August, 2025 | Theodore C. Tanner Jr.

August 12, 2025August 12, 2025 TCTJr Computational_Economics|Direct Thoughts|General Discussion|General Technology|Random_Thoughts

CEO_OKRs_2_CTO_Metrics

Table Courtesy Of Eric Partaker

Certainty = Clear goal × Defined timeframe × Focused execution

~ SMART Goal Framework

Hello, Oh Dear Readers! First, i hope everyone is safe. Second, I’m back at the keyboard and sippin’ on that Carolina Cold Brew (iced tea) while the Palmettos sway like they’re jammin’ to some Allman Brothers or Grateful Dead (aka noodle dance).

I came across the above image in a blog by Eric Partaker. Here is the link from the originating source: OKRs For CEOs .

Eric Partaker lays out 18 CEO KPIs (Key Performance Indicators) to track as a successful company. As a developer-first CTO who’s attempted to wrangle the multi-headed hydra technical beasts at various huge as well as nascent startups, I’ve always seen tech as the engine room powering the whole ship. So, I’ve remapped these CEO metrics to CTO turf, zeroing in on how we track engineering velocity, system resilience, and innovation to drive those business outcomes.

The framework, if you will, is this:

CEO_Sets_North_Star_Company->CEO_OKRs->CEO_OKRs->CTO_KPIs->CTO_Metrics

OKRs (Objectives and Key Results) are a goal-setting framework focused on achieving ambitious, directional goals, while KPIs (Key Performance Indicators) are specific metrics used to track progress and performance. Essentially, OKRs provide the “what” and “how” of achieving a desired outcome, while KPIs provide the “how much” to measure progress. This is also affected by the type of company, for instance, it is extremely difficult if a company has say >90% tied to strict service contracts and staff augmentation to drive this type of behavior, as you are at the mercy of the deliverable and usually a capitated margin.

But here’s the real meat: tracking ain’t just about slapping numbers on a dashboard, it’s about disaggregating the chaos, like splitting LLMs across GPUs for low-latency wins. In a general sense, “disaggregating the chaos” (made-up term) refers to the process of taking a seemingly disordered or unpredictable situation, system, or dataset and breaking it down into its smaller, individual components or elements in order to understand its underlying structure and identify patterns or causes. NOTE: These are more behavioral mappings and metrics and are an adjunct to your real performance of your systems, although I do mention uptime and the like within this context mapping. These will be adjunctive to your core engineering and coder metrics.

We’ll dive deep into tooling like Mixpanel for user-centric product flows (think behavioral analytics on steroids), prometheus for scraping those raw infrastructure metrics (exposing endpoints for time-series data), and grafana for visualizing it all in real-time dashboards that scream actionable insights. Add in all of your engineering metrics and you have “O11y” heaven! Or for some, the other place, because Oh Dear Reader, logging all the metrics leaves no stone unturned. Also, for those that perform R&D Capitalization (if you don’t, you should), this makes the entire process brain-dead even more so than it actually is in most cases.

i’ll weave in how we’d instrument each CTO metric across these prometheus for the low-level scrapes, mixpanel for event-driven user journeys, and grafana to glue it with alerts, panels, and SLO Service Level Objectives queries. Imagine querying prometheus for uptime histograms, funneling mixpanel events for adoption funnels, then grafana-ing it into a unified view with annotations for incidents.

We’ll assume a Kubernetes-orchestrated setup here, ’cause scale’s everything, right? Let’s break it down, OKR,KPI and Metric, with that deeper tracking lens. NOTE: If you want a Cliff’s Notes version, i made a lovely short table. Doom Scroll Oh Dear Reader, to the end.

