Article timeline

Deploy OpenKruise Agents 0.6.0 on Kubernetes 1.30.4, verify the E2B SDK, warm and on-demand creation, filesystem separation and expiry, investigate blocked resume, and compare Kubernetes SIG Apps Agent Sandbox

A source-level review of DeepInfer's initial public commit, covering DSpark, asynchronous scheduling, Attention DP/CP, MoE EP/EPLB, SWA bounded replay, hardware requirements, and Kubernetes integration boundaries

A read-only analysis of existing DeepSeek-V4.1-Flash-1017 metrics on RTX PRO 5000 72GB and H20-3e, examining request shapes, TTFT, TPOT, queues, caching, and resource usage.

Deploy OpenSandbox 1.1.1 on Kubernetes 1.30.4, validate its official Console, execution, authentication, concurrency, and cleanup, and compare architecture, isolation, snapshots, and enterprise selection with Tencent CubeSandbox

An overview of agent reinforcement learning, its frameworks and environment ecosystem, focusing on veRL Agent Loop, Miles TITO, fully asynchronous training, MoE routing replay, and Kubernetes engineering

Compare DeepSeek, OpenAI, Claude, Gemini, Alibaba Model Studio, Brave, and Tavily tool pricing, and understand Search, Fetch, and total agent-task costs

Replacing one node's primary CNI on an existing VLAN network: physical wiring, shared-IPAM ownership, controller isolation, NotReady diagnosis, and observed connectivity

Review NVIDIA NVCRE's public release history, purpose, and production integration, using standalone NCCL/DCGM tests on two nodes with 16 H20-3e GPUs to discuss admission, upgrade regression, troubleshooting, and Kubernetes dependencies

Compare Firecracker and CubeSandbox through their different responsibilities as a VMM and a sandbox platform, covering isolation, Kubernetes integration, templates, snapshots, network governance, and operating costs

Compare Tencent WorkBuddy Enterprise and Managed Agents, Alibaba QoderWake and DingTalk Wukong, and Feishu Aily through Hema data queries, Meiyijia customer service, and operations reports, covering identity, execution, integration, and acceptance

Kill a running guest process on CubeSandbox v0.7.3, then verify rollback and memory recovery after deleting its original sandbox, with a reproducible script and Light evidence screenshots

Eight-GPU vLLM deployment of native mixed-precision DeepSeek 1017 weights, 36 throughput and latency rounds, tools and vision checks, 1M retrieval, and measured resource curves.

Starting from Omarchy 4's Linux desktop and Agent integration, compare mainstream Agent OS approaches, and actually install the desktop on KubeVirt, connect models, generate configuration, and verify persistence

Use one Adapter to connect multiple CubeSandbox clusters, practicing internal OpenClaw templates, long-task log continuation, controlled MCP, and persistent audit, with five-client real-world testing and Light screenshots

An analysis of HiRoute's task routing, model routing, Jev decision-making, ContextHold, stage competence scoring, and limited failover, with a discussion of official cost-reduction experiments, plus hands-on testing of Docker integration with DSH, protocol compatibility, failover, and restart-resume behavior

An introduction to ChatGPT Dot's release timing, long-running tasks, memory, cloud and local execution, Codex collaboration, permission controls, and Kubernetes enterprise integration and evaluation methods, based on official OpenAI materials

Explains how hour-long agent tasks recover after process crashes, network disconnects, and manual pauses, comparing LangGraph, Temporal, Restate, DBOS, Mastra, and session recovery, with reproducible interruption experiments and Kubernetes architecture recommendations

Documents an isolated pilot of OCE on Kubernetes, from Ceph RBD, PostgreSQL TLS, image preparation, tenant authorization, to office-network Console and real model replies, explaining DNS, probe, and proxy authentication troubleshooting

An analysis of OpenClaw Enterprise's resource model, persistent work queue, per-Agent identity, runtime drivers, storage, and observability based on public source code, discussing the value and limitations of enterprise deployment

Discussing the business prerequisites for Volcano scheduling and rescheduling adoption, from resource views, task grading, cross-department preemption consensus, and pre-deployment instrumentation

Using GPU node pool placement as an example, this explains the Scheduling Framework extension points, Profiles, the boundaries between Extender and standalone schedulers, and validation and rollback methods

