AIHot archive record","url":"https://aihot.virxact.com/items/cmtjt4sc8039jroiyz6awpmd5","datePublished":"2026-09-02T07:29:52.000Z","provider":{"@type":"Organization","name":"AIHot","url":"https://aihot.virxact.com/items/cmtjt4sc8039jroiyz6awpmd5"}}],"aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","originalPublisher":{"name":"baseten.co","url":"https://www.baseten.co/blog/the-efficient-frontier-of-llm-inference"},"geoDeepAnswer":null,"article":{"id":"cmtjt4sc8039jroiyz6awpmd5","slug":"cmtjt4sc8039jroiyz6awpmd5","url":"https://www.aioga.com/news/cmtjt4sc8039jroiyz6awpmd5/","title":"Baseten 工程师解析 LLM 推理的有效边界与两类优化技术","title_en":"","summary":"Baseten 工程师 Philip Kiely 撰文解析 LLM 推理的\"有效边界\"框架，将推理技术分为两类：在延迟与吞吐之间做取舍以命中边界上某一点的技术，以及把整条边界外推、创造整体效率提升的技术。","source":"Hacker News 热门（buzzing.cc 中文翻译）","sourceUrl":"https://www.baseten.co/blog/the-efficient-frontier-of-llm-inference","aiHotUrl":"https://aihot.virxact.com/items/cmtjt4sc8039jroiyz6awpmd5","publishedAt":"2026-09-02T07:29:52.000Z","category":"行业动态","score":58,"selected":false,"articleBody":["Inference techniques either move a deployment along the latency–throughput frontier or push the entire frontier out, creating more efficiency to allocate.","In the AI industry, we borrowed the term “efficient frontier” from economists. We use it to talk about managing tradeoffs, most often the tradeoff between cost and capabilities for models. A model is a “frontier model” if it offers the highest degree of intelligence at a given cost or size.","We also have efficient frontiers in inference engineering. Most often, this is expressed as a tradeoff between latency and throughput (which determines cost), though we can also exchange quality for throughput (via quantization, distillation, and pruning) or intelligence for speed (in the form of reasoning level).","There are two types of techniques available to inference engineers:","Techniques which make a tradeoff between two factors to move a deployment along an efficient frontier.","Techniques which push out the entire frontier for a given deployment, creating more overall efficiency which can be allocated to whatever outcome is most beneficial.","Both types of techniques are valuable.","It’s useful to be able to target any point along an efficient frontier by making tradeoffs. Giving up per-user speed makes it possible to build high-throughput, low-cost pipelines for batch workloads. Sacrificing throughput to improve speed makes sense when latency-sensitive users have a high willingness to pay.","And of course, it’s incredibly useful to push out the entire frontier. Unlocking more efficiency creates gains that can be allocated to lower latency, higher throughput, or a combination of the two.","This article details which inference engineering techniques let you target a point on the frontier, and which techniques push the entire frontier out. For this article, we’ll assume we’re running an LLM like GLM-5.3：/library/glm-53/ or Kimi K3：/library/kimi-k3/ for agentic coding with KV cache reuse enabled and optimal KV-aware routing.","Hitting a certain target in production is often less about discovering some novel approach and more about finding the right set of configurations given the nature of the traffic.","In practice, the efficient frontier is very jagged. Rather than a smooth, continuous line between outcomes, small changes can have big impacts. These cutoff points are often unintuitive and must be discovered empirically through sweeps.","The most obvious tradeoff between latency and throughput comes from batch sizing. A batch is the number of requests that are processed concurrently. While token-level continuous batching means that there isn’t any latency from waiting for batches to start, the configured batch size determines the per-user latency and the overall throughput.","With small batch sizes, per-user