Revenue Growth Rate → Time to Market / Development Cycle Time
Look, faster launches mean capturing market waves before they crash—I’ve seen AI models go from lab to live in weeks, spiking revenue like a Black Sabbath riff. Track this with prometheus scraping CI/CD pipeline metrics (e.g., expose /metrics endpoints for build durations, deployment frequencies via kube-state-metrics), mixpanel logging feature release events tied to user cohorts (e.g., track ‘feature_deployed’ events with properties like cycle_time), and grafana dashboards plotting histograms of lead times with alerts if cycles exceed SLOs (query: histogram_quantile(0.95, sum(rate(cycle_time_seconds_bucket[5m])) by (le))). This setup lets you correlate dev velocity to revenue spikes, spotting bottlenecks in real-time.
Gross Margin → Cloud Resource Utilization
Overprovisioning clouds is like burning cash on a bonfire—optimize it, and margins soar. We measure utilization as (allocated resources / total capacity) * 100. Prometheus shines here, scraping node-exporter for CPU/memory usage (e.g., rate(container_cpu_usage_seconds_total[5m]) / machine_cpu_cores), while mixpanel could tag resource spikes to user actions (e.g., event ‘resource_spike’ on high-traffic features). Grafana visualizes it with heatmaps of utilization over time, overlaid with cost annotations from cloud APIsset up queries like avg_over_time(node_memory_MemAvailable_bytes[1h]) to flag waste, tying back to margin erosion.
Net Profit Margin → Cost Per Defect
Defects are silent profit killers; track ’em as total fix costs / defect count. Prometheus scrapes app-level metrics like error rates (e.g., sum(rate(errors_total[5m]))), mixpanel captures user-reported bugs via events (e.g., ‘defect_encountered’ with severity props), and grafana panels trend cost-per-defect with log-scale graphs (query: sum(defect_fix_cost) / count(defects_total)). i’ve used this in in past lives to slash rework by 40%, directly padding profits add SLO alerts for defect density thresholds.
Operating Cash Flow → Technical Debt Reduction
Tech debt’s like barnacles on your hull—slows cash gen. Measure reduction as (debt items resolved / total debt) over sprints. Prometheus monitors code health via sonarqube exporters (e.g., rate(tech_debt_score[1d])), mixpanel tracks debt impact on user flows (e.g., ‘legacy_feature_used’ events), grafana dashboards with pie charts of debt categories (query: sum(tech_debt_resolved) by (type)). Chain it with burn rate queries to see cash flow correlations—personal fave: annotate debt spikes with git commit data for root causes.
Cash Runway → Release Burndown
Burndown charts predict if you’ll flame out; track as remaining tasks / velocity. Prometheus scrapes jira-like tools for burndown metrics (custom exporter for story points), mixpanel logs release milestones as events (e.g., ‘sprint_burndown_update’), grafana burndown graphs with forecast lines (query: predict_linear(release_tasks_remaining[7d], 86400 * 30)). This extends runway by flagging delays early. Especially useful in distributed systems to keep AI/ML deploys on rails without blowing budgets.
Customer Acquisition Cost → Feature Usage and Adoption Rate
High adoption turns CAC into a bargain. Measure adoption as (active users / total users) post-feature. Mixpanel owns this with funnel analysis (e.g., events like ‘feature_viewed’ → ‘feature_engaged’), prometheus for backend load from adopters (rate(feature_requests_total[5m])), grafana cohorts panels (query: sum(mixpanel_adoption_rate) over_time[30d]). Tie it to CAC by overlaying acquisition channels—deep dive: use grafana’s prometheus mixin for alerting on adoption drops below 20%. Of course, one must have an initial CAC even to log this process. Many companies have an idea of how much CAC is for a given customer or even at all. This is an imortant number for top of the funnel enterprise value chain.
Customer Lifetime Value → Uptime/Downtime Rate
Uptime’s the glue for LTV—downtime kills loyalty. Track as (total time – downtime) / total time. Prometheus is king for scraping blackbox exporters (up{job="service"}), mixpanel events for user-impacted outages (e.g., ‘downtime_experienced’), grafana SLO burn rate dashboards (query: 1 - (sum(up[1m]) / count(up[1m]))). I’ve seen this boost LTV by 25% in healthcare APIs add heatmaps for downtime patterns correlated to churn events. In past lives i posted our up time every week twitter and linkedin. “six nines” in some cases. Customers loved it.
LTV-to-CAC Ratio → Automated Test Coverage
Coverage ensures quality without tanking LTV. Measure as (tested lines / total lines) * 100. Prometheus scrapes coverage tools like istanbul where: (rate(test_coverage_ratio[1d])), mixpanel for post-deploy stability events, grafana line graphs with thresholds (query: avg(test_coverage)). Balance ratio by alerting on coverage dips—pro tip: integrate with prometheus’ recording rules for LTV projections based on quality metrics.
Net Revenue Retention → System Scalability Index