Explains VLAN networking, Service translation, and kube-proxy's data path boundaries based on CNI configuration, OVS ports and flow tables, Cilium runtime status, and actual packet captures from an anonymized test cluster

Drawing on Kubernetes source code and public practices from Alibaba and ByteDance, explains serial decision-making and node parallelism, plugin and retry optimization, multi-scheduler sharding, concurrency conflicts, and results validation

Discusses large-scale Kubernetes monitoring from the perspectives of series cardinality, scrape discovery, write and query isolation, alerting, storage, and migration, including hands-on deployment validation of the VictoriaMetrics K8s Stack

Compare model process restart vs Level 1/2 sleep recovery on a single L20, verify short-term throughput after recovery, and analyze Kubernetes DRA sharing boundaries.

Continuously tracking versions, independent evaluations, community weights, and Agent applications of Jev and the System One decision model, preserving results and judgments by week

Using Qwen3.8-27B-FP8 with L20, fixed AIBrix routing, to connect RBG scaling, rolling updates, failure recovery, and client-side TTFT and TPOT evidence.

Model architecture of MiMo-V2.6-Pro-RL, 8-card H20-3e vLLM deployment, text and image acceptance, fixed-length stress testing, and operational boundaries

Systematically introduces how Harbor unifies tasks, Agents, models, isolated environments, verifiers, and trajectories, analyzes its boundaries with Agent Harness, Inspect AI, SWE-bench, LangGraph, and Langfuse, and provides a Kubernetes production deployment solution

MiMo-V2.6-Flash-RL model architecture, SGLang 8-card and vLLM 4-card deployment profiles, fixed-length stress testing, long-context boundaries, multimodal validation, and production deployment recommendations

Deploy Qwen-Image-2.1 on a single NVIDIA L20 in Kubernetes, verifying text rendering, transparent PNGs, and SGLang vs vLLM-Omni throughput, latency, GPU memory, and batch generation workbench

An overview of TypeSafe AI Jev's interface, confidence scores, use cases, community controversy, production architecture, and evaluation methods, explaining its boundaries with LLMs, traditional classifiers, and rule systems

Understanding how Koordlet implements CPU, memory, cache, I/O and network QoS from the control loop, container lifecycle and Linux kernel mechanisms

Understanding Koordinator through resource accounting, QoS, interference governance, and large-scale community practice, and how to safely advance Kubernetes online-offline workload colocation

A production cluster Koordinator Webhook certificate race — its diagnosis, fix, and verification, including sanitized real Grafana metrics, shared Secret configuration, certificate checks, and production acceptance methods

Starting from connection reuse, TLS identity boundaries, rebalancing during scale changes, and policy selection, compare L4 and L7 load balancing approaches for kube-apiserver along with production validation methods

Drawing on public case studies from Alibaba, ByteDance, and overseas sources, this article breaks down control plane, etcd, scheduling, networking, operational risk control, and scale validation methods for Kubernetes at 10,000 nodes

Compare the parallelism capabilities, checkpoint and elasticity boundaries of mainstream multi-node multi-GPU training frameworks, and provide a Kubernetes recovery architecture after node failure, RPO/RTO, drill methods, and a production checklist

Use the OpenTelemetry Collector to aggregate Kubernetes control plane, DNS, and object metrics, integrate with Prometheus, Tempo, and Grafana, and test metric cardinality governance and Span troubleshooting in practice

Explaining OpenTelemetry's Trace, Span, Metric, Log, Context, Resource, OTLP, and Collector using a single Kubernetes request, plus a five-minute experiment that requires no observability backend

Deploy an OpenTelemetry Collector Gateway on a Kubernetes 1.30 cluster, verify Trace, Metric, Kubernetes metadata, Prometheus ServiceMonitor, and Langfuse OTLP ingestion with FastAPI, and provide production deployment recommendations

Use a runnable demo to explain the division of responsibilities among LangChain, LangGraph, and Langfuse, and verify conditional routing, tool calls, slow requests, and error tracing in Kubernetes

From task success, tool trajectories, failure recovery, security governance, cost, and human intervention, establish a reproducible Agent evaluation and scoring system, and explain how Pi, OpenClaw, DeepSeek Harness, and Hermes Agent should be compared

