---
title: "HBM 4.8TB/s：LLM 解码性能超越 FLOPS"
source_url: "https://www.huandroid.com/zh/hbm-4-8tb-s-llm-decode-2"
stream: "X_CASE_Lab"
language: "zh"
platform: HuAndroid Strategic Intelligence Desk
governance: Human-in-Command
epistemic_layer: M-E-P Matrix / SemiAnalysis Benchmark
ai_generated: true
tdm_reservation: 1
date_published: 2026-07-26T14:39:50+02:00
date_modified: 2026-07-27T07:18:11+02:00
tags: ["4.8", "dpd", "GPU", "H100", "H200", "HBM", "inference", "llm", "MLPerf", "TB/s", "性能", "数据中心电力", "解码"]
---

# HBM 4.8TB/s：LLM 解码性能超越 FLOPS

## Content

## 执行摘要

**LLM模型的推理效率在解码阶段由HBM带宽主导，而非FLOPS；然而，数据中心电力需求预测并未反映所观察到的技术改进所带来的相应修正。KV缓存节点间预填充与解码传输延迟在实际场景中仍未量化，而分布式架构（DPD）显示运营成本潜在降低可达50%，但GPU在DPD配置下的实际性能数据尚未公开。** —

MLPerf Inference v6.0和vLLM基准测试表明，在服务器场景下，GPT-OSS-120B模型使用B200的离线吞吐量比顶级B300高出50%，得益于基于900 GB/s NVLink的多节点配置。这一成果源于利用HBM带宽作为关键资源的架构优化：对于Llama 3.1 70B在H100上的表现，张量核心利用率从预填充阶段的92%降至解码阶段的28%，算术强度下降5倍且内存饱和限制了吞吐量。

H100（3,35 TB/s HBM）与H200（4,8 TB/s HBM3e）在解码阶段的性能差异导致吞吐量提升达45%。多家来源报告单GPU性能提升介于1.9倍至4倍之间；NVIDIA表示Llama 2 70B提升1.9倍，而SemiAnalysis指出固定交互场景下提升2-4倍。然而，405B模型的token延迟指标尚未公布：尽管GB200 NVL72在MLPerf v5.0中单8-GPU配置吞吐量比H200高出3.4倍，但token权重传输时间仍属保密。

SGLang在H100上实现Llama 3.1 8B的16,200 token/s吞吐量，超过vLLM的12,500 token/s（+29%），得益于RadixAttention和FlowKV将TTFT降低29%。后者将KV缓存传输平均时间从0.944秒降至0.053秒——下降96%。该实现要求节点间延迟低于1 µs以优化TTFT；InfiniBand NDR（400 Gbps，~1–2 µs）适用于AI集群。然而，SGLang的GPU占用率未在公开记录中披露。

100 GPU H100集群年成本按三年期计算介于500万至700万美元之间（Mercatus Compute），其中能源和冷却占40%-70%。DPD优化可降低30-50%的电力成本，但TCO估算存在显著差异：Disintermediate预估1,024 GPU集群五年期成本60-9000万美元，而Introl计算同期价值860万美元。本地部署与云服务的盈亏平衡点在70%以上利用率下缩短至不足4个月，但DPD中GPU实际能耗数据未公开。

全球数据中心用电量IEA预测2026年达565 TWh（较2025年增长26.4%），其他来源预计更高：InformedClearly指出2026年将超1,000 TWh（相当于德国总消耗）。尽管推理效率在同等功耗下提升40%且查询能耗下降34%，但预测未整合这些修正。IEA预估较2022年增长117%，而每查询能效年度下降一个数量级。

监管反应加速：FEC于2026年6月18日下令美国50 MW以上负载的互联加速，要求30天内完成。NERC在2025年9月记录Level 2警报，因传输故障导致数据中心同时断电1.5 GW。Big Five超大规模云服务商宣布2026年AI投资7250亿美元（同比+77%），与AI相关的债务已超过2026年1,210亿美元（较2024年280亿美元增长）。

互联性能至关重要：CXL.mem平均延迟250 ns，PCIe DMA介于5-16 μs之间，而NVLink 5.0（1.8 TB/s）和6.0（3.6 TB/s）的延迟数据尚未公开。Ultra Ethernet Consortium于2025年6月发布v1.0规范，目标往返时间低于10 µs且带宽超800 Gbps；Keysight Technologies于2026年3月17日演示800GE线路速率互操作性。然而，预填充与解码节点间KV缓存传输实际延迟仍未在开放记录中披露。

替代架构如Belauga（基于CXL的内存共享）将GPU利用率降至87%且预填充效率提升至7.5倍；maru实现基于CXL共享内存的KV缓存存储引擎，但无公开实验数据。AWS在SageMaker HyperPod中使用DPD并配备EFA-RDMA通道，但节点间网络性能规格仍属保密。

缺乏KV缓存传输实际延迟、SGLang GPU占用率及DPD真实能耗数据限制了对提出方案有效性的评估能力。技术改进与电力需求预测间的差异表明现有模型未正确整合推理优化至长期计算中。

## 大模型的推理性能及架构限制

根据MLPerf Inference v6.0，Red Hat AI在B200上使用vLLM实现了GPT-OSS-120B的离线吞吐量最佳表现，超越了B300顶部性能的50%。该结果通过多节点推理配置实现，利用了900 GB/s的NVLink架构。

### 解码阶段受HBM带宽限制，而非FLOPS计算能力

在生成新token的过程中，每个token都需要从VRAM完整读取KV-cache。根据[GMI Cloud](https://www.gmicloud.ai/en/blog/top-gpus-llm-text-inference-bandwidth)的报告，该操作受限于HBM带宽而非ALU计算能力。

Llama 3.1 70B模型在H100上花费80%的wall-clock时间进行解码，由于内存带宽限制导致GPU容量80%未被利用。此数据由[Spheron Network](https://www.spheron.network/blog/heterogeneous-gpu-inference-cost-optimization)证实，其指出在解码阶段处理器仅达到0.17%的TFLOPS能力。

### H100与H200性能对比：内存带宽成为关键因素

NVIDIA H200配备4.8 TB/s HBM3e带宽和141 GB VRAM，相比H100的3.35 TB/s和80 GB。根据[Spheron Network](https://www.spheron.network/blog/nvidia-h100-vs-h200)的数据，这种差异使大型LLM在解码阶段吞吐量提升45%。

多份资料显示H200在固定交互场景下GPU解码吞吐量较H100提升1.9-4倍。[SemiAnalysis](https://www.morphllm.com/h100-vs-h200)报告增幅为2-4倍，而[NVIDIA](https://developer.nvidia.com/blog/nvidia-h200-tensor-core-gpus-and-nvidia-tensorrt-llm-set-mlperf-llm-inference-records)指出在Llama 2 70B上增幅达1.9倍。

### 透明基准测试与资源实际利用率

vLLM、TensorRT-LLM和MLPerf Inference v6.0基准测试提供了解码阶段ALU饱和度的量化数据。根据[Inference Engineering Tech](https://inferenceengineering.tech/learn/gpu-inference)，算术强度阈值300 FLOP/byte区分了计算受限与内存带宽受限负载。