Scalability prevents revenue leaks. Index as (peak load handled / baseline) with stress tests. Prometheus scales via node_load1 (helps you understand the overall workload on a node, indicating potential resource pressure) and horizontal_pod_autoscaler, mixpanel for user growth events, grafana capacity planning panels (query: sum(rate(requests_total[5m])) / max(capacity)). This preserves NRR by forecasting breaks used it in Watson to handle surges without churn.
Churn Rate → MTTR (Mean Time to Recover)
Quick MTTR curbs churn. Calculate as sum(recovery times) / incidents. Prometheus alerts on incident durations (histogram_quantile(0.5, rate(mttr_seconds_bucket[5m]))), mixpanel ‘recovery_noticed’ events, grafana incident timelines with annotations. Deep: Set up grafana’s prometheus datasource for MTTR trends tied to churn cohorts slashed churn 15% in past gigs.
Avg. Revenue Per Account → Innovation Pipeline Strength
Pipeline fuels ARPA via upsells. Strength as (ideas in pipeline / velocity). Mixpanel tracks idea-to-feature funnels, prometheus for R&D resource metrics, grafana kanban-style boards (query: count(innovation_items) by (stage)). Visualize pipeline health to predict ARPA lifts love the fractal-like patterns in innovation flows. You can predict in some cases three months out.
Burn Multiple → Code Deployment Frequency
Frequent deploys tame burn. Frequency as deploys/day. Prometheus scrapes gitops metrics (rate(deploys_total[1d])), mixpanel for deploy-impact events, grafana frequency histograms. Correlate to burn: query sum(burn_rate) / avg(deploy_freq) keeps multiples low while accelerating ARR.
Sales Cycle Length → Average Response Time
Snappy responses shorten cycles. ART as p95 latency. Prometheus http_request_duration_seconds, mixpanel ‘response_delayed’ events, grafana latency heatmaps (query: histogram_quantile(0.95, rate(http_duration_bucket[5m]))). Tie to sales funnels for cycle reductions—game-changer in demos.
Employee Turnover Rate → Team Attrition Rate
Direct mirror; track as (exits / headcount) quarterly. Mixpanel for engagement surveys (events like 'team_feedback'), prometheus for workload metrics (e.g., oncall_burden), grafana attrition trends with forecasts. Add cultural SLOs high attrition tanks everything, as i’ve learned the hard way.
Net Promoter Score → Customer Satisfaction and Retention
Tech usability drives NPS. Mixpanel NPS events with cohorts, prometheus for support ticket resolutions, grafana score evolutions (query: avg(nps_score[30d])). Deep cohorts: Filter by product features to predict retention.
Days Sales Outstanding → Platform Compatibility Score
Compatibility smooths collections. Score as (successful integrations / attempts). Mixpanel integration events, prometheus compatibility checks, grafana success rate panels. Reduces DSO by minimizing delays—query failure rates for alerts.
Growth Efficiency Ratio → Security Incident Response Time
Fast SIRT protects growth. Like MTTR but security-focused: sum(response times) / incidents. Prometheus security exporters (e.g., falco events), mixpanel breach-impact logs, grafana incident dashboards with SLIs. Ensures efficiency without contractions.
EBITDA → Employee Turnover Rate (Tech Team Focus)
Low tech turnover boosts earnings. Same as 14 but team-specific. Mixpanel for tech satisfaction pulses, prometheus productivity metrics, grafana turnover vs. output correlations. Impacts EBITDA via reduced knowledge loss set up queries like sum(turnover_cost) / ebitda.
Revenue Per Employee (calculated as Total Revenue / Average Headcount) is the North Star. In a frontier company like the ones pushing AI boundaries, this metric isn’t just important; it’s the HOLY GRAIL AFAIC. It slices through the noise to show how efficiently your team’s crankin’ out VPH (value per headcount), spotlighting if your tech wizards are amplifying revenue or just burnin’ cycles on rabbit holes. In frontier land, where innovation’s the oxygen and scale’s the game, hit high numbers here (say, north of 500K per employee like at top AI firms), and you’re signaling hyper-efficiency, attractin’ talent and investors like moths to a flame. Low? You’re leaking potential, bogged down by silos or outdated stacks. Tracking deep-dive: Prometheus scrapes raw productivity signals such as: commits_per_engineer(rate(commits_total{team="engineering"}[1d]) / headcount_gauge), mixin’ in resource efficiency (e.g., avg(cpu_usage_per_pod) to flag idle time). Mixpanel nails the revenue tie-in with event flows (e.g., ‘feature_shipped’ → ‘user_adoption’ → ‘revenue_event’, cohorting by engineer contributions via props like engineer_id). Grafana orchestrates the symphony: Custom dashboards with EPI heatmaps (query: sum(revenue_attributable) / avg(tech_headcount[30d])), overlaid with prometheus histograms for output variance and mixpanel funnels for attribution paths. Set SLOs at 80% EPI (Error Percentage Indicator) threshold alert on dips, annotate with git blame for bottlenecks, and forecast trends with predict_linear for headcount scaling. In frontier mode, this setup’s your war room: It reveals if, for instance, that new LLM fine-tunes payin’ off per engineer-hour, ensurin’ every brain cell’s punchin’ above its weight!

