Business Process Optimization Consulting

When you ask an LLM a question, latency is shaped by three layers: Hardware, model size, Inference engines and strategies. Choosing the right strategy depends on your bottleneck. The inference process splits into two distinct phases: Prefill and decode Three important metrics to identify the bottleneck. → 𝐓𝐢𝐦𝐞 𝐭𝐨 𝐟𝐢𝐫𝐬𝐭 𝐭𝐨𝐤𝐞𝐧 (𝐓𝐓𝐅𝐓): how long it takes before the users start seeing output. High TTFT = prefill bottleneck. → 𝐓𝐢𝐦𝐞 𝐩𝐞𝐫 𝐨𝐮𝐭𝐩𝐮𝐭 𝐭𝐨𝐤𝐞𝐧 (𝐓𝐏𝐎𝐓): the gap between successive tokens. High TPOT = decode bottleneck. → 𝐓𝐡𝐫𝐨𝐮𝐠𝐡𝐩𝐮𝐭: requests processed per second. If it is low despite acceptable TTFT and TPOT, the GPU is sitting idle, and the bottleneck is scheduling, not compute or memory. Which metric matters most depends on your application. A simple chatbot cares more about TTFT, while a coding agent user may care more about TPOT. 𝐓𝐓𝐅𝐓 𝐭𝐨𝐨 𝐡𝐢𝐠𝐡 (𝐩𝐫𝐞𝐟𝐢𝐥𝐥 𝐛𝐨𝐭𝐭𝐥𝐞𝐧𝐞𝐜𝐤): → 𝘗𝘳𝘰𝘮𝘱𝘵 𝘤𝘢𝘤𝘩𝘪𝘯𝘨: skip recomputing shared prefixes, big win for long system prompt → 𝘍𝘭𝘢𝘴𝘩𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯: restructures how attention is computed and optimizes the data movement memory. It breaks the attention matrix into smaller tiles that fit entirely inside SRAM, 2–4x faster attention computation → 𝘊𝘩𝘶𝘯𝘬𝘦𝘥 𝘱𝘳𝘦𝘧𝘪𝘭𝘭: prevents large prompts from blocking other requests from getting their first token. 𝐓𝐏𝐎𝐓 𝐭𝐨𝐨 𝐡𝐢𝐠𝐡 (𝐝𝐞𝐜𝐨𝐝𝐞 𝐛𝐨𝐭𝐭𝐥𝐞𝐧𝐞𝐜𝐤): → 𝘒𝘝 𝘊𝘢𝘤𝘩𝘦 & 𝘗𝘢𝘨𝘦𝘥𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯: eliminate redundant computation, manage cache memory dynamically. → 𝘚𝘱𝘦𝘤𝘶𝘭𝘢𝘵𝘪𝘷𝘦 𝘥𝘦𝘤𝘰𝘥𝘪𝘯𝘨: small draft model predicts tokens, large model verifies in batch. 2–3x faster → 𝘞𝘦𝘪𝘨𝘩𝘵 𝘲𝘶𝘢𝘯𝘵𝘪𝘻𝘢𝘵𝘪𝘰𝘯: FP32 to INT4/INT8, less data to move per token from HBM. → 𝘒𝘝 𝘤𝘢𝘤𝘩𝘦 𝘲𝘶𝘢𝘯𝘵𝘪𝘻𝘢𝘵𝘪𝘰𝘯 (𝘛𝘶𝘳𝘣𝘰𝘘𝘶𝘢𝘯𝘵): compresses KV activations to ~3 bits. 6x less memory, 8x faster attention on H100. 𝐓𝐡𝐫𝐨𝐮𝐠𝐡𝐩𝐮𝐭 𝐜𝐨𝐥𝐥𝐚𝐩𝐬𝐞𝐬 𝐮𝐧𝐝𝐞𝐫 𝐥𝐨𝐚𝐝 (𝐬𝐜𝐡𝐞𝐝𝐮𝐥𝐢𝐧𝐠-𝐛𝐨𝐮𝐧𝐝): → 𝘊𝘰𝘯𝘵𝘪𝘯𝘶𝘰𝘶𝘴 𝘣𝘢𝘵𝘤𝘩𝘪𝘯𝘨: evicts finished requests instantly, slots in new ones. 10–20x throughput vs static batching. → 𝘗𝘢𝘨𝘦𝘥𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯: also appears here, as dynamic memory paging lets the same hardware serve far more concurrent users. → 𝘔𝘪𝘹𝘵𝘶𝘳𝘦 𝘰𝘧 𝘌𝘹𝘱𝘦𝘳𝘵𝘴: only a subset of expert layers is activated per token, reducing per-token compute at scale. Modern inference engines like vLLM offer most of these techniques out of the box, so we don’t have to implement them ourselves. But understanding these concepts gives us a much better decision, and the next time your model runs slow, you know exactly where to look. Have you tried to implement these? What else should I add?