对于H100 SXM上的Llama-70B模型，张量核心利用率从预填充阶段的92%降至解码阶段的28%。[Towards Data Science](https://towardsdatascience.com/prefill-is-compute-bound-decode-is-memory-bound-why-your-gpu-shouldnt-do-both)指出解码阶段算术强度较预填充下降5倍，证实了解码受内存主导。

### token权重传输延迟：数据不完整与信息缺口

LLaMA-70B FP16在H100上的token生成延迟计算为140 GB / 3 TB/s ≈ 47 ms/token。[S-Samarth](https://s-samarth.github.io/DataSciencePreparation/LLM/inference-arch/prefill-vs-decode)基于数据传输理论模型提供了该数值。

然而，针对405B参数模型的token权重传输延迟尚未有公开测量数据。Llama 3.1 405B在GB200 NVL72上显示比H200八卡系统MLPerf v5.0吞吐量高3.4倍，但未公布单token延迟指标。

公开记录中无相关数据：企业仅披露了405B模型的总体吞吐量，未发布单token传输延迟。为全面评估，需要405B模型在推理场景下的单token性能数据。

### 分散架构与量化技术的影响

vLLM架构实现预填充与解码分离，优化吞吐量。Qwen3.6-27B模型需16倍B100/B200资源分别处理两阶段并采用KV-aware路由，达到4K ISL / 200 OSL最大吞吐量。

Q4_K_M量化将Llama 2 70B的VRAM消耗从FP16的140 GB降至38 GB。[EaseCloud](https://blog.easecloud.io/ai-cloud/run-70b-models-on-consumer-gpus)指出内存消耗降低至3.7倍，但未说明对延迟或生成质量的影响。

现有证据表明，70B以上模型的解码阶段受HBM带宽主导。vLLM和MLPerf基准测试可量化ALU饱和度，但405B模型的单token传输延迟数据尚未公开。

## SGLang与vLLM在高复杂度工作负载中的性能量化对比

根据2026年1月至5月发布的基准测试数据，SGLang在H100上针对Llama 3.1 8B模型实现了每秒16,200个token的吞吐量，超过vLLM的12,500 token/s——差距达29%。这一改进在具有共享前缀的工作负载（如RAG、多轮对话）中尤为显著，SGLang将TTFT降低了29%：从100 tok/s提升至129 tok/s。

### RadixAttention技术对延迟和吞吐量的影响

SGLang在RAG工作负载中实现了TTFT降低27%（从85 tok/s降至108 tok/s），并在前缀重叠率超过60%的场景下，吞吐量提升20-40%，这得益于RadixAttention技术的实现。该机制通过优化KV Cache缓存，减少了重复序列的重新计算需求。

对于更大模型如Llama 2 70B，SGLang在采用RadixAttention时保持了16,215 token/s的吞吐量（对比vLLM的12,553 token/s——提升29%）。P50和P99延迟平均降低了29%，表明其性能在多种指标上保持一致性。

### 键值缓存传输的网络需求

节点prefill与decode之间键值缓存（KV Cache）的传输需要低于1微秒的延迟以实现优化TTFT。通过FlowKV实现，平均传输时间从0.944秒降至0.053秒——降幅达96%——在分布式框架中表现显著。

llm-d使用NIXL处理此流，支持UCX over RDMA、libfabric over AWS EFA等后端。InfiniBand NDR提供每端口400 Gbps带宽，延迟约1-2微秒，适合GPU-GPU通信的AI集群。

### 框架对比：GPU占用率与硬件兼容性

框架
GPU占用率（范围）
支持的硬件

TensorRT-LLM
85–92%
NVIDIA，仅限非结构化模型

TGI
68–74%
NVIDIA，通用支持

SGLang
未指定
NVIDIA（优先），与vLLM相比覆盖有限

SGLang在真实场景中的GPU占用率数据未在公开记录中披露。该公司已公布性能改进，但未公开实际内存或计算资源利用率的指标。

### 财务影响与技术发展

RadixArk（SGLang背后的初创公司）据行业消息人士透露，已获得1亿美元种子轮融资，估值达4亿美元，NVIDIA和AMD均有参与。vLLM开发者Inferact则筹集了1.5亿美元资金，估值8亿美元。

为全面评估，还需更多关于每生成token运营成本及系统在NVLink Switch或InfiniBand NDR多机架集群中的可扩展性细节。开源资料未包含H100以外的现实配置端到端延迟测试数据。

最值得关注的是FlowKV将键值缓存传输时间降低了96%，这暗示了分布式推理效率基础设施的根本性重构。现有证据的完整性仍受限于未公开的GPU占用率和真实环境性能数据。

## 100块H100 GPU集群的年度拥有成本：传统配置与DPD之间的差异

根据Mercatus Compute（2026年3月）的数据，传统配置下100块H100 GPU集群的年度估算成本介于500万美元至700万美元之间，基于三年期基准。该范围包括每节点10.2千瓦电力消耗、机柜租赁费用（每月150-250美元/千瓦）以及与实际使用量相关的运营支出。

多份资料显示，能源和散热成本占总拥有成本（TCO）的40%-70%。根据Introl（2026年4月）的数据，电力和冷却费用每年约为42万美元，而硬件维护和技术人员费用则增加另外27.5万美元/年。

DPD（动态功率分配）优化显著降低了运营成本。FP4.dev指出，H100 SXM5（TDP 700 W）的能耗低于B200 SXM（1,000 W TDP），在优化场景下电力和冷却成本预计减少30%-50%。

### TCO计算中的量化矛盾

现有数据显示出显著差异。Mercatus Compute报告三年期总拥有成本为5-700万美元，而Disintermediate对10MW（约1,024块H100 GPU）集群的五年期全包成本估算达60-9000万美元。

Introl计算出相同数量GPU的五年期TCO为860万美元，年度运营支出估计在150万至250万美元之间。这种差异未被公开资料解释，也未归因于硬件配置或地理因素的变化。

### 本地部署与云租赁的盈亏平衡及投资回报率

根据GPUinsights.net（2026年5月）的数据，利用率超过70%的工作负载中，本地部署与云租赁的盈亏平衡期已缩短至不足4个月。这一结果归功于DPD架构效率，其成本效益优于传统配置。

Mercatus Compute（2026年3月）指出，100块H100 GPU集群的投资回报期为2-5年，三年期内部收益率（IRR）介于7%-33%之间。回报率受芯片折旧加速影响：Mercatus报告称，由于36个月后残值假设从30%至70%的差异，TCO出现170万美元变动。

### H100 SXM5实际每小时成本及电力附加费

来源
H100 SXM5 GPU每小时成本
每小时电力附加费
能源对总成本的贡献比例

GridStackHub.ai（2026年5月）
$1,9244
$0,1344
7.5%

GPU Compute Index（2026）
$1,49 – $2,27
N/D
N/D

GMI Cloud（2025）
$2,10 – $4,50
N/D
N/D

GridStackHub.ai（2026年5月）数据显示，H100 SXM5在机柜租赁中的实际每小时成本为$1,9244，电力附加费为$0,1344/小时。全国范围内能源成本占总成本的7.5%。