Slotting this as #19 to the lineup ’cause why stop at 18 when the frontier calls for more? Keeps the engine humming!

Here it is in a lovely table for all you excel spreadheet folks:

#	CEO KPI	CTO Metric	Explanation
1	Revenue Growth Rate	Time to Market / Development Cycle Time	Faster launches mean capturing market waves before they crash
2	Gross Margin	Cloud Resource Utilization	Overprovisioning clouds is like burning cash on a bonfire optimize it, and margins soar.
3	Net Profit Margin	Cost Per Defect	Defects are silent profit killers; track ’em as total fix costs / defect count.
4	Operating Cash Flow	Technical Debt Reduction	Tech debt’s like barnacles on your hull slows cash gen.
5	Cash Runway	Release Burndown	Burndown charts predict if you’ll flame out; track as remaining tasks / velocity.
6	Customer Acquisition Cost	Feature Usage and Adoption Rate	High adoption turns CAC into a bargain. Measure adoption as (active users / total users) post-feature.
7	Customer Lifetime Value	Uptime/Downtime Rate	Uptime’s the glue for LTV; downtime kills loyalty. Track as (total time – downtime) / total time. I’ve seen this boost LTV by 25% in healthcare APIs
8	LTV-to-CAC Ratio	Automated Test Coverage	Coverage ensures quality without tanking LTV. Measure as (tested lines / total lines) * 100. Pro tip: integrate with prometheus’ recording rules for LTV projections based on quality metrics.
9	Net Revenue Retention	System Scalability Index	Scalability prevents revenue leaks. Index as (peak load handled / baseline) with stress tests.
10	Churn Rate	MTTR (Mean Time to Recover)	Quick MTTR curbs churn. ProTip: Set up grafana’s prometheus datasource for MTTR trends tied to churn cohorts slashed churn 15% in past gigs.
11	Avg. Revenue Per Account	Innovation Pipeline Strength	Pipeline fuels ARPA via upsells. Strength as (ideas in pipeline / velocity).
12	Burn Multiple	Code Deployment Frequency	Frequent deploys tame burn. Frequency as deploys/day.
13	Sales Cycle Length	Average Response Time	Snappy responses shorten cycles. ART as p95 latency. Tie to sales funnels for cycle reductions game changer in demos.
14	Employee Turnover Rate	Team Attrition Rate	Direct mirror; track as (exits / headcount) quarterly. Add cultural SLOs high attrition tanks everything, as I’ve learned the hard way.
15	Net Promoter Score	Customer Satisfaction and Retention	Tech usability drives NPS. Deep cohorts: Filter by product features to predict retention.
16	Days Sales Outstanding	Platform Compatibility Score	Compatibility smooths collections.
17	Growth Efficiency Ratio	Security Incident Response Time	Fast SIRT protects growth. Like MTTR but security-focused: sum(response times) / incidents.
18	EBITDA	Employee Turnover Rate (Tech Team Focus)	Low tech turnover boosts earnings. Same as 14 but team-specific.
19	Revenue Per Employee	Engineer Productivity Index (EPI)	Revenue Per Employee (calculated as Total Revenue / Average Headcount) is the North Star. In a frontier company like the ones pushin’ AI boundaries, this metric ain’t just important; it’s the holy grail. Hit high numbers here (say, north of $500K per employee like at top AI firms),