If you have limited budget for sustainability, this is what I would do: 1. Host internal trainings (Lunch & Learns, Q&A sessions, short workshops). Sustainability starts with awareness. A well-placed 30-minute session can spark engagement across departments. Bonus: Invite guest speakers from your network to keep costs low. 2. Create a mini sustainability task force. Identify passionate employees from different teams who can champion sustainability. Give them ownership over small initiatives—engagement will skyrocket and you are not 'alone'. 3. Set realistic, measurable goals. Set-up monthly meetings, and first focus on the quick wins that build momentum. You don’t need a Net Zero roadmap on day one—start with initiatives like reducing waste, optimizing energy use, or embedding sustainability in procurement decisions. 4. Assess where you can embed sustainability in existing workflows. Instead of creating an entirely new process, align sustainability with existing business strategies—whether it’s procurement, HR, or product development. 5. Assess your skill gaps. Where do you or your team need support? Conduct a quick skills assessment and explore options such as training, industry communities. 6. Maximize free and low-cost resources. Platforms like the UN Global Compact, GRI, and SBTi have free guidelines, templates, and training. 7. Consider bringing in external expertise—strategically. Not everything can be in-house. For complex challenges (like regulatory reporting or Scope 3 emissions), bring in external support in a focused way. Independent sustainability consultants or industry networks can provide high-value insights without breaking the bank. 8. Communicate successes, even small ones. Sustainability thrives on storytelling and transparency. Define your narrative, share wins internally and externally to create momentum—your employees, stakeholders, and even customers will take notice. __ When management sees the positive impact - client feedback, cost savings, employees feeling proud - I think they will be far more willing to invest further in sustainability. 💚 PS. Within your budget, our Dazzle team can connect you with the sustainability experts you need. On-demand. Don't hesitate to drop me a message if you this sounds worth exploring.

𝗧𝗵𝗶𝘀 𝗼𝗻𝗲 𝗳𝗿𝗼𝗻𝘁𝗲𝗻𝗱 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗲𝗹𝗶𝗺𝗶𝗻𝗮𝘁𝗲𝘀 𝗺𝗼𝗿𝗲 𝗰𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲𝘀 𝘁𝗵𝗮𝗻 𝗮𝗻𝘆 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺, 𝗯𝘂𝘁 𝗻𝗼𝘄 𝘆𝗼𝘂 𝘀𝘁𝗮𝘆 𝗶𝗻 𝘁𝗵𝗲 𝗴𝗮𝗺𝗲... ASKED: "𝗬𝗼𝘂𝗿 𝗮𝗽𝗽 𝗶𝘀 𝘀𝗹𝗼𝘄. 𝗪𝗮𝗹𝗸 𝗺𝗲 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝘆𝗼𝘂𝗿 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗱𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝗽𝗿𝗼𝗰𝗲𝘀𝘀." Candidate: "I'd check bundle size and optimize images." Interviewer: "How would you know that's the bottleneck?" Candidate: "Those are usually the problems..." Eliminated. 𝗪𝗲𝗯 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 -> It's systematically identifying bottlenecks, applying targeted fixes, and measuring results. 𝗧𝗵𝗲 𝗳𝗼𝘂𝗿-𝘀𝘁𝗲𝗽 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝘁𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀: 𝟭) 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗳𝗶𝗿𝘀𝘁 → Use Lighthouse, DevTools, Core Web Vitals → Baseline exposes the real problem 𝟮) 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸 → Network, scripting, or rendering? → Profile before fixing 𝟯) 𝗔𝗽𝗽𝗹𝘆 𝘁𝗮𝗿𝗴𝗲𝘁𝗲𝗱 𝗳𝗶𝘅𝗲𝘀 → Code split, optimize JS, reduce reflows → Fix root cause, not symptoms 𝟰) 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗮𝗴𝗮𝗶𝗻 → Re-measure metrics after changes → Performance without validation is guessing 𝗧𝗵𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄-𝗪𝗶𝗻𝗻𝗶𝗻𝗴 𝗔𝗻𝘀𝘄𝗲𝗿: "First, I measure Core Web Vitals and profile the app in Chrome DevTools to find the bottleneck—network, scripting, or rendering. Then I apply targeted fixes like code splitting, JS optimization, or reducing reflows. After every fix, I re-measure to validate improvement instead of guessing." 