H100云服务价格已降至$3-$4/GPU-小时，较2025年降价；在专业提供商处，可中断工作负载的Spot价格低于$2.00/小时（2026年）。

### 本地优先推理优化的影响

针对推理的优化（vLLM、TensorRT-LLM）使成本较传统配置降低2-6倍。该效率已在采用本地优先架构的本地部署集群中观察到。

公开资料未包含大规模实施此类优化的企业财务表现数据。Mercatus Compute报告称本地部署集群实现正向投资回报率，但未披露运营细节及DPD场景中使用的GPU数量。

为全面评估本地优先与DPD方法的经济优势，需更多实际能耗数据、工作负载持续时间及GPU在真实操作条件下的利用率参数。

公开记录中无相关数据：采用本地优先推理优化的企业未公布关键运营参数，这些参数对于TCO计算或基础设施韧性评估至关重要。

## 数据中心电力需求预测中的数量矛盾

据国际能源署（IEA）估计，全球数据中心能耗在2026年将达到565太瓦时（TWh），较2025年的447 TWh增长26.4%。然而其他来源显示存在分歧：Axis Intelligence Research预计2030年将达950 TWh，相比2026年增长86.4%；GreenFuelJournal则报告同一年度的预测值为945 TWh（基于IEA基准情景）。最高数据来自InformedClearly，其预测2026年将突破1,000 TWh（相当于德国全年电力消耗量）。

### 推理效率：运营数据与需求预测存在矛盾

根据IEA数据，AI查询的能耗成本占数据中心总能耗不足1%，即每年不到4 TWh。优化的推理系统平均消耗0.31 Wh/查询（IQR 0.16–0.60），公开数据估算值较实际运营条件高至20倍。推理效率在2025-2026年间吞吐量保持不变的情况下提升40%，相较前一年单次查询能耗下降34%。

尽管有这些技术改进，需求预测并未相应修正。IEA预计数据中心全球总耗电量将在2026年突破1,000 TWh（较2022年增长117%），而单次查询能耗却每年下降一个数量级。

### 监管反应：加速大容量并网规范

FEC于2026年6月18日发布命令，要求美国加快50 MW以上负荷的并网进程，运营商需重新制定现行费率并在30日内提交可靠性报告。政府于2025年10月23日指示FERC启动规范流程，标准化>20 MW负荷并网，最终截止日期定为2026年4月30日。

NERC发布Q1 2026可靠性指南，要求采用动态模型和短路分析进行大容量负荷整合研究。该机构于2025年9月记录Level 2警报，因正常传输故障期间数据中心同时减少1.5 GW负荷，引发频率过冲和电压偏差。

### 投资与债务：对金融市场的影响

五大超大规模云服务商（Amazon, Alphabet, Meta, Microsoft, Oracle）宣布2026年AI领域总投资达7250亿美元，较2025年峰值4100亿美元增长77%。摩根士丹利将2026年资本支出（CapEx）预估上调至8050亿美元（同比增100%），而CreditSights预计全球CapEx为6020亿美元，其中4500亿美元用于AI基础设施。

与AI相关的债务快速增长：超大规模云服务商在2026年发行了1210亿美元债券，相较2024年的280亿美元显著增长。2026年全球AI相关债务估计达5700亿美元（较2022年水平高出近四倍），占2025年底美元投资（IG）新增供应量的30%。企业未披露AI内部融资结构细节。

### 关键基础设施：延迟与电网系统压力

美国并网积压量从2021至2026年间增长至2,600 GW（五年平均达到运营水平），2021-2024年间从1,400 GW增至2,000 GW。NERC预计到2030年夏季用电需求将增加224 GW，高风险地区包括MISO、PJM、ERCOT和太平洋西北部。

德国政府于2026年5月11日提交《网络法案》草案（Netzpackage），旨在简化并网流程，引入数字化规则和法律时限。爱尔兰数据中心空置率1.3%，在建产能达94%。但未公开具体并网项目积压数量。

## 系统性影响

推理效率的提升降低了每查询的能耗，但电力需求预测并未反映出相应的修正；这种差异表明当前模型高估了技术改进的累积影响。为了更全面的评估，需要有关推理优化如何被监管机构纳入长期需求模型的数据。

## KV缓存在Prefill和Decode节点间的传输延迟限制

专用Prefill与Decode节点间大规模**KV缓存**的传输受硬件互连和网络协议累积延迟的制约。技术资料显示，KV缓存在推理阶段占GPU VRAM高达90%，参数量70B的模型在FP8精度下单个模型HBM消耗可达1TB以上。

### 互连架构与性能实测

NVIDIA发布的NVLink 5.0带宽达1.8 TB/s，NVLink 6.0达到3.6 TB/s，专为AI/HPC集群GPU间延迟优化设计。此类互连相较PCIe DMA的综合延迟（5–16 μs）表现更优。

CXL.mem协议总延迟约250 ns，包含主机控制器（10–20 ns）、交换机穿越（50 ns）和DRAM访问（80–100 ns）三部分。混合系统实测延迟约0.5 ms，其中各组件占比分别为85%、14%和1%。

互连技术
平均延迟
最大带宽
参考文献

CXL.mem
250 ns
未指定
[Sampooni, 2024](https://sampooni.github.io/kv-cache-docs/appendix/f-cxl-technology.html)

PCIe DMA
5–16 μs
未指定
[Sampooni, 2024](https://sampooni.github.io/kv-cache-docs/chapters/ch04-latency.html)

NVLink 5.0
未公开
1,8 TB/s
[AgentMarketCap, 2026](https://agentmarketcap.ai/blog/2026/04/16/ualink-nvlink-ultra-ethernet-scale-up-interconnect-war-agent-inference)

NVLink 6.0
未公开
3,6 TB/s
[AgentMarketCap, 2026](https://agentmarketcap.ai/blog/2026/04/16/ualink-nvlink-ultra-ethernet-scale-up-interconnect-war-agent-inference)

### AI扩展网络标准：超以太网联盟与UALink

**超以太网联盟（UEC）**由AMD、Cisco、Meta和Microsoft等100+成员组成，于2025年6月在旧金山发布v1.0规范，定义了**超以太网传输协议（UET）**。该版本旨在支持百万级GPU/TPU集群，实现低于10 μs的往返时间与单通道800 Gbps以上带宽。

2026年3月17日，Keysight Technologies在OFC 2026展示与Broadcom合作实现的800GE线速率下**链路层重试（LLR）**和**基于信用的流控制（CBFC）**的UET规范互操作性，标志着AI/HPC大规模扩展网络的实用化进展。

**UALink联盟**于2025年3月发布v1.0规范，2026年4月7日更新至v2.0版本，聚焦NVLink与超以太网系统的互操作性。

### 降低内存负载的替代架构

**Belauga**架构（2025年论文提出）采用基于CXL的内存共享管理KV缓存，减少对HBM依赖。相关系统实测显示GPU利用率降低至87%，Prefill效率较全重计算提升7.5倍。

**maru**项目（GitHub托管）实现基于CXL共享内存的KV缓存存储引擎，专为LLM推理优化。仓库未公开实际延迟或生产吞吐量数据。

根据**AWS**披露，SageMaker HyperPod实施的DPD（解耦Prefill Decode）将集群物理分割为Prefill和Decode节点，采用EFA-RDMA通道传输KV缓存。但相关节点间网络性能具体参数未在公开注册表中披露。

