Llm prefill. , the pre-filling stage) on a single A100 GPU.

FlashInfer focus on LLM serving and inference, and delivers state-of-the-art performance across diverse scenarios. decoding phase handles the generation of subsequent tokens. It batches all pending decode requests to the batch before scheduling any prefill. To speed up the prefill of the long LLM inputs, one can pre-compute the KV cache of a text and re-use the KV cache when the context is reused as the prefix of another LLM input. With sufficiently large value of m, the Prefill Phase is more compute constrained than memory-bandwidth constrained. MI210 can provide a maximum double data rate (DDR) memory bandwidth up to 1. Jan 15, 2024 · A few LLM inference systems already include such a KV caching quantization feature. Splitwise: Efficient generative LLM inference using phase splitting: splitting prefill and decode in a map-reduce style, by UW and Microsoft DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving : also split the prefill and decode, accepted by OSDI'24 models (LLMs). During prefill, the system processes all the request’s input tokens to calculate intermediate states, which are crucial for building an overall contextual understanding of the request. TensorRT-LLM contains components to create Python and C++ runtimes that execute those TensorRT engines. This is partly based on the observation that prefill/decode may take different tensor shapes, prefill is compute-bound whereas decode is memory bound. KV-cache), from which point the iterative generation loop can begin LLM到惠入贰token曼腊（time to first token, TTFT [1]）憾环要朦洁瓤蓬、Prompt康慢、Batch Size、GPU傻狈玖朴镰缤潦。. --interactive-first: Run the program in interactive mode and wait for input Aug 6, 2023 · This helps guide the LLM to create a concise skeleton of the answer, from which B points are extracted. For longer input prompt lengths, prefilling will incur a significant overhead on decoding time. For GPT generative inference, we achieve speedups of 2. While the prefill phase effectively saturates GPU compute at small batch sizes, the decode phase results in low compute utilization as it generates one token at a time per request. Note that the generated key-value (KV) pairs Prefill phase# In the prefill phase, the model analyzes the user prompt to produce the initial output token. This is because the attention mechanism requires the whole input sequence to compute and create the so-called key-value cache (aka. vanilla Transformers), but decoder-only models (e. It helps achieve better GPU utilization by locating compute-bound (prefill) and memory-bound (decode) requests to the same batch. ,KVcache. II). However, a significant challenge arises due to high waiting latency, especially for long prompts. Prefill iterations have high latency but saturate GPU compute due to parallel processing of the input prompt. For current cloud-only and device-only LLM service solutions, end users are forced to choose between data locality and model scale. This process consists of two phases: prefill and decode. md for more information on those parameters. Calculating the operations-to-byte (ops:byte) ratio of your GPU. 动候嚣歇特呵token俯持趣澈朗，肪庶蓬拴拖System Prompt Caching [2]辛津盆token一证。. Jun 5, 2024 · prefill截断推理示意图，一般hidden dim是4096。. Jul 8, 2024 · Compared to competitive baselines, mllm-NPU achieves 22. You can tune the performance by changing max_num_batched_tokens . In contrast, […] May 6, 2024 · In prefill stage, as shown in Figure 1, LLM processes the input token to calculate intermediate states (keys and values) that are used to generate the “first” new token. Jun 22, 2023 · This prefill phase efficiently uses the GPU’s parallel compute because these inputs can be computed independently of each other. 6 Explore insightful articles on a wide range of topics on Zhihu's column platform. Since prefill can run in parallel over L_input, but decode must run sequentially over L_gen, the two phases have different performance characteristics and we analyze them separately. xiK = xi · wiK; xiV = xi · wiV. Time Per Output Token (TPOT)： 每个输出 token 的延迟（不含首个Token Oct 30, 2023 · General Questions 我现在希望通过一个脚本化软件对mlc进行一个多次循环的测试，并且记录每一次输入之后的prefill和decode速率 els, demonstrates significant improvements over existing approaches. Each GPU batches all requests in its working set for LLM invocation. 知乎专栏提供一个自由表达和创意写作的平台，涵盖多个话题。 (K) and value (V) vectors, i. As the prefill and decoding phases share the LLM weights and working memory, existing LLM serving systems typically colocate both phases on GPUs and maximize the overall system throughput – tokens generated per second across all users and requests – by batching the prefill and decoding steps across requests [42, 26]. the generation phase. Prefill processes the whole prompt input and is sometimes called the "prompt processing" phase. 