Performance Optimization Techniques

Microsoft just changed the game for local LLM inference! 🤯 They have open-sourced bitnet.cpp, a blazing-fast 1-bit LLM inference framework optimized for CPUs. This is a major step forward for running large models locally, without expensive GPUs or cloud costs. Key highlights: → Run 100B parameter models directly on x86 CPUs → Achieve up to 6x faster inference with 82% lower energy consumption → Leverage ternary weights (-1, 0, +1) and 8-bit activations to dramatically reduce memory usage Alongside this, Microsoft also released BitNet b1.58 2B4T, the first functional open-source model using just 1.58 bits for weights while maintaining strong benchmark performance. If you care about efficient AI at scale, this is worth a look. 📄 Paper: https://lnkd.in/ewhgeJYr 📦 Repo: https://lnkd.in/ePMrPnFC -- If this was useful, a like or repost means a lot and helps others find it too! 🙏 Follow me here or on X → https://x.com/DataChaz for takes on LLMs, AI agents, and data science! 🦾

Quantizing is not enough when fine-tuning a model! Even in the lowest precisions, most of the memory is going to be taken by the optimizer state when training that model! One great strategy that emerged recently is QLoRA. The idea is to apply LoRA adapters to quantized models. When the optimizer state is going to be computed, it is only going to be done on the adapter parameters instead of the whole model, and this will save a large amount of memory! The parameters are converted from BFloat16 / Float16 to 4-bits normal float. This quantization strategy comes from the realization that trained model weights tend to be Normal distributed, and we can create quantization buckets using that fact. This allows the compression of the model parameters without too much information loss. When we quantize a model, we need to capture the quantization constants to be able to dequantize the model. We usually capture them in Float32 to avoid as much dequantization error as possible. To compress further the model, we perform a double quantization to quantize the quantization constants to Float8. During the forward pass, because the input tensors are in BFloat16 / Float16, we need to dequantize the quantized parameters to perform the operations. However, during the backward pass, the original weights do not contribute to the computations, and they can remain quantized.

Most people still think of LLMs as “just a model.” But if you’ve ever shipped one in production, you know it’s not that simple. Behind every performant LLM system, there’s a stack of decisions, about pretraining, fine-tuning, inference, evaluation, and application-specific tradeoffs. This diagram captures it well: LLMs aren’t one-dimensional. They’re systems. And each dimension introduces new failure points or optimization levers. Let’s break it down: 🧠 Pre-Training Start with modality. → Text-only models like LLaMA, UL2, PaLM have predictable inductive biases. → Multimodal ones like GPT-4, Gemini, and LaVIN introduce more complex token fusion, grounding challenges, and cross-modal alignment issues. Understanding the data diet matters just as much as parameter count. 🛠 Fine-Tuning This is where most teams underestimate complexity: → PEFT strategies like LoRA and Prefix Tuning help with parameter efficiency, but can behave differently under distribution shift. → Alignment techniques- RLHF, DPO, RAFT, aren’t interchangeable. They encode different human preference priors. → Quantization and pruning decisions will directly impact latency, memory usage, and downstream behavior. ⚡️ Efficiency Inference optimization is still underexplored. Techniques like dynamic prompt caching, paged attention, speculative decoding, and batch streaming make the difference between real-time and unusable. The infra layer is where GenAI products often break. 📏 Evaluation One benchmark doesn’t cut it. You need a full matrix: → NLG (summarization, completion), NLU (classification, reasoning), → alignment tests (honesty, helpfulness, safety), → dataset quality, and → cost breakdowns across training + inference + memory. Evaluation isn’t just a model task, it’s a systems-level concern. 🧾 Inference & Prompting Multi-turn prompts, CoT, ToT, ICL, all behave differently under different sampling strategies and context lengths. Prompting isn’t trivial anymore. It’s an orchestration layer in itself. Whether you’re building for legal, education, robotics, or finance, the “general-purpose” tag doesn’t hold. Every domain has its own retrieval, grounding, and reasoning constraints. ------- Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