latency is excellent, but few total tokens are generated per GPU. This means the cost per token is quite high. Increasing batch size has the opposite effect: worse per-user latencies, better overall throughput for lower cost.","Today’s LLMs measure in the hundreds of billions or trillions of parameters and must be spread across multiple GPUs. The way in which they are shared, or parallelized, across GPUs can boost either latency or throughput.","For latency-sensitive deployments, focus on increasing Tensor Parallelism (TP). While TP has expensive all-to-all communication, it is effective for lowering latencies as these operations are fast over high-bandwidth NVLink interconnects.","Expert Parallelism (EP) can help with both latency and throughput. A lower degree of EP is often associated with better latencies, while wide EP, including EP across a full rack of GPUs, generally supports higher throughput.","Another parallelism technique for improving throughput is Attention Data Parallelism (ADP). This technique replicates attention layers for parallel computation, which boosts system throughput at the expense of per-request speed.","Quantization, or running a model with a lower level of precision in weights, activations, and/or KV cache values, improves both latency and throughput. A quantized model pushes out the efficient frontier on serving tradeoffs.","However, quantization introduces a new set of tradeoffs between quality and serving efficiency. This is a particularly jagged frontier, where a large degree of improvement to serving efficiency is possible with little-to-no reduction in model quality, especially when using microscaling floating-point number formats like MXFP4 and NVFP4.","These techniques are the ones that make the headlines. Improving overall performance is the most fun part of inference engineering.","The best part is that these techniques often compound. For example, doubling performance from better hardware while also doubling performance from better software means a four times improvement in overall serving, which can be allocated across latency and throughput.","A CUDA kernel is a low-level function that executes a single piece of the inference process, like a matrix multiplication. Improving the performance of individual kernels, as well as the end-to-end performance of a forward pass in the inference engine, means fewer resources are needed to generate each token. These efficiency gains compound throughout the stack and push the frontier of performance.","For more on kernel-level performance, read this excellent writeup by Baseten intern Brian Li：/blog/agentic-kernels-in-production/.","Speculative decoding is the process of guessing which tokens a model might generate, then validating those guesses. When speculative decoding was new, this posed a tradeoff between latency and throughput: speculation was expensive, sequence lengths were short, and acceptance rates were low, meaning speculative decoding was only feasible at small batch sizes.","Today, techniques like EAGLE-3：/blog/how-to-train-custom-eagle-3-heads-for-speculative-decoding/, DSpark, and DFlash：/blog/dflash-faster-llm-inference/ still compete with the main model loop for resources, somewhat limiting maximum batch sizes. However, thanks to the strong performance of these techniques, especially on code generation where output token sequences are relatively predictable, they yield efficiency gains from skipped forward passes in addition to the raw reduction in latency in the form of more tokens per second per user.","P/D disaggregation, or separating prefill and decode onto dedicated workers, is a strategy for optimizing high-volume deployments of LLMs. Running prefill and decode independently means that workers can be optimized for the unique characteristics of each phase of inference, and that the ratio between