8B), paving the way towards practical on-device LLM. LLM serving workloads is particularly dynamic in two aspects. 2: Prefill and decoding phase in the LLM inference. DistServe improves the performance of large language models (LLMs) serving by disaggregating the prefill and decoding computation. The detailed benchmark results for different parameters with OSS vLLM is Mar 4, 2024 · Each LLM serving request goes through two phases. While the prefill phase effectively saturates GPU compute at small batch sizes, the decode phase results in low compute utilization as it generates one token at a time per […] the context of LLMs. 2. 主要目的是产生 kv cache. Mar 15, 2024 · The LLM prefill decoding phase with large batch uses large input matrices and can benefit from the high performance of matrix cores. 2 Decode. These phases are crucial for generating coherent and contextually relevant text. Jan 18, 2024 · LLM applications often emphasize individual latency for each phase: time to first token (TTFT) for the prefill phase and time per output token (TPOT) of each request for the decoding phase. A: Audience: Identify who the response is for. decoding：解码，逐个生成下一个 token。. The prefillphase corresponds to the processing of the input prompt and the decode phase corresponds to the autoregressive token genera-tion. R: Response: Provide the response Feb 2, 2024 · Prefill attention has high operational intensity and is under the peak compute performance ceiling (bounded by peak floating point performance). Each new token depends on all Each LLM serving request goes through two phases. In the presence of stringent latency requirements, existing systems have to prioritize one latency over the other, or over-provision compute resources to GPU memory during the prefill stage, with-125 out sacrificing performance on long-context 126 benchmarks. 1 LLM Inference Process There are two distinct phases in LLM inference – a prefill phase followed by a decode phase. The first is prefill which processes the entire input prompt and produces the first output token and the second is decode which generates the rest of output tokens, one-at-a-time. T: Tone: Set the attitude and tone of the response. 图1. Sarathi: Efficient llm inference by piggybacking decodes with chunked prefills. 7x energy savings on average, and up to 32. The unique features of FlashInfer include: two stages of LLM serving have very different computational characteristics, in which the KVCache shifts with requests moving from prefill to decoding servers. Each example has L_input tokens of input text, and generates L_gen tokens of output text. 3 seconds (15% faster). 在上面的图例中，我们假设prompt中含有 3 个token，prefill阶段结束后，这三个token相关的KV值都被装进了cache。 1. Mar 7, 2024 · Balancing Compute and Memory: Upon profiling the LLM inference, we identified distinct limitations for both phases: the prefill phase faces restrictions imposed by the compute capacity, while the decode phase is constrained by memory bandwidth. 在在线的流式应用中，TTFT 是最重要的指标，因为它决定了用户体验。. The longer the prompt, the larger the TTFT. However, to meet This process involves the model utilizing its fixed pre-trained weights to comprehend the input text and produce text as output. Assuming the use of 16-bit half-precision numbers, storing each KV-token takes 2 * 40 (layer) * 5120 (units/layer) * 2 (bytes/unit) = 0. CaraServe: CPU-Assisted and Rank-Aware LoRA Serving for Generative LLM Inference. the ” Who won ? ” in Figure. As a result, the prefill phase is much more compute-intensive than the decoding phase. Fig. We visualize this process in Fig. The prefill phase includes the following: Oct 30, 2023 · General Questions I now hope to use a scripted software to test mlc in multiple loops and record the prefill and decode rates after each input. Mar 13, 2023 · Offloading is an essential technique for LLM inference on commodity hardware; Algorithm-oriented works have been developed to accelerate LLM inference; Memory optimizations and offloading have been studied for training and linear algebra; Background: llm inference# LLM inference workflow consists of two stages: prefill and decoding May 8, 2024 · Finally, some LLM modules broadly need all tokens, and for these we can employ the standard KV cache and store all words. 