Table 1.0 Easy Explanations and Mappings

Whew, that’s the full rundown feels like paddling through a fractal wave, but with these tools, you’re not just tracking; you’re orchestrating a symphony of data!

Until Then,

#iwishyouwater <- Koa Rothman At Teachpoo Largest in 15 years. They Got The Memo.

Ted ℂ. Tanner Jr. (@tctjr) / X

Muzak To Blog By Devo “Duty Now For the Future” and “Q: Are We Not Men?” They were amazing in concert. Lyrics are so cogent for today

August 3, 2025August 3, 2025 TCTJr Artificial_Intelligence|Complexity_Theory|Distributed_Computing|Mathematics|Random_Thoughts

A Survey Of Architectures And Methodologies For Distributed LLM Dissaggregation

Grok 4’s Idea of a Distributed LLM

I was kicking back in my Charleston study this morning, drinking my usual unsweetened tea in a mason jar, the salty breeze slipping through the open window like a whisper from the Charleston Harbor, carrying that familiar tang of low tide “pluff mud” and distant rain. The sun was filtering through the shutters, casting long shadows across my desk littered with old notes on distributed systems engineering, when I dove into this survey on architectures for distributed LLM disaggregation. It’s a dive into the tech that’s pushing LLMs beyond their limits. As i read the numerous papers and assembled commonalities, it hit me how these innovations echo the battles many have fought scaling AI/ML in production, raw, efficient, and unapologetically forward. Here’s the breakdown, with the key papers linked for those ready to dig deeper.

This is essentially the last article in a trilogy. The sister survey is a blog A Survey of Technical Approaches For Distributed AI In Sensor Networks. Then, for a top-level view, i wrote SnakeByte[21]: The Token Arms Race: Architectures Behind Long-Context Foundation Models, so you’ll have all views of a complete system, sensors->distributed compute methods->context engineering.

NOTE: By the time this is published, a whole new set of papers will come out, and i wrote (and read the papers) in a week.

Overview

Distributed serving of LLMs presents significant technical challenges driven by the immense scale of contemporary models, the computational intensity of inference, the autoregressive nature of token generation, and the diverse characteristics of inference requests. Efficiently deploying LLMs across clusters of hardware accelerators (predominantly GPUs and NPUs) necessitates sophisticated system architectures, scheduling algorithms, and resource management techniques to achieve low latency, high throughput, and cost-effectiveness while adhering to Service Level Objectives (SLOs). As you read the LLM survey, think in terms of deployment architectures:

Layered AI System Architecture:

Sensor Layer: IoT, Cameras, Radar, LIDAR, electro-mag, FLIR etc
Edge/Fog Layer: Edge Gateways, Inference Accelerators, Fog Nodes
Cloud Layer: Central AI Model Training, Orchestration Logic, Data Lake

Each layer plays a role in collecting, processing, and managing AI workloads in a distributed system.