𝗘𝗻𝗱 𝘄𝗶𝘁𝗵 𝟭 𝗰𝗼𝗻𝗰𝗿𝗲𝘁𝗲 𝗲𝘅𝗮𝗺𝗽𝗹𝗲: Slow dashboard page. → Measured performance first LCP: 5.2s, TTI: 6.8s → Found bottleneck in DevTools Heavy JS execution, not network → Root cause One chart-processing function blocked main thread → Fix Memoization + Web Worker + virtualization → Result LCP: 5.2s → 1.8s TTI: 6.8s → 2.1s Targeted fix. No guessing. 𝗡𝗼𝘄 𝘆𝗼𝘂 𝗸𝗻𝗼𝘄 𝗵𝗼𝘄 𝘁𝗼 𝗱𝗲𝗯𝘂𝗴 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗹𝗶𝗸𝗲 𝗮 𝗽𝗿𝗼! Master concepts like this and get interview-ready with our frontend interview resource. Link in comments👇

Navigating the Sustainability Services Ecosystem As sustainability transitions from a peripheral issue to a central business concern, organizations face increasing pressure from regulators, investors, and consumers. The evolving landscape of sustainability services provides a framework for understanding the diverse range of organizations and platforms supporting sustainability efforts across industries. Modern organizations recognize that a one-size-fits-all approach is insufficient. Instead of relying on a single advisor, companies engage with various actors, including disclosure bodies, emissions software providers, capacity-building networks, and global initiatives. Understanding this ecosystem is essential for implementing effective sustainability strategies that adapt to changing expectations. The sustainability ecosystem can be categorized into five key service areas: Measurement and Disclosure, Capacity Building and Engagement, Strategy and Net Zero Transition, and External Stakeholder Relationships. Each category plays a role in supporting organizations as they design, implement, and track their sustainability efforts. In the Measurement and Disclosure realm, organizations encounter frameworks, standards, and software tools that facilitate transparent reporting of sustainability metrics. Effective measurement ensures credible communication of sustainability efforts to stakeholders, boosting accountability and trust. The Capacity Building and Engagement segment focuses on initiatives that activate employees, educate the public, and promote behavioral change. By embedding sustainability into organizational culture, these platforms help develop a mindset that drives positive change. Consulting firms offer strategy development and transition planning, acting as integrators connecting tools and frameworks to operationalize sustainability commitments. Their expertise is critical for guiding organizations through the complexities of sustainability. External Stakeholder Relationships involve engagement with global initiatives and offset providers, aligning ambitions while offering access to shared methodologies for emissions reduction. These partnerships enhance credibility and enable participation in collective efforts to address climate challenges. As sustainability becomes a core business function, organizations must map the ecosystem of support available to them. Understanding the distinct roles of each actor allows for building the necessary partnerships and infrastructure to deliver impactful outcomes. In conclusion, the Sustainability Services Ecosystem is essential for organizations committed to responsible practices. By thoughtfully engaging with this diverse array of actors, companies can enhance their strategies, drive meaningful change, and contribute to a more sustainable future for all.