Analyzes the two-layer control plane, scheduling, failure domains, networking, storage, lifecycle, observability, and GPU scenarios of hosting tenant Kubernetes clusters on Kubernetes with KubeVirt, and provides a production reference architecture and acceptance checklist

A systematic introduction to LangGraph's state graphs, persistence, and human approval, as well as Langfuse's Trace, Prompt, Dataset, Evaluation, and Kubernetes self-hosted architecture

A systematic introduction to the differences between Grok Bot, Grok, @grok, and the xAI API, with analysis of its persistent cloud computer, Skills, Routines, multi-Bot collaboration, approvals, security boundaries, and Kubernetes enterprise integration

Introduces TokenHub.com's OpenAI, Anthropic, and Gemini compatible interfaces, multi-model catalog, unified billing, Agent tool integration, and the security and observability boundaries for Kubernetes production use

Introduces OpenRouter's product positioning, request path, model and Provider routing, Fallback, Prompt Cache, costs, privacy, observability, and Kubernetes access methods

Explains HarnessRouter, UHP, Cloud and Community Edition, describing the Agent Harness unified interface, task lifecycle, security boundaries, cost, and its relationship with DSH and LLM Gateway

A systematic review of model aggregation platforms, self-hosted LLM Gateways, enterprise AI Gateways, inference routing and semantic routing—covering principles, representative products, security risks, and selection methods

A systematic introduction to agent short-term and long-term memory, semantic and episodic memory, write and retrieval, temporal updates, forgetting and deletion, major projects, evaluation methods, and Kubernetes production architecture

From Agent Loop, context, tool interfaces, state recovery, sandbox, permissions, verification, and observability, this systematically introduces the principles of Agent Harness, mainstream implementations, and Kubernetes production architecture

SGLang and vLLM deployment, functional validation, throughput/latency, cross-node communication, and long-context testing of the Qwen3.8 2.4T MoE model on four nodes with 32 H20-3e GPUs

DeepSeek-V4.1-Flash eight-card H20-3e deployment configuration, SGLang and vLLM throughput and latency measurements, anomaly analysis, and deployment recommendations

Understand the audit boundaries and deployment practices of CubeSandbox Agent Adapter v0.5 through execution intent persistence, failure lockdown, and manual reconciliation

On two L20 replicas, control cold/hot caches and hot prefixes, compare random, least-request, and prefix-cache, and explain the benefits and costs using routing decisions, real KV hits, queues, and client latency.

Using per-request logs and a light-themed Grafana dashboard to review cold-start scaling, streaming drain, tenant admission, and cache routing, distinguishing actually verified results from production capabilities still pending.

Compare Karmada multi-cluster orchestration with Virtual Kubelet virtual nodes, starting from application object ownership; analyze replicas, capacity, networking, storage, failure recovery, and GPU scenarios, with configuration examples and observability methods

Reuse AIBrix on a real H20 GPU cluster, deploy a two-replica DeepSeek V4 Flash, calibrate TTFT, TPOT, cache and GPU metrics, and form verifiable operational evidence through tiered load testing and replica recovery drills.

Starting from configuration differences, batch changes, and failure recovery in multi-cluster operations, sharing design trade-offs and implementation methods for stability building

Explains APF principles from request classification, concurrent seats, and fair queuing, then uses Kubernetes 1.30.4 four-phase testing, Grafana screenshots, and source code to explain zero-seat rejection and recovery

Illustrated guide to API Priority and Fairness, client-side rate limiting, event rate limiting, resource quotas, GPU task queues, and model gateway rate limiting, with v1 configuration and observability examples

Explains KV Cache computation principles, GPU memory capacity, prefix reuse, tiered caching and P/D transfer, as well as routing, storage, topology and scaling practices on Kubernetes

Record of image dependencies, model startup, client path failures, and abnormal input handling during MiniMax-M3 testing.

MiniMax-M3 BF16 TP8 dual-engine functionality, text stress test, multimodal and deployment verification.

Using the same model snapshot on four H20-3e GPUs to validate SGLang Diffusion and vLLM-Omni, recording generation, concurrency, media checks, and raw artifact retention

Based on the v0.3.0 release tag and multi-architecture images, verifies OpenClaw, DSH, Hermes, and Codex integration, as well as persistent leases, asynchronous jobs, PTY, Checkpoint, Redis HA, multi-tenant authentication, and migration risks

Deployed Ling-3.0-flash BF16 on a single node with 4×141GB H20, covering SGLang and vLLM deployment, functional acceptance, 256K long-context validation, and single-variable A/B testing of speculative decoding.