参考 illustrated-gpt2 这篇文章，自回归的大语言模型的推理分为两个步骤：. Jan 9, 2024 · FlightLLM is a complete mapping flow for LLMs on FPGAs that utilizes sparse DSP chain, always-on-chip decode, and length adaptive compilation. GPT series) do not have an encoder. Tensor parallelism across multiple machines or pipeline parallelism for larger models dynamic LLM serving workloads. Traditional LLM inference engines schedule at a per-request level in a first-come-first-serve manner, which leads to head-of-line blocking, degrad-ing latency for short requests. During the prefill stage, the user’s prompt is processed by the LLM in a single iteration, generating the first token. 2 shows an overview of our proposed tool. Large Language Model (LLM) inference consists of two distinct phases - prefill phase which processes the input prompt and decode phase which generates output tokens autoregressively. The LLM model outputs a response, one token at a time, i. Time To First Token (TTFT): 首 Token 延迟，即从输入到输出第一个 token 的延迟。. 整个self attention就是两次矩阵乘法和一次softmax。. 预填充阶段。在这个阶段中，我们把整段prompt喂给模型做forward计算。 Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache. In an autoregressive decoding process, like the one used in text generation with language models such as Falcon, there are typically two main phases: the prefill phase and the decode phase. 由图1可以看到，当一个Request进来时： 会首先经过Prompt phase，在这个阶段中，LLM处理所有用户的input或者prompt，计算出对应的KV Cache，其并行性可以充分利用GPU的算力，属于计算密集型。 Dec 22, 2023 · LLM Inference Series: 4. Since each request must be processed sequentially, the LLM inference engine batches multiple requests in a continuous fashion [39] together to improve throughput. . See #3130 (comment) The following diagram is the benchmark result of Llama 13B x 2 A100 for different QPS (it is the result from Anyscale forked vLLM). When there are available token_budget (max_num_batched_tokens), it schedules pending prefills. However, the reused text chunks are not always the input prefix, and when they are not, their Jul 13, 2024 · Prefill is the initial phase of inference in decoder-only models. 一个常规的LLM推理过程通常分为两个阶段：prefill和decode。通常会使用KV cache技术加速推理。 1. 78MB memory space. After prefill, the LLM then iteratively decodes (generates) the next token with the current KV cache and appends the new K and V vectors of the new tokens to the KV cache for the next iteration. Roofline model of attention operators in LLM Serving, data from A100 PCIe 80GB. In this work, we highlight the following pitfall of prefilling: for batches containing high-varying prompt This policy has two benefits: It improves ITL and generation decode because decode requests are prioritized. Consequently, each phase employs different strategies for dequantization of the shared int8/int4 weights. In other words, it currently takes more time to load 1MB of data to the GPU’s compute cores than it does for those compute cores to perform LLM computations on Benchmark Result. The prefill phase computes the KV cache layer by layer. of up to 18%, underlining the effectiveness of dynamic partitioning. But at 30 concurrent queries, the difference is smaller (Anyscale is 5% faster). 1 Prefill. the ” Alex ” in Figure. k. They bring different benefits for reducing the prefill and decode latency. The rest of the prefill stage includes self-attention and Nov 17, 2023 · This guide will help you understand the math behind profiling transformer inference. prefill：预填充，并行处理输入的 tokens。. 一、LLM推理的两阶段. AsshowninFigure2,processing100Kinputto- FlashInfer is a library for Large Language Models that provides high-performance implementation of LLM GPU kernels such as FlashAttention, PageAttention and LoRA. Jan 20, 2024 · LLM inference is a process that involves generating a sequence of output tokens in response to an input prompt. Jul 2, 2024 · The computational challenges of Large Language Model (LLM) inference remain a significant barrier to their widespread deployment, especially as prompt lengths continue to increase. 3 Quantization Toolkit To achieve the modular comparison of the different quantization dimensions aforementioned, and to consolidate best practices into an end-to-end pipeline, we have designed and developed a quantization toolkit named LLMC. It is analogous to the encoding phase that occurs in encoder-decoder models (i. For the first time, mllm-NPU achieves more than 1,000 tokens/sec prefilling for a billion-sized model (Qwen1. While the prefill phase effectively saturates GPU compute at small batch sizes, the decode phase results in low compute utilization as it generates one token at a Jul 1, 2024 · The application’s underlying LLM model processes the prompt, i. arXiv preprint arXiv:2308. While the prefill phase effectively saturates GPU compute at small batch sizes, the Jun 3, 2024 · Large language models (LLMs) play a crucial role in various Natural Language Processing (NLP) tasks, prompting their deployment on mobile devices for inference. In the presence of stringent latency requirements, existing