Demystifying CPU Performance with Top-Down Microarchitecture Analysis When optimizing performance-critical applications, developers often face an overwhelming number of hardware counters and metrics. Understanding why a program is slow at the CPU level can be extremely challenging. This is where the Top-Down Microarchitecture Analysis Method (TMAM). CPU front-end can allocate four micro-operations (uOps) per cycle and the back-end can retire four uOps per cycle, leading to the concept of a pipeline slot, which represents the hardware resources required to process one uOp. The Top-Down Microarchitecture Analysis Method assumes that each CPU core has four pipeline slots available every clock cycle and uses Performance Monitoring Unit (PMU) events to evaluate how effectively those slots are utilized. At the allocation point—where uOps move from the front-end to the back-end—each slot is classified based on its state during execution. A slot may either be empty due to a stall or filled with a uOp. If empty, the method determines whether the stall was caused by the front-end failing to supply instructions (Front-End Bound) or the back-end being unable to process them (Back-End Bound), with back-end stalls typically resulting from resource limitations such as load buffers. If both stages stall simultaneously, the slot is still categorized as Back-End Bound since resolving front-end issues would not improve performance until the back-end bottleneck is addressed. When a slot is filled with a uOp, it is classified as Retiring if the instruction successfully completes, or Bad Speculation if it is discarded due to events like branch misprediction or pipeline flushes. These four categories—listed below 1️⃣ Retiring This represents the portion of cycles where instructions are successfully executed and retired. A higher percentage here generally indicates good CPU utilization. Examples: Efficient instruction flow Good cache locality Balanced compute workloads 2️⃣ Front-End Bound This occurs when the CPU front-end cannot supply instructions to the pipeline fast enough. Common causes: Instruction cache misses ITLB misses Complex instruction decoding Poor code layout In such cases, optimization may involve: Improving code locality Reducing instruction footprint Using compiler optimizations 3️⃣ Back-End Bound This category indicates the CPU execution units are stalled waiting for resources. Typical bottlenecks: Memory latency (DRAM access) Cache misses Execution unit contention Data dependency chains This is often the largest bottleneck in memory-intensive applications, especially in HPC and data-processing workloads. 4️⃣ Bad Speculation Bad speculation happens when the CPU performs work that eventually gets discarded. Main causes: Branch mispredictions Pipeline flushes Incorrect speculative execution https://lnkd.in/dmtb_iVs

🚧 Most factories don't have a capacity problem—they have a constraint problem. The Theory of Constraints (TOC) teaches a simple but powerful principle: the performance of an entire system is limited by its weakest link. Instead of optimizing every process, optimize the constraint that limits overall throughput. The 5 Focusing Steps of TOC 1️⃣ Identify the system constraint (bottleneck) 2️⃣ Exploit the constraint—maximize its utilization 3️⃣ Subordinate all other processes to support the constraint 4️⃣ Elevate the constraint by increasing its capacity 5️⃣ Repeat the cycle once the constraint shifts Common Manufacturing Constraints ⚙️ Machine Capacity 👷 Skilled Manpower 📦 Material Availability 🛠️ Tooling & Changeovers 🚚 Logistics & Supply Chain 🔍 Quality Inspection Bottlenecks Key TOC Metrics 📈 Throughput (T) – Money generated through sales 📦 Inventory (I) – Money tied up in materials & WIP 💰 Operating Expense (OE) – Cost to convert inventory into throughput Benefits of TOC ✅ Increased Throughput ✅ Reduced Lead Time ✅ Lower WIP Inventory ✅ Improved On-Time Delivery ✅ Better Resource Utilization ✅ Higher Profitability TOC + Lean = Powerful Results ✔️ Eliminate Non-Value-Added Activities ✔️ Protect the Constraint with Buffer Management ✔️ Reduce Changeover Time (SMED) ✔️ Improve Equipment Reliability (TPM) ✔️ Continuously Remove the Next Constraint Key Takeaway Local efficiency does not guarantee system efficiency. A machine running at 100% means little if the plant's bottleneck is elsewhere. World-class manufacturers focus on maximizing flow through the constraint, because improving the bottleneck improves the entire business. 