For independent public release, this report intentionally omits cluster names, internal addresses, image registry names, jump-host details, credentials, and unrelated workloads.

Launcher Pods are suitable for admission validation, but are essentially privileged host management code. Therefore, the security experiment also launched a native microVM using a version-matched Firecracker jailer binary, and checked the h

Kubernetes container metrics cannot cover the full cost of Kata/Firecracker sandboxes. Capacity decisions need to align at least the following three layers of data:

This pattern schedules a privileged Pod with Firecracker as its main process, suitable for hardware admission and lifecycle experiments on isolated nodes. It is not a CRI Runtime, nor is it a public sandbox service that can be safely expose

This guide converts an ordinary Kubernetes Pod into a Kata sandbox hosted by Firecracker. It reproduces an already-validated experimental chain, while keeping node names, image registries, jump hosts, and cluster-specific CNI control inform

The designated experiment subset, namely experiments 1, 2, 3, 5, and 7, was executed on dedicated Workers. Unless explicitly stated as repeated measurements, the results below are smoke tests and compatibility observations.

This guide first downloads artifacts on a networked workstation, then transfers them to an isolated Linux node. The commands in this document deliberately do not depend on any specific jump host, cluster name, image registry, or code reposi

Firecracker and CubeSandbox are not direct substitutes at the same layer. Firecracker is a VMM; CubeSandbox is an agent-oriented sandbox control plane that provides scheduling, images, snapshots, networking, policy, APIs, and SDKs around KV

This experiment runs OpenClaw, DSH, Hermes, and Codex as ordinary Kubernetes Pods, with the runtime selected via runtimeClassName: kata-fc-lab. Each Pod therefore has a Kata/Firecracker microVM isolation boundary around it. The Agent's Cube

From Gang Scheduling, DRA, GPU scale-to-zero, NUMA and observability, this analysis examines the real benefits and adoption boundaries of Kubernetes 1.37 versus 1.35 for training and inference platforms.

Deployed Hy4-preview BF16 on two 8×H20 nodes with SGLang TP16 and vLLM TP8×PP2, covering functional acceptance, RDMA verification, 130K stress testing, and Needle/Agent correctness tests.

Running DeepSeek-V4-Flash-Vision-Exp on a single node with 4×141GB H20 using SGLang Preview, documenting NVMe warm-up, 13m52s cold start, OpenWebUI image chat, and 79-round multimodal load testing.

Hands-on comparison based on isolated installs of OpenClaw 2026.7.1 and 2026.8.1, covering Gateway, Control UI, Workboard, headless Agent, and doctor migration

Baseline benchmark of GLM-5.3 native FP8 with SGLang/vLLM on a single node of 8×141GB H20, comparing output throughput, TTFT, TPOT, and end-to-end latency.

Using real OpenClaw and DeepSeek Harness sessions, sink Agent code execution into CubeSandbox MicroVMs via Skills and controlled plugins that do not require Skills, and actually test policies, lifecycle, and desensitized auditing

Deploy CubeSandbox v0.7.0 in an existing Kubernetes cluster, completing KVM, XFS, Helm, node registration, template building, and sandbox lifecycle acceptance

Verify CubeSandbox's 60ms cold start, KVM/PVM, XFS, eBPF, Helm, node privileges, and production readiness boundaries

Deploy GLM-5.3-Flash on 4×H20, and test SGLang and vLLM for functionality, throughput, long context, Prefix Cache, and OpenWebUI integration.