prefill and decode workers can be adjusted to match the input and output sequence lengths and cache hit rates from incoming traffic.","This article provided a basic overview of techniques for managing tradeoffs versus techniques for improving systemwide performance. For more detail on every technique mentioned in this article, read my free book Inference Engineering ：https://www.baseten.co/inference-engineering/.","Stay up to date on model performance, inference infrastructure, and more.","Dedicated Inference：/products/dedicated-inference/","Model Labs：/products/baseten-for-model-labs/","Model Runtimes：/platform/model-performance/","Infrastructure：/platform/cloud-native-infrastructure/","Multi-cloud：/products/multi-cloud-capacity-management/","Self-hosted：/deployments/baseten-self-hosted/","Hybrid：/deployments/baseten-hybrid/","Forward deployed engineers：/platform/embedded-engineering/","Transcription：/solutions/transcription/","Image Generation：/solutions/image-generation/","Text-to-speech：/solutions/text-to-speech/","Large language models：/solutions/llms/","Compound AI：/solutions/compound-ai/","Documentation：https://docs.baseten.co/","Savings calculator：/resources/calculator/","Trust Center：https://trust.baseten.co","Terms and Conditions：/terms-and-conditions/","Service Level Agreement：/service-level-agreement/","GLM-5.3-Flash：/library/glm-53-flash/","DeepSeek V4 Pro 0813：/library/deepseek-v4-pro-0813/"],"articleImages":[{"sourceUrl":"https://www.baseten.co/_next/image/?url=https%3A%2F%2Fwww.datocms-assets.com%2F104802%2F1788306398-efficientfrontierblogheader.png%3Fauto%3Dformat%26fit%3Dcrop%26h%3D630%26w%3D1200&w=3840&q=100","alt":"The efficient frontier of LLM inference","afterParagraph":0,"url":"/media/articles/cmtjt4sc8039jroiyz6awpmd5/2d9ba42c5277a579.avif"},{"sourceUrl":"https://www.baseten.co/_next/image/?url=https%3A%2F%2Fwww.datocms-assets.com%2F104802%2F1788302077-efficient-frontier-image4.png%3Fauto%3Dformat%26w%3D1200&w=3840&q=75","alt":"An efficient frontier shows the range of optimal combinations when trading off between two valuable outcomes in a resource-constrained environment.","afterParagraph":1,"url":"/media/articles/cmtjt4sc8039jroiyz6awpmd5/ff1ff97e6abed0cc.avif"},{"sourceUrl":"https://www.baseten.co/_next/image/?url=https%3A%2F%2Fwww.datocms-assets.com%2F104802%2F1788302085-efficient-frontier-image5.png%3Fauto%3Dformat%26w%3D1200&w=3840&q=75","alt":"Techniques for managing tradeoffs let you target an outcome along an efficient frontier.","afterParagraph":10,"url":"/media/articles/cmtjt4sc8039jroiyz6awpmd5/886ba0ea6e57b090.avif"},{"sourceUrl":"https://www.baseten.co/_next/image/?url=https%3A%2F%2Fwww.datocms-assets.com%2F104802%2F1788302093-efficient-frontier-image6.png%3Fauto%3Dformat%26w%3D1200&w=3840&q=75","alt":"Parallelism splits large models across multiple GPUs.","afterParagraph":14,"url":"/media/articles/cmtjt4sc8039jroiyz6awpmd5/d71896ea2b5f9949.avif"},{"sourceUrl":"https://www.baseten.co/_next/image/?url=https%3A%2F%2Fwww.datocms-assets.com%2F104802%2F1788302101-efficient-frontier-image1.png%3Fauto%3Dformat%26w%3D1200&w=3840&q=75","alt":"Techniques for pushing out the frontier create universal gains.","afterParagraph":20,"url":"/media/articles/cmtjt4sc8039jroiyz6awpmd5/3a6f1b5b1df80d7e.avif"},{"sourceUrl":"https://www.baseten.co/_next/image/?url=https%3A%2F%2Fwww.datocms-assets.com%2F104802%2F1788302109-efficient-frontier-image2.png%3Fauto%3Dformat%26w%3D1200&w=3840&q=75","alt":"In practice, disaggregation is often most useful for increasing throughput while keeping latencies the same or slightly better.","afterParagraph":26,"url":"/media/articles/cmtjt4sc8039jroiyz6awpmd5/2348debdddd0f8e4.avif"}],"mediaStatus":"ok","articleBodyZh":["推理技术要么沿着延迟-吞吐量边界推进部署，要么将整个边界推向外面，从而提高分配效率。","在人工智能行业，我们借用了经济学家的“高效前沿”一词。我们用它来谈论管理权衡，通常是模型成本与能力之间的权衡。如果一个模型在一定成本或规模下提供了最高程度的智能，则称为“前沿模型”。","我们在推理工程领域也有高效的前沿。