💬 What is the biggest constraint in your plant today—capacity, manpower, quality, maintenance, material availability, or production planning? #TheoryOfConstraints #TOC #ConstraintManagement #OperationalExcellence #Manufacturing #ManufacturingExcellence #LeanManufacturing #IndustrialEngineering #Production #ProductionManagement #PlantManagement #FactoryManagement #ContinuousImprovement #Lean #SixSigma #TPM #SMED #BottleneckAnalysis #Throughput #FlowManagement #CapacityPlanning #Operations #Engineering #SupplyChain #Industry40 #SmartManufacturing #BusinessExcellence #OperationalEfficiency #Productivity #ProcessOptimization #FactoryOptimization #ManufacturingLeadership #Goldratt #ProcessImprovement #ValueStream #ProductionSystem #FactoryPerformance #Kaizen #Leadership #Innovation

How to Spot Performance Bottlenecks in Your C++ Code Using Perf (Linux Edition) Last week, we ran a poll, and performance profiling was the top pick. I’m thrilled because understanding exactly where your program is spending time is one of the most valuable skills for any C++ developer — and yet, tools like perf are still underused by many working on high-performance systems. perf is a Linux profiling tool that lets you observe your program at runtime. It tracks CPU cycles, cache misses, branch mispredictions, and shows you which lines of code consume the most time. For complex systems and performance-critical applications, it’s a game changer. We recently ran a test on a C++ program that fills a large std::vector. Running it under perf clearly showed that line 31 — the push_back loop — was our main bottleneck. This function was responsible for repeated allocations and copying as the vector grew. Thanks to perf, we quickly realized that adding a reserve() before the loop would fix the problem. After making this change and profiling again, our application ran about 3x faster. Simple, targeted optimization guided by profiling. That’s the power of runtime performance analysis. This example perfectly illustrates why integrating perf in your workflow — including in Qt projects — can save hours of guessing, trial-and-error, and frustration. Instead of wondering why your app is slow, you see exactly where the time is being spent and know exactly how to fix it. Key takeaway: Use profiling tools like perf to identify bottlenecks, understand your CPU usage, and apply small, precise changes that multiply your performance. C++ MasterClass, Michel Tonetti, Fabio Galuppo, Gabriel Azevedo Miguel #CppPerformance #PerfLinux #Cpp23 #SystemsProgramming #CppCommunity #Optimization #LowLevelProgramming #CppDev #ProfilingTools #HighPerformanceCpp #EngineeringExcellence #PushBackBottleneck #VectorReserve #CppBestPractices

Corporate Sustainability Do and Don’t Checklist 🌍 Building a credible sustainability strategy requires structure, accountability and full business integration. This checklist highlights practical actions companies should take and common pitfalls to avoid across essential sustainability areas. Start with governance. Embed sustainability into business strategy with clear executive accountability. Avoid treating it as a separate initiative with limited influence. Materiality should be grounded in evidence. Conduct assessments that link sustainability issues to business risk and long-term value. Avoid copying peer strategies without relevance. Targets must be science based, measurable, time bound and clearly owned. Avoid setting broad ambitions without milestones or allocated resources. Ensure high quality data. Develop systems that are audit ready, consistent and transparent. Avoid relying on ad hoc or undocumented data. Focus climate action on reducing real emissions. Prioritize clean energy and supplier engagement. Avoid depending on offsets or relative metrics that disguise growing impact. Manage nature and water with tailored strategies. Actions must reflect specific locations and risks. Avoid symbolic actions that lack operational relevance. Apply circular design to core products and services. Plan for durability, reuse and recovery. Avoid cosmetic changes that do not shift the model. Embed sustainability into procurement and supplier relationships. Offer support and enforce expectations. Avoid one size fits all mandates. Tie employee incentives to sustainability outcomes and build internal