> **“KV缓存在32K token以上上下文推理中主导VRAM预算”**
> —— DigitalApplied, 2026博客

现有资料缺乏实际环境中Prefill与Decode节点间KV缓存传输时间的直接数据。实际性能受网络拓扑、协议选择（EFA-RDMA vs UET）及CXL互连实现等多重因素影响。

真实环境中的KV缓存跨节点传输延迟数据未在公开记录中披露。为全面评估，需补充以下信息：(1) 800GE UET集群的实测往返时间；(2) 生产环境中LLR和CBFC带来的额外开销；(3) 多节点系统CXL内存访问延迟。

大规模KV缓存管理能力取决于高带宽互连、微秒级拥塞控制协议与共享内存架构的协同演进。缺乏公开性能数据限制了对现有解决方案有效性的评估能力。

 

*[Andrey Matveev](https://unsplash.com/@zelebb) 在Unsplash上的照片

⎈ 由多智能体架构在知识安全模式下自主生成。阅读 [操作声明](https://www.huandroid.com/disclaimer)*

# URL

## I.

* [https://www.redhat.com/en/blog/red-hat-ai-tops-mlperf-inference-v60-vllm-qwen3-vl-whisper-and-gpt-oss-120b](https://www.redhat.com/en/blog/red-hat-ai-tops-mlperf-inference-v60-vllm-qwen3-vl-whisper-and-gpt-oss-120b)

* [https://www.promptquorum.com/local-llms/multi-gpu-local-llms](https://www.promptquorum.com/local-llms/multi-gpu-local-llms)

* [https://www.gmicloud.ai/en/blog/top-gpus-llm-text-inference-bandwidth](https://www.gmicloud.ai/en/blog/top-gpus-llm-text-inference-bandwidth)

* [https://rocm.blogs.amd.com/artificial-intelligence/mlperf-inference-v6.0/README.html](https://rocm.blogs.amd.com/artificial-intelligence/mlperf-inference-v6.0/README.html)

* [https://gigagpu.com/llama-3-1-performance-report-april-2026/](https://gigagpu.com/llama-3-1-performance-report-april-2026/)

* [https://towardsdatascience.com/prefill-is-compute-bound-decode-is-memory-bound-why-your-gpu-shouldnt-do-both/](https://towardsdatascience.com/prefill-is-compute-bound-decode-is-memory-bound-why-your-gpu-shouldnt-do-both/)

* [https://www.progressiverobot.com/2026/05/12/vllm-gpu-sizing-configuration-guide/](https://www.progressiverobot.com/2026/05/12/vllm-gpu-sizing-configuration-guide/)

* [https://arxiv.org/html/2606.17104v1](https://arxiv.org/html/2606.17104v1)

* [https://www.spheron.network/blog/hbm3e-vs-hbm4-vs-hbm4e-llm-inference-guide/](https://www.spheron.network/blog/hbm3e-vs-hbm4-vs-hbm4e-llm-inference-guide/)

* [https://codingprotocols.com/blog/vllm-quantization-tensor-parallelism-kubernetes](https://codingprotocols.com/blog/vllm-quantization-tensor-parallelism-kubernetes)

* [https://rocm.docs.amd.com/en/docs-7.0-rc1/preview/benchmark-docker/inference-vllm-llama-3.3-70b-fp8.html](https://rocm.docs.amd.com/en/docs-7.0-rc1/preview/benchmark-docker/inference-vllm-llama-3.3-70b-fp8.html)

* [https://www.spheron.network/blog/nvidia-h200-specs/](https://www.spheron.network/blog/nvidia-h200-specs/)

* [https://docs.nvidia.com/dgx/dgxh100-user-guide/introduction-to-dgxh100.html](https://docs.nvidia.com/dgx/dgxh100-user-guide/introduction-to-dgxh100.html)

* [https://www.bis.gov/press-release/department-commerce-revises-license-review-policy-semiconductors-exported-china](https://www.bis.gov/press-release/department-commerce-revises-license-review-policy-semiconductors-exported-china)

* [https://www.spheron.network/blog/nvidia-h100-specs/](https://www.spheron.network/blog/nvidia-h100-specs/)

* [https://nvidia.github.io/TensorRT-LLM/1.3.0rc15/blogs/H100vsA100.html](https://nvidia.github.io/TensorRT-LLM/1.3.0rc15/blogs/H100vsA100.html)

* [https://www.spheron.network/blog/nvidia-h100-vs-h200/](https://www.spheron.network/blog/nvidia-h100-vs-h200/)

* [https://developer.nvidia.com/blog/nvidia-h200-tensor-core-gpus-and-nvidia-tensorrt-llm-set-mlperf-llm-inference-records/](https://developer.nvidia.com/blog/nvidia-h200-tensor-core-gpus-and-nvidia-tensorrt-llm-set-mlperf-llm-inference-records/)

* [https://developer.nvidia.com/blog/nvidia-blackwell-delivers-massive-performance-leaps-in-mlperf-inference-v5-0/](https://developer.nvidia.com/blog/nvidia-blackwell-delivers-massive-performance-leaps-in-mlperf-inference-v5-0/)

* [https://inferenceengineering.tech/learn/gpu-inference/](https://inferenceengineering.tech/learn/gpu-inference/)

* [https://www.morphllm.com/h100-vs-h200](https://www.morphllm.com/h100-vs-h200)

* [https://dataforcee.us/2026/04/15/prefill-is-compute-bound-decode-is-memory-bound-why-your-gpu-shouldnt-do-both/](https://dataforcee.us/2026/04/15/prefill-is-compute-bound-decode-is-memory-bound-why-your-gpu-shouldnt-do-both/)

* [https://www.spheron.network/blog/heterogeneous-gpu-inference-cost-optimization/](https://www.spheron.network/blog/heterogeneous-gpu-inference-cost-optimization/)

* [https://medium.com/@yugank.aman/the-decode-tax-why-your-30k-gpu-runs-at-0-17-utilization-de48fc005d74](https://medium.com/@yugank.aman/the-decode-tax-why-your-30k-gpu-runs-at-0-17-utilization-de48fc005d74)

* [https://thakicloud.github.io/en/llmops/b200-vllm-prefill-decode-disaggregation-tps/](https://thakicloud.github.io/en/llmops/b200-vllm-prefill-decode-disaggregation-tps/)

* [https://www.gmicloud.ai/en/blog/best-gpus-optimized-llm-inference](https://www.gmicloud.ai/en/blog/best-gpus-optimized-llm-inference)

* [https://docs.nvidia.com/dynamo/zh-CN/recipes/qwen3-235b-a22b-fp8](https://docs.nvidia.com/dynamo/zh-CN/recipes/qwen3-235b-a22b-fp8)

* [https://deploybase.ai/articles/h200-runpod](https://deploybase.ai/articles/h200-runpod)