Few Lessons from Deploying and Using LLMs in Production Deploying LLMs can feel like hiring a hyperactive genius intern—they dazzle users while potentially draining your API budget. Here are some insights I’ve gathered: 1. “Cheap” is a Lie You Tell Yourself: Cloud costs per call may seem low, but the overall expense of an LLM-based system can skyrocket. Fixes: - Cache repetitive queries: Users ask the same thing at least 100x/day - Gatekeep: Use cheap classifiers (BERT) to filter “easy” requests. Let LLMs handle only the complex 10% and your current systems handle the remaining 90%. - Quantize your models: Shrink LLMs to run on cheaper hardware without massive accuracy drops - Asynchronously build your caches — Pre-generate common responses before they’re requested or gracefully fail the first time a query comes and cache for the next time. 2. Guard Against Model Hallucinations: Sometimes, models express answers with such confidence that distinguishing fact from fiction becomes challenging, even for human reviewers. Fixes: - Use RAG - Just a fancy way of saying to provide your model the knowledge it requires in the prompt itself by querying some database based on semantic matches with the query. - Guardrails: Validate outputs using regex or cross-encoders to establish a clear decision boundary between the query and the LLM’s response. 3. The best LLM is often a discriminative model: You don’t always need a full LLM. Consider knowledge distillation: use a large LLM to label your data and then train a smaller, discriminative model that performs similarly at a much lower cost. 4. It's not about the model, it is about the data on which it is trained: A smaller LLM might struggle with specialized domain data—that’s normal. Fine-tune your model on your specific data set by starting with parameter-efficient methods (like LoRA or Adapters) and using synthetic data generation to bootstrap training. 5. Prompts are the new Features: Prompts are the new features in your system. Version them, run A/B tests, and continuously refine using online experiments. Consider bandit algorithms to automatically promote the best-performing variants. What do you think? Have I missed anything? I’d love to hear your “I survived LLM prod” stories in the comments!

Demystifying PLC Communication Protocols: The Backbone of Industrial Connectivity In today’s smart manufacturing environments, communication protocols are essential for enabling seamless interaction between PLCs, HMIs, sensors, and control systems. They define the language and rules devices use to share data across networks. Without these protocols, devices may be physically connected—but not truly communicating. Here’s a quick breakdown of common PLC communication protocols and where they shine: Common Protocols: Ethernet: High-speed, internet-capable, and supports up to 255 devices. Ideal for modern industrial networks requiring fast, real-time data transfer. Profibus: A robust fieldbus protocol supporting up to 127 devices over 15 km. Widely used in process automation. RS-232 & RS-485: Serial communication protocols for short-distance, point-to-point (RS-232) or multi-drop (RS-485) communication. Still useful in legacy systems. MPI & PPI: Proprietary Siemens protocols. MPI is used for multi-master communication; PPI for basic point-to-point links in smaller applications. ControlNet & DeviceNet: Allen-Bradley/ Rockwell protocols designed for deterministic data exchange (ControlNet) and device-level networking (DeviceNet). USB/PC Adapters: Often used for programming or initial setup. Not typically suited for large-scale communication. Key Factors to Consider: 1. Baud Rate: Speed of data transfer (e.g., Ethernet = 100 Mb/s, RS-232 = 19.2 kb/s) 2. Network Length: How far communication can reach (e.g., ControlNet = 30 km) 3. Number of Nodes: How many devices can connect (e.g., Profibus = 127) Tip: Use Ethernet for high-speed, scalable networks. Choose ControlNet when long-distance and real-time reliability are critical. #PLCProgramming #CommunicationProtocols #IndustrialAutomation #ControlSystems #SmartManufacturing #IIoT #Ethernet #Profibus #RS232 #RS485 #AutomationEngineer #FactoryAutomation