通常，这表现为延迟与吞吐量之间的权衡（决定成本），当然我们也可以用质量换取吞吐量（通过量化、蒸馏和修剪），或用智能换取速度（以推理水平的形式）。","推理工程师可以使用两种技术：","在两个因素之间做出权衡的技术，推动部署沿高效前沿推进。","这些技术能够在特定部署中突破整个前沿，创造更高的整体效率，并可分配给最有利的结果。","这两种类型的技术都是有价值的。","能够通过权衡来精准定位高效前沿的任何点是很有用的。放弃每个用户的速度，使得为批处理工作负载构建高通量、低成本的流水线成为可能。当对延迟敏感的用户愿意支付时，牺牲吞吐量以提升速度是合理的。","当然，推动整个前沿也非常有用。释放更高效率带来的收益可以分配给更低延迟、更高吞吐量，或两者结合。","本文详细介绍了哪些推理工程技术允许你锁定前沿上的某个点，哪些技术将整个前沿推开。本文假设我们运行的是像 GLM-5.3：/library/glm-53/ 或 Kimi K3：/library/kimi-k3/ 这样的大型语言模型，用于agentic编码，启用 KV 缓存重用并实现最佳 KV 感知路由。","在生产环境中达到某个目标，往往不是发现某种新方法，而是根据流量的性质找到合适的配置组合。","实际上，高效的边界非常锯齿状。结果之间没有一条平滑、连续的线，小的改变可能带来巨大影响。这些截断点往往不直观，必须通过实证扫描来发现。","延迟与吞吐量之间最明显的权衡来自批量大小。批处理是指同时处理的请求数量。虽然令牌级连续批处理意味着等待批次开始时没有延迟，但配置的批处理大小决定了每用户的延迟和整体吞吐量。","在小批量时，每用户延迟非常优异，但每个GPU产生的总令牌数较少。这意味着每个令牌的成本相当高。增加批次规模则产生相反效果：每用户延迟变差，整体吞吐量更好，成本更低。","如今的大型语言模型参数达数千亿甚至数万亿，必须分布在多个GPU上。它们在GPU间的共享或并行化方式可以提升延迟或吞吐量。","对于延迟敏感的部署，重点提升张量并行性（TP）。虽然TP的全对全通信成本较高，但由于这些操作在高带宽NVLink互连上速度快，有助于降低延迟。","专家并行（EP）可以帮助提升延迟和吞吐量。较低的 EP 通常伴随着更好的延迟，而宽 EP，包括跨整机架 GPU 的 EP，通常支持更高的吞吐量。","另一种提升吞吐量的并行技术是注意力数据并行（ADP）。该技术复制注意力层以实现并行计算，从而提升系统吞吐量，但牺牲了每次请求的速度。","量化，即在权重、激活和/或KV缓存值上运行较低精度的模型，既能提升延迟，也能提升吞吐量。量化模型推动了服务权衡的高效边界。","然而，量化引入了一组新的质量与服务效率之间的权衡。这是一个特别崎岖的前沿领域，在使用像 MXFP4 和 NVFP4 这样的微缩浮点数格式时，有可能在几乎不降低模型质量的情况下大幅提升服务效率。","这些技术是那些占据头条的技术。提升整体性能是推理工程中最有趣的部分。","最棒的部分是这些技术通常是可以叠加的。例如，通过更好的硬件使性能翻倍，同时通过更好的软件也使性能翻倍，意味着整体服务性能提升四倍，这可以在延迟和吞吐量之间分配。","CUDA 内核是一个低级函数，执行推理过程中的单个部分，例如矩阵乘法。提升单个内核的性能，以及推理引擎中前向传递的端到端性能，意味着生成每个 token 所需的资源更少。这些效率提升会在整个技术栈中叠加，并推动性能前沿的发展。","关于内核级性能的更多内容，请阅读 Baseten 实习生 Brian Li 的这篇优秀文章：/blog/agentic-kernels-in-production/。","推测解码是猜测模型可能生成的 token，然后验证这些猜测的过程。当推测解码刚出现时，这在延迟和吞吐量之间带来了权衡：推测很昂贵，序列长度很短，接受率很低，这意味着推测解码仅在小批量下可行。","如今，像 EAGLE-3：/blog/how-to-train-custom-eagle-3-heads-for-speculative-decoding/、DSpark 和 DFlash：/blog/dflash-faster-llm-inference/ 等技术仍然与主模型循环争夺资源，某种程度上限制了最大批量大小。然而，得益于这些技术的强大性能，尤其是在输出 token 序列相对可预测的代码生成中，它们不仅通过跳过前向传递带来效率提升，还通过每个用户每秒更多 token 的形式减少了延迟。","P/D 分离，或者将预填充（prefill）和解码（decode）分配到专用工作节点，是优化大规模 LLM 部署的一种策略。独立运行预填充和解码意味着工作节点可以针对推理每个阶段的独特特性进行优化，并且可以根据输入和输出序列长度以及来自流量的缓存命中率调整预填充和解码工作节点的比例。","本文提供了关于管理权衡技巧与提升系统整体性能技巧的基本概述。想要了解本文中提到的每种技术的更多细节，请阅读我的免费书籍《推理工程》：https://www.baseten.co/inference-engineering/。","随时了解模型性能、推理基础设施等信息。","专用推理：/products/dedicated-inference/","Model Labs：/products/baseten-for-model-labs/","模型运行时：/platform/model-performance/","基础设施：/平台/云原生基础设施/","多云：/products/multi-cloud-capacity-management/","自宿主：/deployments/baseten-self-hosted/","混合：/deployments/baseten-hybrid/","前置部署工程师：/平台/嵌入式工程/","转录：/solutions/transcription/","图像生成：/解决方案/图像生成/","文本转语音：/solutions/text-to-speech/","大型语言模型：/solutions/llms/","复合人工智能：/solutions/compound-ai/","文档：https://docs.baseten.co/","储蓄计算器：/resources/calculator/","信任中心：https://trust.baseten.co","条款和条件：/terms-and-conditions/","服务等级协议：/service-level-agreement/","GLM-5.3-Flash：/library/glm-53-flash/","DeepSeek V4 Pro 0813：/library/deepseek-v4-pro-0813/"],"translationStatus":"translated","bodyOrigin":"source-page","editorial":{"summary":"Baseten 工程师 Philip Kiely 介绍 LLM 推理的“有效边界”框架，将优化技术分为两类：在延迟与吞吐之间权衡以移动部署位置，或推动整体边界外移以提升效率。","background":"来源将推理中的有效边界主要描述为延迟与吞吐的权衡，吞吐又影响成本。质量与吞吐、智能程度与速度之间，也可通过量化、蒸馏、剪枝和推理级别进行取舍。","viewpoint":"Aioga 判断：该框架的价值在于区分局部取舍与整体效率提升，便于编辑和工程团队分别审视部署目标与优化结果，但来源未说明具体技术方案的性能幅度。","implications":"可能影响：不同工作负载可能需要选择有效边界上的不同位置；批处理可接受较低的单用户速度以换取吞吐，延迟敏感场景则可能重视速度。上述取舍不代表固定的最优方案。","nextStep":"后续观察：需要继续核对文章是否给出具体推理技术、测量方法和部署数据，并关注其如何区分移动边界与外推边界，避免将框架描述误读为确定性性能结论。","evidenceRefs":["title","summary","articleBody","source"],"status":"published","aiGenerated":true,"autoApproved":true,"generatedBy":"aioga-editorial:gpt-5.6-sol","reviewedBy":"aioga-editorial-review:gpt-5.6-sol","generatedAt":"2026-09-02T08:29:14.838Z","sourceHash":"5cb52a8008e1b734","review":{"approved":true,"groundedness":94,"clarity":92,"duplicationRisk":15,"blockingIssues":[],"notes":["“便于编辑和工程团队分别审视部署目标与优化结果”属于基于来源框架的合理判断，已明确标注为观点，不构成事实性断言。","可将“吞吐又影响成本”改为“吞吐通常会影响成本”，以更贴近来源中的表述，但不影响事实准确性。"]