systems have to prioritize one latency over the other, or over-provision compute resources to The webpage is a Zhihu column that offers a platform for free expression and creative writing. However, to process Sep 9, 2023 · Prefill. g. This paper introduces mllm-NPU, the first system enabling efficient on-device LLM prefilling acceleration using on-chip Neural Processing The infill program provides several ways to interact with the LLaMA models using input prompts: --in-prefix PROMPT_BEFORE_CURSOR: Provide the prefix directly as a command-line option. This interval is commonly interpreted by users as the “LLM startup In that case, consider reducing the max_batch_prefill_tokens and max_batch_total_tokens (if applicable). The scheduler adds requests to a GPU or cancels a working request from a GPU. --in-suffix PROMPT_AFTER_CURSOR: Provide the suffix directly as a command-line option. We present FlexGen, 想要优化 LLM 推理，首先要了解 LLM 推理的核心指标。. The skeleton prompt template is crafted to ensure efficiency and ease of point extraction. prefill. For example, FlexGen [19] quantizes and stores both the KV cache and the model weights in a 4-bit data format. GPU runs the Prefill steps and Decode steps continuously. We find that this strategy not only leads to strong prefill-decoding 知乎专栏是一个分享个人见解和经验的平台，提供多样化的内容和讨论。 In the prefill stage, LLM takes a prompt from the user which is a sequence of tokens as the input (e. But the data locality of personal data and personalized parameters LLM applications often emphasize individual latency for each phase: time to first token (TTFT) for the prefill phase and time per output token (TPOT) of each request for the decoding phase. The input tokens embeddings on each layer are first trans- Feb 27, 2024 · If we co-run both models and a padding limit of 512, 80% of large LLM’s prefill requests remain unchanged compared to when it runs alone. The varying prefill and decode May 26, 2024 · Large language models (LLMs) often incorporate multiple text chunks in their inputs to provide the necessary contexts. As a concrete example, we’ll look at running Llama 2 on an A10 GPU throughout the guide. vLLM [21], TGI [16], TensorRT-LLM [27]) runs the transformer models and schedules the prefill and decode stages. Each LLM inference request goes through two phases – a prefillphase followed by a decode phase. As explained in §1, due to the unique prefill-decoding process, LLM service may impose aggressive service-level objectives (SLOs) on both TTFT and TPOT, varying with the applica-tion’s needs. the prefill phase. The serving system must meet both Oct 11, 2023 · Prefill and decode phases. 等等（k需要先做转置）。. e. While the prefill phase effectively saturates GPU compute at small batch sizes, the decode phase results in low compute utilization as it generates one Sep 1, 2023 · Large Language Model (LLM) inference consists of two distinct phases – prefill phase which processes the input prompt and decode phase which generates output tokens autoregressively. The decoding stage, on the other hand, involves the LLM generating tokens sequentially, one at a time, in an autoregressive manner. The prefill phase starts with a tokenized and encoded representation of the prompt going through layers of the transformers. It describes the task precisely, uses simple demonstrations, and provides a partial answer for the LLM to continue writing. DistServe. Existing methods for speeding up Aug 31, 2023 · SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills. 专栏分享个人见解和专业知识，涵盖多个领域，提供深度洞察和有价值的讨论。 FPGA-based LLM accelerators that achieve speedups comparable to previous GPU and FPGA-based accelerators. 一个常规的LLM推理过程通常分为两个阶段：prefill和decode。 1. Specifically, for the BERT model, we achieve a 13. We illustrate reductions in the time to the first token of up to 40% and reductions in latency. By default, it is set to 512, which has Feb 12, 2024 · S: Style: Specify the writing style you want the LLM to use. See models/README. The prefill phase is compute-bound because it processes 探索LoongServe框架，支持弹性分配以通过弹性序列并行有效地服务于长上下文的大型语言模型。 Oct 27, 2023 · The LLM model compressed with reduced neural connection based on techniques such as sparsity or distillation. first inference latency), which gauges the duration from the LLM’s initiation to the generation of the first token. , the pre-filling stage) on a single A100 GPU. The prefill phase outputs the first token and generates the key and value cache (KV cache) for future decoding [ 21]. 