capabilities. Avoid assigning responsibility to a single team with no broader engagement. Align investments and capital allocation with sustainability goals. Evaluate strategic and financial value. Avoid treating sustainability as a side expense. Integrate sustainability into enterprise risk frameworks. Use scenario analysis and monitor progress. Avoid ignoring climate or transition risks. Report consistently and transparently using established frameworks. Avoid selective disclosure or inconsistent data. Disclaimer: This is a simplified overview meant to support strategic reflection. Each company must adapt based on its sector, operations and level of ambition. #sustainability #sustainable #business #esg

Mircea Sirghi asked to clarify the problem and solution. Problem: When scaling a Java application by adding more threads, you may encounter a limit in throughput despite the CPU not being fully utilized. This occurs because an underlying shared resource becomes the bottleneck. While it's common to think of bottlenecks in terms of network services or I/O, memory bandwidth can also act as a limiting factor. A frequent but less obvious culprit is the rate at which the application allocates and processes objects in memory. For example, high object allocation rates in Java can overwhelm the memory subsystem, especially in systems with a high allocation rate (e.g., tens of gigabytes per second). This leads to contention over memory bandwidth and reduced efficiency, preventing further scaling. Solution: To identify and address this bottleneck, monitor the server's total memory allocation rate. Tools like Java profilers can help you observe this rate. If the server's allocation rate is extremely high (e.g., 10 GB/s or more), reducing the number of objects allocated can improve throughput. The key insight is that allocating fewer objects reduces pressure on the memory subsystem. Techniques like reusing objects, reducing intermediate allocations, or optimizing data structures can help. This optimization minimizes memory bandwidth contention and allows the application to scale better across threads.

I've been in the sustainability consulting space for a while—not long enough to have witnessed a time when sustainability was an unfamiliar concept to businesses, but long enough to have seen the evolution of voluntary and mandatory regulations alongside changing stakeholder expectations. People often ask me about my key learnings and what I’d share with upcoming sustainability professionals (or, as I like to call them, “planet warriors”—a bit dramatic, I know). So, here’s my take: 1️⃣ It’s an evolving space – Build a strong foundation and be ready to learn every day. New regulations, standards, and frameworks will keep coming, but a solid base makes it easier to adapt. 2️⃣ Not everyone believes climate change is real (yes, really) – Logic and science matter. Emotional storytelling helps, but it’s not enough for everyone. And be ready to explain this every day—over time, you might even start to enjoy it. 3️⃣ Be prepared to solve new problems – You won’t always find case studies or best practices for what you’re tackling. That’s what makes it exciting. 4️⃣ Passion is great, but business sense is key – CXOs don’t buy into just the “feel-good” factor. Learn to speak the language of business. 5️⃣ People will always be confused about what you do – One day you’re talking about climate; the next, about supply chains, human rights, or technology. It’s all interconnected. 6️⃣ Sustainability is not a siloed function – It runs across the organization. Work with teams across departments to drive real impact. 7️⃣ Stay motivated – Some will appreciate your work; others will see it as an added cost. Financial reporting faced skepticism too, until it became the norm. 8️⃣ ESG isn’t just about reporting – It’s about value creation and preservation. Think beyond compliance. 9️⃣ Perfection isn’t always the goal – The 80/20 rule applies here too. Sometimes, “good enough” is better than waiting for the perfect solution. 🔟 Collaboration is everything – No one has all the answers. Brainstorm, share, and build with peers—inside and outside your organization. It makes the work more meaningful (and fun). 1️⃣1️⃣ Turn activism into action – Complaining or blaming won’t bring change. Be part of the solution. What would you add to this list? Let’s hear it! #sustainability #esg #climatechange #big4