},"validation":{"passed":true,"mode":"ai-auto","revisions":0,"checks":["schema","length","source-attribution","editorial-labels","inference-boundary","low-source-overlap","no-html","independent-ai-review"]}},"tags":["行业动态","Hacker News 热门（buzzing.cc 中文翻译）"],"translations":{"zh-CN":{"title":"Baseten 工程师解析 LLM 推理的有效边界与两类优化技术","summary":"Baseten 工程师 Philip Kiely 撰文解析 LLM 推理的\"有效边界\"框架，将推理技术分为两类：在延迟与吞吐之间做取舍以命中边界上某一点的技术，以及把整条边界外推、创造整体效率提升的技术。","category":"行业动态","source":"baseten.co","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Baseten 工程师解析 LLM 推理的有效边界与两类优化技术 - Aioga AI资讯","description":"Baseten 工程师 Philip Kiely 撰文解析 LLM 推理的\"有效边界\"框架，将推理技术分为两类：在延迟与吞吐之间做取舍以命中边界上某一点的技术，以及把整条边界外推、创造整体效率提升的技术。","url":"https://www.aioga.com/news/cmtjt4sc8039jroiyz6awpmd5/","articleBody":["推理技术要么沿着延迟-吞吐量边界推进部署，要么将整个边界推向外面，从而提高分配效率。","在人工智能行业，我们借用了经济学家的“高效前沿”一词。我们用它来谈论管理权衡，通常是模型成本与能力之间的权衡。如果一个模型在一定成本或规模下提供了最高程度的智能，则称为“前沿模型”。","我们在推理工程领域也有高效的前沿。通常，这表现为延迟与吞吐量之间的权衡（决定成本），当然我们也可以用质量换取吞吐量（通过量化、蒸馏和修剪），或用智能换取速度（以推理水平的形式）。","推理工程师可以使用两种技术：","在两个因素之间做出权衡的技术，推动部署沿高效前沿推进。","这些技术能够在特定部署中突破整个前沿，创造更高的整体效率，并可分配给最有利的结果。","这两种类型的技术都是有价值的。","能够通过权衡来精准定位高效前沿的任何点是很有用的。放弃每个用户的速度，使得为批处理工作负载构建高通量、低成本的流水线成为可能。当对延迟敏感的用户愿意支付时，牺牲吞吐量以提升速度是合理的。","当然，推动整个前沿也非常有用。释放更高效率带来的收益可以分配给更低延迟、更高吞吐量，或两者结合。","本文详细介绍了哪些推理工程技术允许你锁定前沿上的某个点，哪些技术将整个前沿推开。本文假设我们运行的是像 GLM-5.3：/library/glm-53/ 或 Kimi K3：/library/kimi-k3/ 这样的大型语言模型，用于agentic编码，启用 KV 缓存重用并实现最佳 KV 感知路由。","在生产环境中达到某个目标，往往不是发现某种新方法，而是根据流量的性质找到合适的配置组合。","实际上，高效的边界非常锯齿状。结果之间没有一条平滑、连续的线，小的改变可能带来巨大影响。这些截断点往往不直观，必须通过实证扫描来发现。","延迟与吞吐量之间最明显的权衡来自批量大小。批处理是指同时处理的请求数量。虽然令牌级连续批处理意味着等待批次开始时没有延迟，但配置的批处理大小决定了每用户的延迟和整体吞吐量。","在小批量时，每用户延迟非常优异，但每个GPU产生的总令牌数较少。这意味着每个令牌的成本相当高。增加批次规模则产生相反效果：每用户延迟变差，整体吞吐量更好，成本更低。","如今的大型语言模型参数达数千亿甚至数万亿，必须分布在多个GPU上。它们在GPU间的共享或并行化方式可以提升延迟或吞吐量。","对于延迟敏感的部署，重点提升张量并行性（TP）。虽然TP的全对全通信成本较高，但由于这些操作在高带宽NVLink互连上速度快，有助于降低延迟。","专家并行（EP）可以帮助提升延迟和吞吐量。较低的 EP 通常伴随着更好的延迟，而宽 EP，包括跨整机架 GPU 的 EP，通常支持更高的吞吐量。","另一种提升吞吐量的并行技术是注意力数据并行（ADP）。该技术复制注意力层以实现并行计算，从而提升系统吞吐量，但牺牲了每次请求的速度。","量化，即在权重、激活和/或KV缓存值上运行较低精度的模型，既能提升延迟，也能提升吞吐量。量化模型推动了服务权衡的高效边界。","然而，量化引入了一组新的质量与服务效率之间的权衡。这是一个特别崎岖的前沿领域，在使用像 MXFP4 和 NVFP4 这样的微缩浮点数格式时，有可能在几乎不降低模型质量的情况下大幅提升服务效率。","这些技术是那些占据头条的技术。提升整体性能是推理工程中最有趣的部分。","最棒的部分是这些技术通常是可以叠加的。例如，通过更好的硬件使性能翻倍，同时通过更好的软件也使性能翻倍，意味着整体服务性能提升四倍，这可以在延迟和吞吐量之间分配。","CUDA 内核是一个低级函数，执行推理过程中的单个部分，例如矩阵乘法。提升单个内核的性能，以及推理引擎中前向传递的端到端性能，意味着生成每个 token 所需的资源更少。这些效率提升会在整个技术栈中叠加，并推动性能前沿的发展。","关于内核级性能的更多内容，请阅读 Baseten 实习生 Brian Li 的这篇优秀文章：/blog/agentic-kernels-in-production/。","推测解码是猜测模型可能生成的 token，然后验证这些猜测的过程。当推测解码刚出现时，这在延迟和吞吐量之间带来了权衡：推测很昂贵，序列长度很短，接受率很低，这意味着推测解码仅在小批量下可行。","如今，像 EAGLE-3：/blog/how-to-train-custom-eagle-3-heads-for-speculative-decoding/、DSpark 和 DFlash：/blog/dflash-faster-llm-inference/ 等技术仍然与主模型循环争夺资源，某种程度上限制了最大批量大小。然而，得益于这些技术的强大性能，尤其是在输出 token 序列相对可预测的代码生成中，它们不仅通过跳过前向传递带来效率提升，还通过每个用户每秒更多 token 的形式减少了延迟。","P/D 分离，或者将预填充（prefill）和解码（decode）分配到专用工作节点，是优化大规模 LLM 部署的一种策略。独立运行预填充和解码意味着工作节点可以针对推理每个阶段的独特特性进行优化，并且可以根据输入和输出序列长度以及来自流量的缓存命中率调整预填充和解码工作节点的比例。","本文提供了关于管理权衡技巧与提升系统整体性能技巧的基本概述。想要了解本文中提到的每种技术的更多细节，请阅读我的免费书籍《推理工程》：https://www.baseten.co/inference-engineering/。","随时了解模型性能、推理基础设施等信息。","专用推理：/products/dedicated-inference/","Model 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techniques that extrapolate the entire boundary to create overall efficiency improvements.","category":"Industry","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Baseten Engineers Analyze Effective Boundaries in LLM Inference and Two Types of Optimization Techniques - Aioga AI News","description":"Baseten engineer Philip Kiely wrote about the 'effective boundary' framework for LLM inference, categorizing inference techniques into two types: techniques that trade off latency...","url":"https://www.aioga.com/en/news/cmtjt4sc8039jroiyz6awpmd5/","contentTranslated":true,"sourceHash":"638f790adf42e2ba","translatedAt":"2026-09-02T08:25:38.641Z"},"ja":{"title":"BasetenエンジニアがLLM推論の有効境界と2種類の最適化技術を解析","summary":"BasetenのエンジニアPhilip Kielyは、LLM推論の「有効境界」フレームワークを解説。