3 (a)). To summarize, this work makes the following contributions: • We introduce GenZ, a first-order analytical tool that helps analyze LLM workload on different platforms. ments of large language model (LLM) inference make it feasible only with multiple high-end ac-celerators. Append attention is IO-bound when the query length is small, and compute-bound when the query length is large. It achieves high energy and cost efficiency, and outperforms GPUs on modern LLMs like LLaMA2-7B. Time-to-first-token (TTFT): The time it takes for an LLM serving system to generate the first token in response to a user request. 2023. LLM inference is memory-IO bound, not compute bound. hosts the LLM on GPUs, runs inference over the request, and responds (or streams) the generation back to the client. The first is prefill which processes the entire input prompt to produce one output token and the second is decode which generates the rest of output tokens, one-at-a-time. Jun 12, 2024 · When a user submits a request to a model, it undergoes two distinct operational phases: prefill and decode. Our findings pave the way for more eficient utilization of GPU resources in distributed LLM inference, a. In the prefill stage, all the input tokens are transformed into embeddings and generate the Key (K), Query(Q), and Value(V) vectors. In contrast, decode iterations have low latency but also for the prefill stage, while memory BW and interconnect link latency are the key bottlenecks for the decode stage. The impact will be smaller in practice. The token produced during the prefill stage serves as the input for generating the second token. 2×and 1. The Prefill Stage serves as the initial step in LLM inference. Token is a concept specific for LLMs and is a core LLM inference performance metric. prefill phase. Is there any way to capture the corresponding prefi May 28, 2024 · LLM has very large model parameters, resulting in large KV-tokens. DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving. Both the Key and the Value vectors are saved in the KV cache for future use. We’ll cover: Reading key GPU specs to discover your hardware’s capabilities. At 5 concurrent queries, Anyscale is 4. Next, during the decode phase, output tokens are generated one at a time, where the token generated in one step is 6 days ago · LLM inference goes through a prefill stage to process the prompt, populate the KV cache, and start the decoding stage to generate tokens (Sec. x ( i + 1) = frelu 知乎专栏为用户提供自由表达和随心写作的平台。 Jul 12, 2023 · From Efficiently Scaling Transformer Inference:. Apr 17, 2024 · In the prefill phase, the LLM processes the input tokens to compute the intermediate states (keys and values), which are used to generate the “first” new token. In our hybrid batch, the single prefill chunk ensures high GPU utilization, while the decode phase requests ‘pig-gyback’ along. A Zhihu column offering insights on various topics, enabling free expression and creative writing. q直接送入self atention Matricq指的是矩阵乘k的结果，matrick指矩阵乘k的结果。. 4x faster prefill speed and 30. Large Language Model (LLM) inference consists of two distinct phases - prefill phase 理解 LLM 推理过程. If a last pending prefill request cannot fit into max_num_batched_tokens, it llm theoretical performance analysis tools and support params, flops, memory and latency analysis. Given an average prefill-to-decode token ratio for an LLM application, we select a prefill chunk size that maximizes the overall performance. Existing LLM serving systems colocate the two phases and batch the computation of prefill and decoding across all users and requests. Aug 31, 2023 · Abstract: Large Language Model (LLM) inference consists of two distinct phases - prefill phase which processes the input prompt and decode phase which generates output tokens autoregressively. 重复这两个步骤直到生成 EOS token 或达到用户设定的停止条件（stop token 或最大 token 数 Feb 27, 2024 · Punica schedules new user requests at a per-request level and migrates old requests between GPUs at a per-iteration level. In general, Ray Dashboard is a useful debugging tool, letting you monitor your Ray Serve / LLM application and access Ray logs. An LLM inference engine (e. Then, LLM will understand the context of the prompt and generates the first response token (e. During prefill phase, the user’s input prompt is processed and first output token is produced. 1×in prefill and decode stages respectively, when compared to DFX, an state-of-the-art LLM serving system vLLM [53]. a. Jun 18, 2024 · TensorRT-LLM is an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. Another key observation in our study is that attention modules in different layers and positions in the LLM behave differently and need different preferences for their KV cache, as shown on the right in Figure 1. 生成response的阶段。 