Using the SGLang Day-0 official image and BF16/FP8 weights, validate the Qwen4 preview architecture, precision and capacity, 262K long context, PLE, MTP, long/short workload colocation, and framework support boundaries on 4×H20

From NVIDIA Device Plugin, Topology Manager, Volcano, Koordinator, HAMi to Kubernetes DRA: evaluating the real capabilities and production adoption paths of GPU device topology-aware scheduling

Starting from a W&B Local instance that ran for two years, combined with real SFT testing on SwanLab, this article explains why experiment tracking is hard to productionize, and how teams should evaluate and accept such tools

Using NCCL AllReduce/All-to-All microbenchmarks, DeepSeek V4 Flash MoE SFT positive/negative comparisons, and six SwanLab runs to explain which parallelization strategies actually benefit from RDMA

Analyze community practices such as KubeFix, K8s Distill, KubeBench, and ITBench, and explain how to prepare data, train models, and complete trustworthy evaluation for Kubernetes documentation Q&A, Manifest generation, and troubleshooting

For readers encountering LLM training for the first time, this guide uses one complete example to explain SFT, Token, Loss, LoRA, QLoRA, Batch, Epoch, Checkpoint, Dense, MoE, DP, TP, PP, EP, ZeRO, and evaluation

Using official images on the same L20 to benchmark SGLang v0.5.16, v0.5.17, and v0.5.18, and analyzing throughput, tail latency, startup, and capacity changes against official Release notes

Deploy SwanLab on Kubernetes, integrate ms-swift, distinguish experiment tracking from infrastructure monitoring, and validate the metrics pipeline and overfitting inflection points with real SFT runs on Qwen3.5, Qwen3.6 MoE, and DeepSeek V4

Use Qwen3.5-4B to complete the LoRA, SwanLab, and blind test loop, then use 8 to 16 H20 GPUs to test DeepSeek V4 Flash's Expert Parallel, cross-node GDRDMA, and overfitting inflection point

Deploy MiniMax-Music3 on a single GPU in Kubernetes, and connect an OpenAI-compatible LLM to automatically generate song titles, structured lyrics, and production descriptions

Records Qwen3.8-27B official FP8 artifacts, vLLM and SGLang images, single-card L20 Kubernetes testing, OpenWebUI integration, and subsequent MTP and long-context A/B

A sanitized troubleshooting record of ncclCommInitRank unhandled cuda error: tracing from GPU, PCIe, and RDMA step by step to cgroup OOM and NVIDIA locked-page failure

Packaging DeepSeek Harness, a browser desktop, and a custom DSH Plugin into a single OCI image, documenting Kubernetes StatefulSet, PVC, NetworkPolicy, Cilium probes, and Tailscale remote access boundaries

A systematic breakdown of DeepSeek Harness from source directories, the Cordis plugin tree, Profiles and Bundles, Agent turns, event-sourced sessions, and capability seams

Build multi-architecture Docker images for DeepSeek Harness and deploy securely with Docker Compose and Kubernetes StatefulSet, resolving EROFS, persistence, native module, and web exposure issues

Records the vLLM and SGLang baselines for GLM-5.2 FP8 on a single node with TP=8, as well as the KV transfer and end-to-end performance comparison between TCP and RDMA under two-node P/D separation

From Tencent WorkBuddy's Agent, Runtime, Session, Skill, MCP, and evaluation capabilities, deconstruct the complete enterprise Agent technology stack from model to sandbox, identity, observability, and delivery

From object storage, parallel file systems, P2P, node caching to GPU Loader, break down the large model weight delivery chain and provide actionable selection and testing routes

Distinguishes P/D KV direct transfer, KV Offload, Prefix-aware routing, and cross-engine shared caching, compares Mooncake, AIBrix, LMCache/llm-d, and Dynamo KVBM, and provides a progressive validation roadmap for DeepSeek V4 Flash

Starting from the real problem of DeepSeek-V4-Pro rolling restarts taking nearly half an hour, this article breaks down the startup critical path to show how to optimize H20 node cache restarts to the five-minute scale, along with the engineering trade-offs of model caching, weight loading, JIT, CUDA Graph, and hot recovery

Design of AI application building, pilot testing, approval, publishing, permissions, and runtime for non-R&D users such as business, legal, and operations teams

The real evolution from JupyterHub/KubeSpawner, code-server, and group-level eight-GPU sharing to Ceph RBD, CephFS, Workspace Operator, and KubeVirt root disks

Analyzes OpenClaw's reusable capabilities, enterprise platform gaps, security risks, recommended architecture, and adoption decisions

Product landscape, implementation principles, development frameworks, risks, and evolution trends for mainstream coding, browser, and enterprise agents

Records resource conditions, vLLM startup flow, performance baseline, same-Pod PD separation testing, and OpenWebUI integration for DeepSeek-V4-Flash-0731 on a single-node eight-card H20