Distributed System Architectures and Disaggregation

Modern distributed Large Language Models serving platforms are moving beyond monolithic deployments to adopt disaggregated architectures. A common approach involves separating the computationally intensive prompt processing (prefill phase) from the memory-bound token generation (decode phase). This disaggregation addresses the bimodal latency characteristics of these phases, mitigating pipeline bubbles that arise in pipeline-parallel deployments KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving. As a reminder in LLMs, KV cache stores key and value tensors from previous tokens during inference. In transformer-based models, the attention mechanism computes key (K) and value (V) vectors for each token in the input sequence. Without caching, these would be recalculated for every new token generated, leading to redundant computations and inefficiency.

Systems like Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving propose a KVCache-centric disaggregated architecture with dedicated clusters for prefill and decoding. This separation allows for specialized resource allocation and scheduling policies tailored to each phase’s demands. Similarly, P/D-Serve: Serving Disaggregated Large Language Model at Scale focuses on serving disaggregated LLMs at scale across tens of thousands of devices, emphasizing fine-grained P/D organization and dynamic ratio adjustments to minimize inner mismatch and improve throughput and Time-to-First-Token (TTFT) SLOs. KVDirect: Distributed Disaggregated LLM Inference explores distributed disaggregated inference by optimizing inter-node KV cache transfer using tensor-centric communication and a pull-based strategy.

Further granularity in disaggregation can involve partitioning the model itself across different devices or even separating attention layers, as explored by Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache, which disaggregates attention layers to enable flexible resource scheduling and enhance memory utilization for long contexts. DynaServe: Unified and Elastic Execution for Dynamic Disaggregated LLM Serving unifies and extends both colocated and disaggregated paradigms using a micro-request abstraction, splitting requests into segments for balanced load across unified GPU instances.

The distributed nature also necessitates mechanisms for efficient checkpoint loading and live migration. ServerlessLLM: Low-Latency Serverless Inference for Large Language Models proposes a system for low-latency serverless inference that leverages near-GPU storage for fast multi-tier checkpoint loading and supports efficient live migration of LLM inference states.

Scheduling and Resource Orchestration

Effective scheduling is paramount in distributed LLM serving due to heterogeneous request patterns, varying SLOs, and the autoregressive dependency. Existing systems often suffer from head-of-line blocking and inefficient resource utilization under diverse workloads.

Preemptive scheduling, as implemented in Fast Distributed Inference Serving for Large Language Models, allows for preemption at the granularity of individual output tokens to minimize latency. FastServe employs a novel skip-join Multi-Level Feedback Queue scheduler leveraging input length information. Llumnix: Dynamic Scheduling for Large Language Model Serving introduces dynamic rescheduling across multiple model instances, akin to OS context switching, to improve load balancing, isolation, and prioritize requests with different SLOs via an efficient live migration mechanism.

Prompt scheduling with KV state sharing is a key optimization for workloads with repetitive prefixes. Preble: Efficient Distributed Prompt Scheduling for LLM Serving is a distributed platform explicitly designed for optimizing prompt sharing through a distributed scheduling system that co-optimizes KV state reuse and computation load-balancing using a hierarchical mechanism. MemServe: Context Caching for Disaggregated LLM Serving with Elastic Memory Pool integrates context caching with disaggregated inference, supported by a global scheduler that enhances cache reuse through a global prompt tree-based locality-aware policy. Locality-aware fair scheduling is further explored in Locality-aware Fair Scheduling in LLM Serving, which proposes Deficit Longest Prefix Match (DLPM) and Double Deficit LPM (D2LPM) algorithms for distributed setups to balance fairness, locality, and load-balancing.