推論技術を2種類に分類しました：遅延とスループットの間でトレードオフを行い境界上のある点を狙う技術、および境界全体を外挿して全体効率を向上させる技術。","category":"業界動向","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 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기술.","url":"https://www.aioga.com/ko/news/cmtjt4sc8039jroiyz6awpmd5/","contentTranslated":true,"sourceHash":"638f790adf42e2ba","translatedAt":"2026-09-02T08:25:43.873Z"},"es":{"title":"Ingenieros de Baseten analizan los límites efectivos de la inferencia LLM y dos tipos de técnicas de optimización","summary":"El ingeniero de Baseten, Philip Kiely, escribió un artículo analizando el marco de \"límite efectivo\" de la inferencia LLM, clasificando las técnicas de inferencia en dos tipos: aquellas que hacen concesiones entre latencia y rendimiento para alcanzar un punto específico del límite, y aquellas que extienden todo el límite para lograr una mejora de eficiencia general.","category":"Industria","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Ingenieros de Baseten analizan los límites efectivos de la inferencia LLM y dos tipos de técnicas de optimización - Aioga Noticias de IA","description":"El ingeniero de Baseten, Philip 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fazem trade-off entre latência e taxa de transferência para atingir um ponto dentro do limite, e técnicas que extrapolam todo o limite, criando um aumento geral de eficiência.","category":"行业动态","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Engenheiros da Baseten analisam os limites efetivos da inferência LLM e dois tipos de técnicas de otimização - Aioga Notícias de IA","description":"O engenheiro da Baseten, Philip Kiely, publicou um artigo analisando a estrutura de \"limites efetivos\" da inferência LLM, dividindo as técnicas de inferência em duas categorias: té...","url":"https://www.aioga.com/pt-BR/news/cmtjt4sc8039jroiyz6awpmd5/","contentTranslated":true,"sourceHash":"638f790adf42e2ba","translatedAt":"2026-09-02T08:25:56.082Z"},"ru":{"title":"Инженеры Baseten анализируют эффективные границы вывода LLM и два типа оптимизационных технологий","summary":"Инженер Baseten Филип Кайли написал статью, разъясняющую рамки \"эффективных границ\" вывода LLM, разделяя методы вывода на два типа: технологии, которые идут на компромисс между задержкой и пропускной способностью, чтобы попасть в определенную точку границы, и технологии, которые прокладывают всю границу и создают улучшение общей эффективности.","category":"行业动态","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Инженеры Baseten анализируют эффективные границы вывода LLM и два типа оптимизационных технологий - Aioga Новости ИИ","description":"Инженер Baseten Филип Кайли написал статью, разъясняющую рамки \"эффективных границ\" вывода LLM, разделяя методы вывода на два типа: технологии, которые идут на компромисс между зад...","url":"https://www.aioga.com/ru/news/cmtjt4sc8039jroiyz6awpmd5/","contentTranslated":true,"sourceHash":"638f790adf42e2ba","translatedAt":"2026-09-02T08:25:56.781Z"},"ar":{"title":"مهندسو Baseten يوضحون الحدود الفعالة لاستدلال LLM ونوعين من تقنيات التحسين","summary":"كتب مهندس Baseten، فيليب كيلي، مقالة تشرح إطار \"الحدود الفعالة\" لاستدلال LLM، حيث صنف تقنيات الاستدلال إلى نوعين: تقنيات لتحقيق نقطة معينة على الحدود من خلال موازنة بين التأخير والإنتاجية، وتقنيات لتوسيع كامل الحدود وخلق تحسين كلي في الكفاءة.","category":"行业动态","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"مهندسو Baseten يوضحون الحدود الفعالة لاستدلال LLM ونوعين من تقنيات التحسين - Aioga أخبار الذكاء الاصطناعي","description":"كتب مهندس Baseten، فيليب كيلي، مقالة تشرح إطار \"الحدود الفعالة\" لاستدلال LLM، حيث صنف تقنيات الاستدلال إلى نوعين: تقنيات لتحقيق نقطة معينة على الحدود من خلال موازنة بين التأخير