* [https://blog.easecloud.io/ai-cloud/run-70b-models-on-consumer-gpus/](https://blog.easecloud.io/ai-cloud/run-70b-models-on-consumer-gpus/)

* [https://www.compute-market.com/blog/multi-gpu-local-llm-setup-guide-2026](https://www.compute-market.com/blog/multi-gpu-local-llm-setup-guide-2026)

* [https://robertz.co/simulators/computer-science/systems/llm-model-scaling/](https://robertz.co/simulators/computer-science/systems/llm-model-scaling/)

* [https://www.sitepoint.com/vram-requirements-70b-models-16gb-gpu-minimum-2026/](https://www.sitepoint.com/vram-requirements-70b-models-16gb-gpu-minimum-2026/)

* [https://insidepc.tech/hardware/for-ai/ai-builds/ryzen-ai-max-395-local-llm](https://insidepc.tech/hardware/for-ai/ai-builds/ryzen-ai-max-395-local-llm)

* [https://insiderllm.com/guides/running-70b-models-locally-vram-guide/](https://insiderllm.com/guides/running-70b-models-locally-vram-guide/)

* [https://willitrunai.com/blog/vllm-multi-gpu-setup-guide](https://willitrunai.com/blog/vllm-multi-gpu-setup-guide)

* [https://syndromeag.com/catalog/ai-accelerators/ai-nvidia-h200-141gb-sxm/](https://syndromeag.com/catalog/ai-accelerators/ai-nvidia-h200-141gb-sxm/)

* [https://s-samarth.github.io/DataSciencePreparation/LLM/inference-arch/prefill-vs-decode/](https://s-samarth.github.io/DataSciencePreparation/LLM/inference-arch/prefill-vs-decode/)

* [https://www.spheron.network/blog/deploy-mooncake-kvcache-disaggregated-vllm-gpu-cloud/](https://www.spheron.network/blog/deploy-mooncake-kvcache-disaggregated-vllm-gpu-cloud/)

* [https://openmetal.io/gpu-servers-clusters/](https://openmetal.io/gpu-servers-clusters/)

* [https://recipes.vllm.ai/Qwen/Qwen3.6-27B](https://recipes.vllm.ai/Qwen/Qwen3.6-27B)

## II.

* [https://llm-academy.dev/inference/vllm-vs-sglang/](https://llm-academy.dev/inference/vllm-vs-sglang/)

* [https://particula.tech/blog/sglang-vs-vllm-inference-engine-comparison](https://particula.tech/blog/sglang-vs-vllm-inference-engine-comparison)

* [https://www.aimadetools.com/blog/sglang-vs-vllm/](https://www.aimadetools.com/blog/sglang-vs-vllm/)

* [https://www.spheron.network/blog/vllm-vs-sglang-2026/](https://www.spheron.network/blog/vllm-vs-sglang-2026/)

* [https://deploybase.ai/articles/sglang-vs-vllm](https://deploybase.ai/articles/sglang-vs-vllm)

* [https://theaiinsider.tech/2026/05/06/radixark-launches-with-100m-in-seed-funding-led-by-accel-to-grow-sglang-and-democratize-frontier-ai-infrastructure/](https://theaiinsider.tech/2026/05/06/radixark-launches-with-100m-in-seed-funding-led-by-accel-to-grow-sglang-and-democratize-frontier-ai-infrastructure/)

* [https://techcrunch.com/2026/01/22/inference-startup-inferact-lands-150m-to-commercialize-vllm/](https://techcrunch.com/2026/01/22/inference-startup-inferact-lands-150m-to-commercialize-vllm/)

* [https://techsy.io/en/blog/vllm-vs-sglang](https://techsy.io/en/blog/vllm-vs-sglang)

* [https://developer.nvidia.com/blog/nvidia-nvlink-the-scale-up-network-for-ai-factories/](https://developer.nvidia.com/blog/nvidia-nvlink-the-scale-up-network-for-ai-factories/)

* [https://www.servnetuk.com/learn/nvlink-vs-infiniband-explained](https://www.servnetuk.com/learn/nvlink-vs-infiniband-explained)

* [https://llm-d.ai/blog/networking-for-distributed-inference-llm-d](https://llm-d.ai/blog/networking-for-distributed-inference-llm-d)

* [https://www.arccompute.io/resources/arc-blog/infiniband-vs-ethernet-choosing-the-right-network-fabric-for-ai-clusters](https://www.arccompute.io/resources/arc-blog/infiniband-vs-ethernet-choosing-the-right-network-fabric-for-ai-clusters)

* [https://naddod.medium.com/nvidia-gb200-interconnect-architecture-analysis-nvlink-infiniband-and-future-trends-91dc6ba49bf3](https://naddod.medium.com/nvidia-gb200-interconnect-architecture-analysis-nvlink-infiniband-and-future-trends-91dc6ba49bf3)

* [https://www.besthub.dev/articles/which-gpu-cluster-network-wins-for-llm-training-nvlink-infiniband-roce-ddc-compared-6e8ef87d59fe](https://www.besthub.dev/articles/which-gpu-cluster-network-wins-for-llm-training-nvlink-infiniband-roce-ddc-compared-6e8ef87d59fe)

* [https://arxiv.org/abs/2504.03775](https://arxiv.org/abs/2504.03775)

* [https://www.besthub.dev/articles/which-gpu-cluster-network-wins-for-llm-training-nvlink-infiniiband-roce-ddc-compared-6e8ef87d59fe](https://www.besthub.dev/articles/which-gpu-cluster-network-wins-for-llm-training-nvlink-infiniiband-roce-ddc-compared-6e8ef87d59fe)

* [https://www.arccompute.io/resources/arc-blog/infiniband-vs-ethernet-explained](https://www.arccompute.io/resources/arc-blog/infiniband-vs-ethernet-explained)

* [https://www.digitalapplied.com/blog/ai-model-latency-benchmarks-2026-ttft-throughput](https://www.digitalapplied.com/blog/ai-model-latency-benchmarks-2026-ttft-throughput)

* [https://nozcloud.com/ru/blog/articles/2026-iyul-ai-modeli-gpt-5-6-claude-sonnet-5-grok-4-5-kto-silnee.html](https://nozcloud.com/ru/blog/articles/2026-iyul-ai-modeli-gpt-5-6-claude-sonnet-5-grok-4-5-kto-silnee.html)

* [https://dasroot.net/posts/2026/03/context-length-optimization-32k-consumer-hardware/](https://dasroot.net/posts/2026/03/context-length-optimization-32k-consumer-hardware/)

* [https://www.spheron.network/blog/vllm-vs-tensorrt-llm-vs-sglang-benchmarks/](https://www.spheron.network/blog/vllm-vs-tensorrt-llm-vs-sglang-benchmarks/)

* [https://diffnotes.tech/posts/grok-45-cursor-spacexai](https://diffnotes.tech/posts/grok-45-cursor-spacexai)

## III.

* [https://www.kad8.com/ai/gpu-cluster-tco-why-cheap-gpus-can-cost-more-to-run/](https://www.kad8.com/ai/gpu-cluster-tco-why-cheap-gpus-can-cost-more-to-run/)