Serving multi-SLO requests efficiently requires sophisticated queue management and scheduling. Queue management for slo-oriented large language model serving is a queue management system that handles batch and interactive requests across different models and SLOs using a Request Waiting Time (RWT) Estimator and a global scheduler for orchestration. SLOs-Serve: Optimized Serving of Multi-SLO LLMs optimizes the serving of multi-stage LLM requests with application- and stage-specific SLOs by customizing token allocations using a multi-SLO dynamic programming algorithm. SeaLLM: Service-Aware and Latency-Optimized Resource Sharing for Large Language Model Inference proposes a service-aware and latency-optimized resource sharing framework for large language model inference.

For complex workloads like agentic programs involving multiple LLM calls with dependencies, traditional request-level scheduling is suboptimal. Autellix: An Efficient Serving Engine for LLM Agents as General Programs treats programs as first-class citizens, using program-level context to inform scheduling algorithms that preempt and prioritize LLM calls based on program progress, demonstrating significant throughput improvements for agentic workloads. Parrot: Efficient Serving of LLM-based Applications with Semantic Variable focuses on end-to-end performance for LLM-based applications by introducing the Semantic Variable abstraction to expose application-level knowledge and enable data flow analysis across requests. Conveyor: Efficient Tool-aware LLM Serving with Tool Partial Execution optimizes for tool-aware LLM serving by enabling tool partial execution alongside LLM decoding.

Memory Management and KV Cache Optimizations

The KV cache’s size grows linearly with sequence length and batch size, becoming a major bottleneck for GPU memory and throughput. Distributed serving exacerbates this by requiring efficient management across multiple nodes.

Effective KV cache management involves techniques like dynamic memory allocation, swapping, compression, and sharing. KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving proposes KV-cache streaming for fast, fault-tolerant serving, addressing GPU memory overprovisioning and recovery times. It utilizes microbatch swapping for efficient GPU memory management. On-Device Language Models: A Comprehensive Review presents techniques for managing persistent KV cache states including tolerance-aware compression, IO-recompute pipelined loading, and optimized chunk lifecycle management.

In distributed environments, sharing and transferring KV cache states efficiently are critical. MemServe introduces MemPool, an elastic memory pool managing distributed memory and KV caches. Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache leverages a pooled GPU memory strategy across a cluster. Prefetching KV-cache for LLM Inference with Dynamic Profiling proposes prefetching model weights and KV-cache from off-chip memory to on-chip cache during communication to mitigate memory bottlenecks and communication overhead in distributed settings. KVDirect: Distributed Disaggregated LLM Inference specifically optimizes KV cache transfer using a tensor-centric communication mechanism.

Handling Heterogeneity and Edge/Geo-Distributed Deployment

Serving LLMs cost-effectively often requires utilizing heterogeneous hardware clusters and deploying models closer to users on edge devices or across geo-distributed infrastructure.

Helix: Serving Large Language Models over Heterogeneous GPUs and Networks via Max-Flow addresses serving LLMs over heterogeneous GPUs and networks by formulating inference as a max-flow problem and using MILP for joint model placement and request scheduling optimization. LLM-PQ: Serving LLM on Heterogeneous Clusters with Phase-Aware Partition and Adaptive Quantization supports efficient serving on heterogeneous GPU clusters through adaptive model quantization and phase-aware partition. Efficient LLM Inference via Collaborative Edge Computing leverages collaborative edge computing to partition LLM models and deploy them on distributed devices, formulating device selection and partition as an optimization problem.

Deploying LLMs on edge or geo-distributed devices introduces challenges related to limited resources, unstable networks, and privacy. PerLLM: Personalized Inference Scheduling with Edge-Cloud Collaboration for Diverse LLM Services provides a personalized inference scheduling framework with edge-cloud collaboration for diverse LLM services, optimizing scheduling and resource allocation using a UCB algorithm with constraint satisfaction. Distributed Inference and Fine-tuning of Large Language Models Over The Internet investigates inference and fine-tuning over the internet using geodistributed devices, developing fault-tolerant inference algorithms and load-balancing protocols. MoLink: Distributed and Efficient Serving Framework for Large Models is a distributed serving system designed for heterogeneous and weakly connected consumer-grade GPUs, incorporating techniques for efficient serving under limited network conditions. WiLLM: an Open Framework for LLM Services over Wireless Systems proposes deploying LLMs in core networks for wireless LLM services, introducing a “Tree-Branch-Fruit” network slicing architecture and enhanced slice orchestration.