Using NVIDIA GPUs, read-only CephFS, ComfyUI extra_model_paths, Init Container model aliases, and two-tier Ingress to serve a MiniMax-H3 workflow page in a network-restricted Kubernetes environment

On a production Kubernetes cluster using NVIDIA L20, AIBrix v0.7.0, RayClusterFleet, StormService, and vLLM, verify two-machine model parallelism, NIXL P/D separation, eight-node Qwen3-235B FP8, and DeepSeek 70B four-node single-GPU, two-node dual-GPU, and single-node four-GPU topologies

Deploy SGLang Router and two OpenAI-Compatible Mock Workers on a GPU-less Kubernetes 1.30 cluster; test dynamic discovery, round robin, removal and recovery, and Prometheus metrics

Deploy RoleBasedGroup on a Kubernetes 1.30 cluster, validate role dependencies, service discovery, scaling, self-healing, two-node Ray inference, and NIXL P/D disaggregation, and compare RBG with AIBrix using the same images and models

Install Higress in isolation within an existing Kubernetes cluster, understand Controller, Gateway, Console, AI Proxy and observability plugins, and design same-cluster and cross-cluster paths between Higress and AIBrix

Install AIBrix v0.7.0 on a Kubernetes 1.30 cluster, use CPU mock to validate model routing, P/D, StormService, autoscaling, Prometheus metrics, and Higress two-layer gateway chaining

Use Kueue or Volcano to implement GPU quota borrowing between business groups, combine KServe, AIBrix, KEDA and vLLM to build real-time and scheduled elasticity, and establish an observability closed loop from hardware health to per-token cost

In a Kubernetes cluster without Ceph, confine KubeVirt VMs to a single node, use local disks to preserve the complete system environment, and access the desktop from a browser through a restricted noVNC gateway

Provide each user with a persistent Linux workstation with a dedicated RBD root disk, covering host preparation, KubeVirt/CDI, GPU passthrough, snapshots, failure recovery, and production acceptance

Measure the real cost-benefit of preemption notifications, checkpoints, requeueing, and training recovery

Use dual-pool capacity, KV transfer, and real workloads to determine whether P/D separation is worthwhile

Migrating local model validation into load-testable, canary-deployable, observable vLLM/KServe services

Estimate model weights, KV Cache, runtime overhead, maximum concurrency, and per-token cost

Designing Registry, object storage, P2P, and node cache experiments for TB-scale models and 100-node inference pools

Identify GPU idle time from data, CPU, communication, kernel, batching, and platform metrics

Validate admission queues, Gang, fair sharing, preemption, and topology capabilities with identical training workloads

Choosing an AI scheduling platform based on training, inference, topology, queues, ecosystem, and operational boundaries

Fix model, hardware, and load to fairly compare mainstream LLM inference engines

Review training and inference incidents using timelines, fault domains, recovery evidence, and action items

Locate GPU Pending layer by layer: queue admission, scheduling, topology, devices, storage, and elasticity

Locate GPU failures using XID, ECC, NVLink, device loss, NCCL, RDMA, and kubelet evidence

Using platform contracts to integrate Ascend and other domestic accelerators, managing differences in drivers, resource names, images, scheduling, and observability

Evaluate cloud GPUs by inventory, topology, network, storage, Spot, egress, and software stack

Synchronize images, charts, models, drivers, packages, and security metadata in environments without public network access

Validate Agent isolation boundaries with file, credential, network, resource exhaustion, and persistence tests

Comparing the managed boundaries and AI/GPU capabilities of Alibaba Cloud ACK, Tencent Cloud TKE, Huawei Cloud CCE, AWS EKS, Google GKE, and Azure AKS

Layered selection from kubectl, Web UI and GitOps to Rancher, Cluster API, Gardener, Karmada and OCM

Lists the Kubernetes cluster components needed for minimal closed-loop, training, big data, Ray, online inference, and advanced inference scenarios, along with the rationale for introducing them and acceptance criteria

The boundaries of Ray Core, Data, Train, Tune, Serve, Serve LLM, and KubeRay in large model data processing, training, post-training, and online inference

Comparing the layers, composition patterns, and selection boundaries of vLLM, KServe, AIBrix, Ray Serve, BentoML, NVIDIA NIM, and application orchestration frameworks