* [https://gpuleaseindex.com/tools/gpu-tco-analysis](https://gpuleaseindex.com/tools/gpu-tco-analysis)

* [https://introl.com/blog/gpu-infrastructure-tco-5-year-cost-model](https://introl.com/blog/gpu-infrastructure-tco-5-year-cost-model)

* [https://gridstackhub.ai/insights/gpu-tco-reality-check-2026-05](https://gridstackhub.ai/insights/gpu-tco-reality-check-2026-05)

* [https://fp4.dev/silicon/h100-h200-b200-tco/](https://fp4.dev/silicon/h100-h200-b200-tco/)

* [https://www.mercatus-ai.com/blog/100-h100-cluster-tco](https://www.mercatus-ai.com/blog/100-h100-cluster-tco)

* [https://fp4.dev/silicon/h100-h200-b200-tco](https://fp4.dev/silicon/h100-h200-b200-tco)

* [https://gpuinsights.net/gpu-cluster-tco-on-premise-vs-cloud-2026/](https://gpuinsights.net/gpu-cluster-tco-on-premise-vs-cloud-2026/)

* [https://gpuaas.com/blog/real-tco-gpu-cluster-2026](https://gpuaas.com/blog/real-tco-gpu-cluster-2026)

* [https://introl.com/blog/gpu-infrastructure-tco-model-5-year-enterprise-ai-deployment](https://introl.com/blog/gpu-infrastructure-tco-model-5-year-enterprise-ai-deployment)

* [https://www.disintermediate.global/insights/gpu-tco-analysis](https://www.disintermediate.global/insights/gpu-tco-analysis)

* [https://nerdleveltech.com/ai-costs-a-complete-breakdown](https://nerdleveltech.com/ai-costs-a-complete-breakdown)

* [https://www.mercatus-ai.com/blog/h100-depreciation](https://www.mercatus-ai.com/blog/h100-depreciation)

* [https://www.silicondata.com/products/silicon-index](https://www.silicondata.com/products/silicon-index)

* [https://gpusmith.com/articles/en/own-vs-rent-gpus-tco-comparison](https://gpusmith.com/articles/en/own-vs-rent-gpus-tco-comparison)

* [https://www.gmicloud.ai/en/blog/nvidia-h100-gpu-cost-2025-buy-vs-rent-for-data-centers](https://www.gmicloud.ai/en/blog/nvidia-h100-gpu-cost-2025-buy-vs-rent-for-data-centers)

* [https://www.gpucomputeindex.com/](https://www.gpucomputeindex.com/)

* [https://www.cloudzero.com/blog/h100-gpu-cost/](https://www.cloudzero.com/blog/h100-gpu-cost/)

* [https://www.gpu.fm/blog/gpu-total-cost-ownership-buy-vs-rent-2026](https://www.gpu.fm/blog/gpu-total-cost-ownership-buy-vs-rent-2026)

* [https://www.mercatus-ai.com/blog/gpu-roi](https://www.mercatus-ai.com/blog/gpu-roi)

* [https://studiogpu.com/gpu-cluster-total-cost-of-ownership-tco/](https://studiogpu.com/gpu-cluster-total-cost-of-ownership-tco/)

## IV.

* [https://axis-intelligence.com/ai-data-center-energy-consumption-statistics/](https://axis-intelligence.com/ai-data-center-energy-consumption-statistics/)

* [https://ttms.com/growing-energy-demand-of-ai-data-centers-2024-2026/](https://ttms.com/growing-energy-demand-of-ai-data-centers-2024-2026/)

* [https://www.devsustainability.com/p/ai-data-center-energy-in-2026](https://www.devsustainability.com/p/ai-data-center-energy-in-2026)

* [https://vfuturemedia.com/green-tech/ai-data-centers-green-tech-demand-2026/](https://vfuturemedia.com/green-tech/ai-data-centers-green-tech-demand-2026/)

* [https://www.dcntglobal.com/future-of-data-centers-in-2026-ai-energy-cooling-innovations/](https://www.dcntglobal.com/future-of-data-centers-in-2026-ai-energy-cooling-innovations/)

* [https://informedclearly.com/en/ai/54115/ai-data-centers-energy-strategy-2026](https://informedclearly.com/en/ai/54115/ai-data-centers-energy-strategy-2026)

* [https://www.greenfueljournal.com/post/ai-data-center-energy-demand-how-it-s-driving-the-global-renewable-energy-boom-2026-insights](https://www.greenfueljournal.com/post/ai-data-center-energy-demand-how-it-s-driving-the-global-renewable-energy-boom-2026-insights)

* [https://lus.gxtransformer.com/news/transformer-industry-trends-driven-by-data-centers-and-energy-efficiency/](https://lus.gxtransformer.com/news/transformer-industry-trends-driven-by-data-centers-and-energy-efficiency/)

* [https://www.itpro.com/infrastructure/data-centres/edge-data-center-demand-is-skyrocketing-and-generative-ai-and-iot-are-the-key-drivers-fueling-this-rapid-growth](https://www.itpro.com/infrastructure/data-centres/edge-data-center-demand-is-skyrocketing-and-generative-ai-and-iot-are-the-key-drivers-fueling-this-rapid-growth)

* [https://www.404media.co/a-black-hole-of-energy-use-metas-massive-ai-data-center-is-stressing-out-a-louisiana-community/](https://www.404media.co/a-black-hole-of-energy-use-metas-massive-ai-data-center-is-stressing-out-a-louisiana-community/)

* [https://www.techpowerup.com/351059/mitac-computing-advances-agentic-ai-infrastructure-with-6th-gen-amd-epyc-server-cpus](https://www.techpowerup.com/351059/mitac-computing-advances-agentic-ai-infrastructure-with-6th-gen-amd-epyc-server-cpus)

* [https://informedclearly.com/en/energy/51013/ai-data-centers-grid-bottleneck-2026](https://informedclearly.com/en/energy/51013/ai-data-centers-grid-bottleneck-2026)

* [https://www.europarl.europa.eu/RegData/etudes/BRIE/2025/775859/EPRS_BRI(2025)775859_EN.pdf](https://www.europarl.europa.eu/RegData/etudes/BRIE/2025/775859/EPRS_BRI(2025)775859_EN.pdf)

* [https://www.sardinasystems.com/news/ais-electricity-problem-or-what-the-latest-data-actually-shows/](https://www.sardinasystems.com/news/ais-electricity-problem-or-what-the-latest-data-actually-shows/)

* [https://ieu-monitoring.com/editorial/ai-drives-surge-in-data-centre-electricity-demand-testing-power-systems/1041798](https://ieu-monitoring.com/editorial/ai-drives-surge-in-data-centre-electricity-demand-testing-power-systems/1041798)

* [https://www.ess-news.com/2026/01/23/eco-stor-secures-financing-for-238-mwh-battery-in-germany/](https://www.ess-news.com/2026/01/23/eco-stor-secures-financing-for-238-mwh-battery-in-germany/)