وال...","url":"https://www.aioga.com/ar/news/cmtjt4sc8039jroiyz6awpmd5/","contentTranslated":true,"sourceHash":"638f790adf42e2ba","translatedAt":"2026-09-02T08:26:02.930Z"},"hi":{"title":"Baseten इंजीनियरों ने LLM इन्फरेंस की प्रभावी सीमाओं और दो प्रकार की ऑप्टिमाइजेशन तकनीकों का विश्लेषण किया","summary":"Baseten इंजीनियर Philip Kiely ने LLM इन्फरेंस के 'प्रभावी सीमा' फ्रेमवर्क का विश्लेषण करते हुए लिखा, इन्फरेंस तकनीकों को दो प्रकारों में विभाजित किया: एक, विलंब और थ्रूपुट के बीच समझौता करते हुए सीमा पर किसी बिंदु को लक्षित करने वाली तकनीकें, और दो, पूरी सीमा का विस्तार करके कुल दक्षता में सुधार करने वाली तकनीकें।","category":"行业动态","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Baseten इंजीनियरों ने LLM इन्फरेंस की प्रभावी सीमाओं और दो प्रकार की ऑप्टिमाइजेशन तकनीकों का विश्लेषण किया - Aioga AI समाचार","description":"Baseten इंजीनियर Philip Kiely ने LLM इन्फरेंस के 'प्रभावी सीमा' फ्रेमवर्क का विश्लेषण करते हुए लिखा, इन्फरेंस तकनीकों को दो प्रकारों में विभाजित किया: एक, विलंब और थ्रूपुट के बीच स...","url":"https://www.aioga.com/hi/news/cmtjt4sc8039jroiyz6awpmd5/","contentTranslated":true,"sourceHash":"638f790adf42e2ba","translatedAt":"2026-09-02T08:26:02.747Z"},"it":{"title":"Ingegneri di Baseten analizzano i confini efficaci e due tipi di tecniche di ottimizzazione del ragionamento LLM","summary":"L'ingegnere di Baseten Philip Kiely ha scritto un articolo analizzando il framework dei 'confini efficaci' del ragionamento LLM, dividendo le tecniche di inferenza in due categorie: quelle che bilanciano latenza e throughput per colpire un punto specifico sul confine, e quelle che estendono tutto il bordo per migliorare l'efficienza complessiva.","category":"行业动态","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Ingegneri di Baseten analizzano i confini efficaci e due tipi di tecniche di ottimizzazione del ragionamento LLM - Aioga Notizie IA","description":"L'ingegnere di Baseten Philip Kiely ha scritto un articolo analizzando il framework dei 'confini efficaci' del ragionamento LLM, dividendo le tecniche di inferenza in due categorie...","url":"https://www.aioga.com/it/news/cmtjt4sc8039jroiyz6awpmd5/","contentTranslated":true,"sourceHash":"638f790adf42e2ba","translatedAt":"2026-09-02T08:26:07.914Z"},"nl":{"title":"Baseten-ingenieur analyseert de effectieve grenzen van LLM-redenering en twee soorten optimalisatietechnieken","summary":"Baseten-ingenieur Philip Kiely heeft een artikel geschreven over het 'effectieve grenzen'-kader van LLM-redenering, waarbij redeneringstechnieken in twee categorieën worden verdeeld: technieken die een afweging maken tussen latentie en doorvoer om een punt op de grens te bereiken, en technieken die de hele grens extrapoleren om algemene efficiëntieverbetering te creëren.","category":"行业动态","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Baseten-ingenieur analyseert de effectieve grenzen van LLM-redenering en twee soorten optimalisatietechnieken - Aioga AI-nieuws","description":"Baseten-ingenieur Philip Kiely heeft een artikel geschreven over het 'effectieve grenzen'-kader van LLM-redenering, waarbij redeneringstechnieken in twee categorieën worden verdeel...","url":"https://www.aioga.com/nl/news/cmtjt4sc8039jroiyz6awpmd5/","contentTranslated":true,"sourceHash":"638f790adf42e2ba","translatedAt":"2026-09-02T08:26:08.364Z"},"tr":{"title":"Baseten Mühendisleri LLM Akıl Yürütme Sınırlarını ve İki Tür Optimizasyon Tekniğini Analiz Ediyor","summary":"Baseten mühendisi Philip Kiely, LLM akıl yürütmesinin \"etkin sınır\" çerçevesini yazdı; akıl yürütme tekniklerini iki kategoriye ayırdı: gecikme ve verim arasında ödün vererek sınırın belirli bir noktasına ulaşmayı hedefleyen teknikler ve tüm sınırı öteleyip genel verimlilik artışı yaratan teknikler.","category":"行业动态","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Baseten Mühendisleri LLM Akıl Yürütme Sınırlarını ve İki