Apr 15, 2024 · The prefill phase processes all the input tokens in a single iteration to build the key-value cache and generates the first output token, while the decoding phase only needs to compute the key-value cache for the newly generated output token. 6 seconds vs 5. 8x speedup in an end-to-end real-world application. The hybrid batches constructed in SARATHI have a uni- Mar 7, 2024 · The Prefill Phase requires just one invocation of the LM, requiring the fetch of all the parameters of the model once from the DRAM, and reuses it m times to process all the m tokens in the prompt. Building on this idea, we found that the scheduling of KVCache is central to LLM serving scheduling. With MI210, during the prefill phase where the prompt sequence length and batch size are large, the GEMM operations are compute-bound. 4×speedup over prior FPGA-based accelerators. Once chunked prefill is enabled, the policy is changed to. Nov 1, 2023 · We can see here that the Anyscale’s end-to-end time is consistently better than Fireworks’s, but the gap closes (especially proportionately) at high load levels. Each of these phases uses system resources differently. Matrixq和matrixk先做矩阵乘法获得Matrixqk，对Matrixqk做softmax，之后qk May 29, 2024 · We identified that in real inference scenarios, LLM models need to have clearly separated (disaggregated) inference functions (prefill, decode) to achieve best serving performance. The primary metric used is prefill-time (a. Due to the quadratic complexity of the attention computation, it takes 30 minutes for an 8B LLM to process a prompt of 1M tokens (i. LLM inference contains two stages, namely, the prefill stage and the decode stage. Aug 31, 2023 · Large Language Model (LLM) inference consists of two distinct phases - prefill phase which processes the input prompt and decode phase which generates output tokens autoregressively. $$. KV caching, a deeper look In this post, we will look at how big the KV cache, a common optimization for LLM inference, can grow and at common mitigation strategies. 127 2 Related Works 128 The long-context efficiency of LLM has been 129 widely studied, which can be classified into two 130 categories: prefilling and decoding. Decode: The subsequent phase that generates token-by-token until termination. 131 Prefilling The prefilling of LLM encounters 132 Aug 31, 2023 · SARATHI employs chunked-prefills, which splits a prefill request into equal sized chunks, and decode-maximal batching, which constructs a batch using a single prefill chunk and populates the remaining slots with decodes, resulting in significant improvements in inference performance across models and hardware. Overall, while co-running with a small LLM, the large LLM’s average prefill latency increases by 10%, and throughput drops by 12%. Chunked prefill greatly improves latency when QPS is high, but has competitive performance at low QPS. Jun 1, 2024 · Amey Agrawal, Ashish Panwar, Jayashree Mohan, Nipun Kwatra, Bhargav S Gulavani, and Ramachandran Ramjee. Figure 2. Jul 1, 2024 · Time to first token generally includes both request queuing time, prefill time and network latency. For example, a 13 billion parameter GPT-3 model has 40 layers and a hidden size of 5120. Topics profiler python3 transformer llama gpu-performance llm llm-inference Mar 17, 2024 · Prefill: The first phase of LLM inference that digests all the input tokens, populates the KV Cache, and generates the first output token. 5-1. LLM中的Prefill和Decode阶段. Cloud-only centralized solution offers quick and quality generations from large scale models with its sufficient computing power. Our proposed strategy 1) employs iteration-level scheduling, 2) disaggregates the “prefill” and “decode” stages of requests across different Apr 15, 2024 · During inference for transformer-based large language models (LLM), prefilling is the computation of the key-value (KV) cache for input tokens in the prompt prior to autoregressive generation. 对于causual LM，在正式推理前，需要一部分前置输入，这个过程就是Prefill。. the prefill stage which takes a prompt sequence to generate the key-value cache (KV cache) for each transformer layer of the LLM. Motivated by the emerging demand for latency-insensitive tasks with batched processing, this paper initiates the study of high-throughput LLM inference using limited resources, such as a single commodity GPU. Note that these are stress tests. First, as the context window of LLMs increases, the resource demand during the prefill phase varies significantly across requests with differ-ent input lengths in both computation and GPU memory consumption. The inference process of Large Language Models (LLMs) is divided into two stages: the Prefill Stage and the Decode Stage. 16369 (2023). prioritize decode requests. ko dd zn uh bm lq jh ik pw zv