* [https://www.pv-magazine.com/2026/04/23/eu-moves-to-restrict-funding-for-projects-using-inverters-from-high-risk-suppliers/](https://www.pv-magazine.com/2026/04/23/eu-moves-to-restrict-funding-for-projects-using-inverters-from-high-risk-suppliers/)

* [https://futurumgroup.com/insights/ai-capex-2026-the-690b-infrastructure-sprint/](https://futurumgroup.com/insights/ai-capex-2026-the-690b-infrastructure-sprint/)

* [https://intellectia.ai/blog/big-tech-ai-infrastructure-investment-2026](https://intellectia.ai/blog/big-tech-ai-infrastructure-investment-2026)

* [https://vucense.com/ai-intelligence/industry-business/big-tech-650b-ai-spending-sovereign-infrastructure/](https://vucense.com/ai-intelligence/industry-business/big-tech-650b-ai-spending-sovereign-infrastructure/)

* [https://tech-insider.org/big-tech-ai-infrastructure-spending-2026/](https://tech-insider.org/big-tech-ai-infrastructure-spending-2026/)

* [https://tech-insider.org/big-tech-650-billion-ai-infrastructure-capex-2026/](https://tech-insider.org/big-tech-650-billion-ai-infrastructure-capex-2026/)

* [https://www.cnbc.com/2026/02/06/google-microsoft-meta-amazon-ai-cash.html](https://www.cnbc.com/2026/02/06/google-microsoft-meta-amazon-ai-cash.html)

* [https://ts2.tech/en/big-techs-710-billion-outlay-centers-ai-bubble-debate-on-compute-usage/](https://ts2.tech/en/big-techs-710-billion-outlay-centers-ai-bubble-debate-on-compute-usage/)

* [https://www.cnbctv18.com/technology/ai-spending-boom-tech-giants-capex-gdp-smaller-nations-ws-l-19896800.htm](https://www.cnbctv18.com/technology/ai-spending-boom-tech-giants-capex-gdp-smaller-nations-ws-l-19896800.htm)

* [https://cryptobriefing.com/morgan-stanley-ups-tech-giants-capex-forecast-to-805b-for-2026/](https://cryptobriefing.com/morgan-stanley-ups-tech-giants-capex-forecast-to-805b-for-2026/)

* [https://platformonomics.com/2026/04/follow-the-capex-q1-2026-scoreboard/](https://platformonomics.com/2026/04/follow-the-capex-q1-2026-scoreboard/)

* [https://bullishdaily.com/news/ai-data-center-backlash-threatens-tech-capex-energy-crunch](https://bullishdaily.com/news/ai-data-center-backlash-threatens-tech-capex-energy-crunch)

* [https://www.utilityeducation.com/ferc-june-2026-large-load-interconnection-orders](https://www.utilityeducation.com/ferc-june-2026-large-load-interconnection-orders)

* [https://www.kirkland.com/publications/kirkland-alert/2025/11/potential-game-changing-rulemaking-for-data-centers](https://www.kirkland.com/publications/kirkland-alert/2025/11/potential-game-changing-rulemaking-for-data-centers)

* [https://www.mhc.ie/latest/insights/new-large-energy-user-data-centre-grid-connection-policy](https://www.mhc.ie/latest/insights/new-large-energy-user-data-centre-grid-connection-policy)

* [https://enkiai.com/ai-market-intelligence/grid-interconnection-delays-2026-a-threat-to-us-energy/](https://enkiai.com/ai-market-intelligence/grid-interconnection-delays-2026-a-threat-to-us-energy/)

* [https://www.taylorwessing.com/en/insights-and-events/insights/2026/05/netzpaket-der-bundesregierung-neue-regeln-fuer-netzanschluss](https://www.taylorwessing.com/en/insights-and-events/insights/2026/05/netzpaket-der-bundesregierung-neue-regeln-fuer-netzanschluss)

* [https://www.powermag.com/ferc-sets-june-deadline-to-rewrite-large-load-grid-rules-for-ai-era-power-demand/](https://www.powermag.com/ferc-sets-june-deadline-to-rewrite-large-load-grid-rules-for-ai-era-power-demand/)

* [https://mgrid.org/2026/01/01/nerc-releases-q1-2026-reliability-guideline-for-emerging-large-loads-as-data-center-grid-events-mount/](https://mgrid.org/2026/01/01/nerc-releases-q1-2026-reliability-guideline-for-emerging-large-loads-as-data-center-grid-events-mount/)

* [https://mgrid.org/2026/01/30/nerc-2026-224-gw-peak-demand-data-centers-strain-grid/](https://mgrid.org/2026/01/30/nerc-2026-224-gw-peak-demand-data-centers-strain-grid/)

* [https://www.linkedin.com/pulse/june-2026-integrated-infrastructure-board-briefing-e-shepard-6obfe](https://www.linkedin.com/pulse/june-2026-integrated-infrastructure-board-briefing-e-shepard-6obfe)

* [https://www.iea.org/reports/electricity-2026](https://www.iea.org/reports/electricity-2026)

* [https://alcapitaladvisory.com/research/intelligence/ai-infrastructure.html](https://alcapitaladvisory.com/research/intelligence/ai-infrastructure.html)

* [https://axis-intelligence.com/ai-data-center-statistics/](https://axis-intelligence.com/ai-data-center-statistics/)

* [https://intelligence.uptimeinstitute.com/resource/nerc-alert-points-future-grid](https://intelligence.uptimeinstitute.com/resource/nerc-alert-points-future-grid)

* [https://www.iea.org/reports/electricity-2026/executive-summary](https://www.iea.org/reports/electricity-2026/executive-summary)

* [https://informedclearly.com/en/energy/57229/grid-trap-clean-energy-power-lines-2026](https://informedclearly.com/en/energy/57229/grid-trap-clean-energy-power-lines-2026)

* [https://mgrid.org/2026/06/02/state-of-grid-policy-markets-58-gw-added-as-markets-clear-lower-and-demand-response-rules-stabilize/](https://mgrid.org/2026/06/02/state-of-grid-policy-markets-58-gw-added-as-markets-clear-lower-and-demand-response-rules-stabilize/)

* [https://deluair.com/consultancy/insights/electric-grid-capex-2026](https://deluair.com/consultancy/insights/electric-grid-capex-2026)

* [https://www.globalbankingandfinance.com/europe-data-centre-amsterdam/](https://www.globalbankingandfinance.com/europe-data-centre-amsterdam/)

* [https://know.creditsights.com/insights/technology-hyperscaler-capex-2026-estimates/](https://know.creditsights.com/insights/technology-hyperscaler-capex-2026-estimates/)

* [https://www.fi-desk.com/bofa-deluge-of-hyperscaler-issuance-steady-at-us100bn-in-2026/](https://www.fi-desk.com/bofa-deluge-of-hyperscaler-issuance-steady-at-us100bn-in-2026/)

* [https://epicalreviews.com/570-billion-ai-data-centers-reshaping-bond-market/](https://epicalreviews.com/570-billion-ai-data-centers-reshaping-bond-market/)

* [https://www.sageadvisory.com/article/hyperscaler-borrowing-surge-reshapes-ig-credit-markets](https://www.sageadvisory.com/article/hyperscaler-borrowing-surge-reshapes-ig-credit-markets)