Tür Optimizasyon Tekniğini Analiz Ediyor - Aioga AI Haberleri","description":"Baseten mühendisi Philip Kiely, LLM akıl yürütmesinin \"etkin sınır\" çerçevesini yazdı; akıl yürütme tekniklerini iki kategoriye ayırdı: gecikme ve verim arasında ödün vererek sınır...","url":"https://www.aioga.com/tr/news/cmtjt4sc8039jroiyz6awpmd5/","contentTranslated":true,"sourceHash":"638f790adf42e2ba","translatedAt":"2026-09-02T08:26:14.041Z"},"vi":{"title":"Kỹ sư Baseten phân tích ranh giới hiệu quả của suy luận LLM và hai kỹ thuật tối ưu","summary":"Kỹ sư Baseten Philip Kiely viết bài phân tích khung 'ranh giới hiệu quả' của suy luận LLM, chia kỹ thuật suy luận thành hai loại: kỹ thuật đánh đổi giữa độ trễ và thông lượng để đạt một điểm trên ranh giới, và kỹ thuật mở rộng toàn bộ ranh giới, tạo ra cải thiện hiệu suất tổng thể.","category":"行业动态","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Kỹ sư Baseten phân tích ranh giới hiệu quả của suy luận LLM và hai kỹ thuật tối ưu - Tin tức AI Aioga","description":"Kỹ sư Baseten Philip Kiely viết bài phân tích khung 'ranh giới hiệu quả' của suy luận LLM, chia kỹ thuật suy luận thành hai loại: kỹ thuật đánh đổi giữa độ trễ và thông lượng để đạ...","url":"https://www.aioga.com/vi/news/cmtjt4sc8039jroiyz6awpmd5/","contentTranslated":true,"sourceHash":"638f790adf42e2ba","translatedAt":"2026-09-02T08:26:12.915Z"},"id":{"title":"Insinyur Baseten menganalisis batas efektif inferensi LLM dan dua jenis teknik optimasi","summary":"Insinyur Baseten Philip Kiely menulis analisis mengenai kerangka 'batas efektif' inferensi LLM, membagi teknik inferensi menjadi dua jenis: teknik yang menyeimbangkan antara latensi dan throughput untuk mencapai titik tertentu pada batas, dan teknik yang memperluas seluruh batas untuk menciptakan peningkatan efisiensi secara keseluruhan.","category":"行业动态","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Insinyur Baseten menganalisis batas efektif inferensi LLM dan dua jenis teknik optimasi - Berita AI Aioga","description":"Insinyur Baseten Philip Kiely menulis analisis mengenai kerangka 'batas efektif' inferensi LLM, membagi teknik inferensi menjadi dua jenis: teknik yang menyeimbangkan antara latens...","url":"https://www.aioga.com/id/news/cmtjt4sc8039jroiyz6awpmd5/","contentTranslated":true,"sourceHash":"638f790adf42e2ba","translatedAt":"2026-09-02T08:26:19.102Z"},"th":{"title":"วิศวกร Baseten วิเคราะห์ขอบเขตที่มีประสิทธิผลของการคาดเดา LLM และสองประเภทเทคนิคการปรับปรุง","summary":"วิศวกร Baseten Philip Kiely เขียนบทความวิเคราะห์กรอบ \"ขอบเขตที่มีประสิทธิผล\" ของการคาดเดา LLM แบ่งเทคนิคการคาดเดาเป็นสองประเภท: เทคนิคที่ต้องแลกเปลี่ยนระหว่างความล่าช้าและปริมาณข้อมูล เพื่อให้ตรงจุดบนขอบเขต และเทคนิคที่คาดการณ์ขอบเขตทั้งหมดเพื่อสร้างประสิทธิภาพโดยรวม","category":"行业动态","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"วิศวกร Baseten วิเคราะห์ขอบเขตที่มีประสิทธิผลของการคาดเดา LLM และสองประเภทเทคนิคการปรับปรุง - ข่าว AI Aioga","description":"วิศวกร Baseten Philip Kiely เขียนบทความวิเคราะห์กรอบ \"ขอบเขตที่มีประสิทธิผล\" ของการคาดเดา LLM แบ่งเทคนิคการคาดเดาเป็นสองประเภท: เทคนิคที่ต้องแลกเปลี่ยนระหว่างความล่าช้าและปริมาณข้อ...","url":"https://www.aioga.com/th/news/cmtjt4sc8039jroiyz6awpmd5/","contentTranslated":true,"sourceHash":"638f790adf42e2ba","translatedAt":"2026-09-02T08:26:20.344Z"},"pl":{"title":"Inżynierowie z Basthenn analizują efektywne granice rozumowania LLM oraz dwa typy technik optymalizacyjnych","summary":"Inżynier Basthenn, Philip Kelly, napisał analizę ram \"efektywnej granicy\" dla wnioskowania LLM, dzieląc techniki rozumowania na dwie kategorie: te, które dokonują kompromisu między opóźnieniem a ssakiem, by osiągnąć punkt na granicy, oraz te, które przesuwają całą granicę na zewnątrz, aby zwiększyć ogólną efektywność.","category":"行业动态","source":"Hacker News 热门（buzzing.cc 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