Spark, Flink, Kafka, Trino, Lakehouse, object storage, Operators, queue scheduling, and integration with LLM training and RAG

From JupyterHub, Kubeflow and managed Workbench to full-GPU, MIG, shared GPU, user Home, object storage and local cache production selection

A systematic selection approach from threat models, lifecycle control planes, gVisor, Kata, microVMs to managed sandboxes

Use Ollama, llama.cpp, LM Studio, LocalAI, MLX-LM, and vLLM/SGLang to quickly validate models, APIs, RAG, and Agents

From Federation evolution to MultiKueue, Karmada, OCM, MCS and Liqo, combined with large-scale GPU training and inference design

Coordinating HPA, KEDA, Kueue, Cluster Autoscaler, and Karpenter to handle GPU cold starts, fragmentation, and interruptions

Quick reference for common terms in GPU, big data, scheduling, training, inference, networking, Agent sandboxes, model artifacts, and reliability

Designing document ingestion, Embedding, vector databases, Reranker, caching, index publishing, and disaster recovery on Kubernetes

Understand continuous batching, prefix cache, quantization, speculative decoding, and multiple parallelism strategies with verifiable methods

Distinguish API Gateway, Gateway API Inference Extension, and model request scheduling; design authentication, quotas, cache awareness, and release strategies

Compare the capabilities, boundaries, and Kubernetes integration approaches of engines such as vLLM, SGLang, TensorRT-LLM, Triton, and llama.cpp

Design multi-machine model replicas, LeaderWorkerSet, Prefill/Decode disaggregation and KV transfer, and compare AIBrix, llm-d, KServe, Dynamo, Ray Serve and the vLLM Production Stack

From GPU Device Plugin, Training Operators, and MLOps to DRA, Inference Gateway, and disaggregated inference

Five reference architectures ranging from small GPU platforms, multi-tenant training, online inference, disaggregated inference to Agent sandboxes

Complete node inspection, device smoke test, model deployment, metric verification, and failure drill on an existing GPU Kubernetes cluster.

Understanding the complete execution path of AI workloads in Kubernetes from controllers, scheduling, devices, networking, and storage

Managing model formats, OCI artifacts, cross-region replication, P2P distribution, node caching, streaming loading, signing, and cold start

Build maintainable GPU nodes from firmware, kernel, drivers, container runtime, to Kubernetes device integration

Plan training and inference clusters from failure domains, node pools, control plane, network, and storage

Integration methods, differences, and selection boundaries for multi-vendor GPUs, TPUs, Gaudi, and cloud AI accelerators on Kubernetes

Understanding the responsibilities, APIs, and migration paths of Kubernetes device discovery, container injection, and dynamic resource allocation

Identity, tenant isolation, Pod security, image and model supply chain, and compliance auditing

Failure models, RPO, RTO, graceful exit, Spot, and recovery drills for AI jobs

Kueue queues, ResourceFlavor, Cohort, priority, preemption, and tenant governance

Version matrix, progressive upgrades, CRDs, backup, disaster recovery, and incident response

Metrics and tracing from user requests and training jobs to Pods, GPUs, network, and storage

Weak-network autonomy, edge device access, model OTA, and cloud-edge management

Object storage, shared files, local NVMe, data loading, and multi-level cache design

Cost attribution, unit economics, capacity forecasting, and elasticity strategies for AI platforms

Layered benchmarks and release gates for GPU, network, storage, training, and inference

Runtime isolation, networking, identity, tool permissions, and audit design for Agent workloads

> Target host: 192.168.1.200; host log timestamps are in CST (UTC+8). This report records environment checks, version planning, multi-stage backups, minor-version-by-minor-version upgrades, fault handling, final verification, and the Kubern

InfiniBand, RoCE, GPUDirect, NCCL, Kubernetes integration, and layered troubleshooting

Pipelines, experiments, model registry, GitOps, and internal platform APIs

Control plane, runtime, routing, scaling, and capacity design for online large model services

Full-GPU, MIG, sharing, topology, affinity, and resource fragmentation governance

Training controllers, Gang Scheduling, collective communication, fault tolerance, and job lifecycle

From current-state assessment to 30-, 60-, and 90-day build plans and long-term capability evolution