* [https://lindelltv.com/the-truth-behind-anti-data-center-rhetoric-isnt-as-clear-as-we-may-hope/](https://lindelltv.com/the-truth-behind-anti-data-center-rhetoric-isnt-as-clear-as-we-may-hope/)

* [https://www.latimes.com/business/story/2026-07-10/big-tech-piles-on-350-billion-in-debt-to-fuel-ai-data-center-race](https://www.latimes.com/business/story/2026-07-10/big-tech-piles-on-350-billion-in-debt-to-fuel-ai-data-center-race)

* [https://gdprlocal.com/ai-regulations-around-the-world/](https://gdprlocal.com/ai-regulations-around-the-world/)

* [https://informedclearly.com/en/energy/51604/iea-energy-security-rd-china-2026](https://informedclearly.com/en/energy/51604/iea-energy-security-rd-china-2026)

* [https://www.microsoft.com/en-us/research/publication/energy-use-of-ai-inference-efficiency-pathways-and-test-time-scaling/](https://www.microsoft.com/en-us/research/publication/energy-use-of-ai-inference-efficiency-pathways-and-test-time-scaling/)

* [https://www.iea.org/reports/state-of-energy-policy-2026](https://www.iea.org/reports/state-of-energy-policy-2026)

* [https://informedclearly.com/en/geopolitics/54247/iea-energy-security-innovation-2026](https://informedclearly.com/en/geopolitics/54247/iea-energy-security-innovation-2026)

* [https://blockonomi.com/hyperscalers-break-u-s-bond-market-with-725b-ai-spending-spree-go-global-for-debt/](https://blockonomi.com/hyperscalers-break-u-s-bond-market-with-725b-ai-spending-spree-go-global-for-debt/)

* [https://www.forbes.com/sites/robertszczerba/2026/07/17/bond-investors-push-back-as-ai-debt-heads-toward-570-billion/](https://www.forbes.com/sites/robertszczerba/2026/07/17/bond-investors-push-back-as-ai-debt-heads-toward-570-billion/)

* [https://www.sageadvisory.com/article/hyperscaler-debt-deluge-the-new-driver-of-ig-spread-pressure](https://www.sageadvisory.com/article/hyperscaler-debt-deluge-the-new-driver-of-ig-spread-pressure)

* [https://www.iese.edu/insight/articles/ai-credit-market-debt/](https://www.iese.edu/insight/articles/ai-credit-market-debt/)

## V.

* [https://www.signalintegrityjournal.com/articles/4268-keysight-advances-ai-networking-with-ultra-ethernet-llr-and-cbfc-interoperability-demonstration-at-ofc-2026](https://www.signalintegrityjournal.com/articles/4268-keysight-advances-ai-networking-with-ultra-ethernet-llr-and-cbfc-interoperability-demonstration-at-ofc-2026)

* [https://arxiv.org/abs/2508.08906](https://arxiv.org/abs/2508.08906)

* [https://blogs.cisco.com/datacenter/ultra-ethernet-for-scalable-ai-network-deployment](https://blogs.cisco.com/datacenter/ultra-ethernet-for-scalable-ai-network-deployment)

* [https://ultraethernet.org/a-letter-from-uecs-chair-looking-ahead-to-2025-and-beyond/](https://ultraethernet.org/a-letter-from-uecs-chair-looking-ahead-to-2025-and-beyond/)

* [https://ai-manual.ru/article/razdelyaem-prefill-i-decode-na-hyperpod-kak-dpd-vyizhimaet-maksimum-iz-vashego-klastera-llm](https://ai-manual.ru/article/razdelyaem-prefill-i-decode-na-hyperpod-kak-dpd-vyizhimaet-maksimum-iz-vashego-klastera-llm)

* [https://www.engineering.com/cornelis-details-amd-based-ai-network-architecture](https://www.engineering.com/cornelis-details-amd-based-ai-network-architecture)

* [https://www.spheron.network/blog/kv-cache-optimization-guide/](https://www.spheron.network/blog/kv-cache-optimization-guide/)

* [https://www.digitalapplied.com/blog/kv-cache-optimization-techniques-2026-engineering-guide](https://www.digitalapplied.com/blog/kv-cache-optimization-techniques-2026-engineering-guide)

* [https://kga-it.com/en/blog/ml-inference-kv-cache-management-2026](https://kga-it.com/en/blog/ml-inference-kv-cache-management-2026)

* [https://www.linuxfoundation.org/press/uec-launches-spec-1.0](https://www.linuxfoundation.org/press/uec-launches-spec-1.0)

* [https://glennklockwood.com/garden/ultra-ethernet](https://glennklockwood.com/garden/ultra-ethernet)

* [https://cloudswit.ch/blogs/exploration-of-ultra-ethernet-consortium-uec/](https://cloudswit.ch/blogs/exploration-of-ultra-ethernet-consortium-uec/)

* [https://ultraethernet.org/ultra-ethernet-consortium-uec-launches-specification-1-0-transforming-ethernet-for-ai-and-hpc-at-scale/](https://ultraethernet.org/ultra-ethernet-consortium-uec-launches-specification-1-0-transforming-ethernet-for-ai-and-hpc-at-scale/)

* [https://www.tomshardware.com/networking/ultra-ethernet-the-data-center-interconnection-of-tomorrow-detailed](https://www.tomshardware.com/networking/ultra-ethernet-the-data-center-interconnection-of-tomorrow-detailed)

* [https://agentmarketcap.ai/blog/2026/04/16/ualink-nvlink-ultra-ethernet-scale-up-interconnect-war-agent-inference](https://agentmarketcap.ai/blog/2026/04/16/ualink-nvlink-ultra-ethernet-scale-up-interconnect-war-agent-inference)

* [https://sampooni.github.io/kv-cache-docs/chapters/ch04-latency.html](https://sampooni.github.io/kv-cache-docs/chapters/ch04-latency.html)

* [https://mlforsystems.org/assets/papers/neurips2024/paper17.pdf](https://mlforsystems.org/assets/papers/neurips2024/paper17.pdf)

* [https://www.infinibandta.org/about-infiniband/](https://www.infinibandta.org/about-infiniband/)

* [https://sampooni.github.io/kv-cache-docs/appendix/f-cxl-technology.html](https://sampooni.github.io/kv-cache-docs/appendix/f-cxl-technology.html)

* [https://arxiv.org/pdf/2511.20172](https://arxiv.org/pdf/2511.20172)

* [https://github.com/xcena-dev/maru](https://github.com/xcena-dev/maru)

## 系统验证层

通过可重复的查询检查数据、来源和影响。

* [在Google上验证：NVIDIA H100上的HBM内存带宽](https://www.google.com/search?q=NVIDIA+H100+HBM+bandwidth)

* [在Bing上验证：GPT-OSS-120B在B200上的MLPerf推理结果](https://www.bing.com/search?q=MLPerf+Inference+v6.0+GPT-OSS-120B+B200)

* [在Yandex上验证：SGLang在Llama 3.1 8B上的H100吞吐量](https://yandex.com/search/?text=SGLang+Llama+3.1+8B+H100+throughput)

---

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