On the of most recent papers that echo my sentiment from years ago where is i’ve said “Vertically Trained Horizontally Chained” (maybe i should trademark that …) is Small Language Models are the Future of Agentic AI where they lay out the position that specific task LLMs are sufficiently robust, inherently more suitable, and necessarily more economical for many invocations in agentic systems, and are therefore the future of agentic AI. The argumentation is grounded in the current level of capabilities exhibited by these specialized models, the common architectures of agentic systems, and the economy of LM deployment. They further argue that in situations where general-purpose conversational abilities are essential, heterogeneous agentic systems (i.e., agents invoking multiple different models chained horizontally) are the natural choice. They discuss the potential barriers for the adoption of vertically trained LLMs in agentic systems and outline a general LLM-to-specific chained model conversion algorithm.

Other Optimizations and Considerations

Quantization is a standard technique to reduce model size and computational requirements. Atom: Low-bit Quantization for Efficient and Accurate LLM Serving proposes a low-bit quantization method (4-bit weight-activation) to maximize serving throughput by leveraging low-bit operators and reducing memory consumption, achieving significant speedups over FP16 and INT8 with negligible accuracy loss.

Splitting or partitioning models can also facilitate deployment across distributed or heterogeneous resources. SplitLLM: Collaborative Inference of LLMs for Model Placement and Throughput Optimization designs a collaborative inference architecture between a server and clients to enable model placement and throughput optimization. A related concept is Split Learning for fine-tuning, where models are split across cloud, edge, and user devices Hierarchical Split Learning for Large Language Model over Wireless Network. BlockLLM: Multi-tenant Finer-grained Serving for Large Language Models enables multi-tenant finer-grained serving by partitioning models into blocks, allowing component sharing, adaptive assembly, and per-block resource configuration.

Performance and Evaluation Metrics

Evaluating and comparing distributed LLM serving platforms requires appropriate metrics and benchmarks. The CAP Principle for LLM Serving: A Survey of Long-Context Large Language Model Serving suggests a trade-off between serving context length (C), serving accuracy (A), and serving performance (P). Developing realistic workloads and simulation tools is crucial. BurstGPT: A Real-world Workload Dataset to Optimize LLM Serving Systems provides a real-world workload dataset for optimizing LLM serving systems, revealing limitations of current optimizations under realistic conditions. LLMServingSim: A HW/SW Co-Simulation Infrastructure for LLM Inference Serving at Scale is a HW/SW co-simulation infrastructure designed to model dynamic workload variations and heterogeneous processor behaviors efficiently. ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency focuses on a holistic system view to optimize LLM serving in an end-to-end manner, identifying and addressing bottlenecks beyond just LLM inference.

Conclusion

The landscape of distributed LLM serving platforms is rapidly evolving, driven by the need to efficiently and cost-effectively deploy increasingly large and complex models. Key areas of innovation include the adoption of disaggregated architectures, sophisticated scheduling algorithms that account for workload heterogeneity and SLOs, advanced KV cache management techniques, and strategies for leveraging diverse hardware and deployment environments. While significant progress has been made, challenges remain in achieving optimal trade-offs between performance, cost, and quality of service (QOS) across highly dynamic and heterogeneous real-world scenarios.

As the sun set and the neon glow of my screen dimmed, i wrapped this survey up, leaving me pondering the endless horizons of AI/ML scaling like waves crashing on the shore, relentless and full of promise and thinking how incredible it is to be working in these areas where what we have dreamed for decades has come to fruition?