The best way to tune a Large Language Model depends on what you need it to do. Improving instruction-following, building domain expertise, reducing training costs, and aligning responses with specific expectations all require different approaches. That is why it is important to understand the fine-tuning techniques available. Here are 20 methods worth knowing: → 𝗣𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿-𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗺𝗲𝘁𝗵𝗼𝗱𝘀 LoRA, QLoRA, Prefix Tuning, Adapter Tuning, P-Tuning, BitFit, and Soft Prompts adapt a model without updating all its parameters. These methods are useful when computing power, memory, training data, or time is limited. → 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗽𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 Instruction Tuning improves the model’s ability to understand and follow user commands. RLHF, RLAIF, DPO, GRPO, and RLVR use human feedback, AI-generated feedback, preference pairs, group-based rewards, or verifiable outcomes to improve behaviour. → 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗮𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻 Continued Pretraining and Domain-Adaptive Pretraining expose the model to specialised data relevant to a particular industry or use case. This can improve performance in areas such as finance, healthcare, legal workflows, engineering, and customer support. → 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗼𝗽𝘁𝗶𝗺𝗶𝘀𝗮𝘁𝗶𝗼𝗻 Multi-Task Tuning, Federated Tuning, Data Selection, and Curriculum Learning can improve generalisation, privacy, efficiency, and training stability. → 𝗙𝘂𝗹𝗹 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 This approach updates every parameter in the model. It offers maximum flexibility but requires significantly more data, memory, computing power, and training time. The right technique depends on: ↳ The task you are solving ↳ The quality of your training data ↳ The size of the model ↳ Your available computing budget ↳ The level of control you need For many teams, LoRA, QLoRA, or Instruction Tuning is the most practical place to begin. Which fine-tuning technique would you choose for your current use case?

Using the correct index → 70x faster query. Here's exactly what I did to achieve this. I was fetching unprocessed messages from a database table. The rows were queried in batches of 1000 (this was configurable). Since the query was performance-critical, I looked at the execution plan. → Table scan → Filtering → Sorting → Limit The table scan was very expensive because we had millions of rows. It would be much faster if I could use an index. Even better if the index only contained the relevant rows (filtering). And even better if it were a covered index. A covered index contains all the columns needed to satisfy a query without accessing the table. The execution plan after adding the index: → Index only scan → Limit The index-only scan is key here - we're not touching the table at all! Want to learn more about optimizing database queries? Start here: https://lnkd.in/eTSGGVXG I'm noting that this is a specialized optimization, not something you'll do often. But it's definitely a powerful technique for those performance-critical scenarios. --- Sign up for the .NET Weekly with 72K+ other engineers, and get a free Clean Architecture template: https://lnkd.in/e756tX44

I watched a senior engineer spend three weeks quantizing an LLM to 4-bit. The P99 latency got worse. The issue wasn’t the technique; it was treating quantization as a storage problem instead of a memory-bandwidth problem. At Twitter, I spent a month debugging why our "optimized" models ran slower than the originals. The models were smaller. The math was correct. Yet latency regressed. The missing piece: the *unpacking tax*. Here’s the reality most benchmarks hide: Time ≈ Total bytes moved / Memory bandwidth On paper, moving from FP16 (16-bit) to INT4 (4-bit) means 4× less data moving across the memory bus per token. In a memory-bound regime, that translates to 3–4× higher throughput. But there’s a catch. GPUs don’t compute in 4-bit or 8-bit. Those weights are dequantized back to FP16/BF16 in the local cache before computation. That dequantization costs clock cycles and creates production surprises: → High batch sizes: Time saved on memory movement dominates = throughput improves → Batch size of 1: Unpacking overhead dominates = latency gets worse Quantization is not a free win. It’s a tradeoff. If you’re choosing a method, align it with your deployment reality: → GPTQ: Effective for static weights, but sensitive to outliers → AWQ: Preserves critical weights at higher precision for better quality → GGUF: Excellent for CPU/Metal inference, less relevant for H100/A100 clusters This is Part 4 of a deep dive into inference optimization. Previous posts: Memory Wall: https://lnkd.in/gdT26UTV KV Cache: https://lnkd.in/gKkrqVzf Paged Attention: https://lnkd.in/gX5JNZhn Next up: I will break down the closest thing to "cheating physics" in ML - Speculative Decoding. What’s the most expensive quantization mistake you’ve seen in production - latency, quality, or operability?

📚 LC-MS/MS Study Notes | Amino Acid Analysis | Method Development & Validation Every robust LC-MS/MS method begins with a strong understanding of analytical fundamentals—not just instrument operation. As part of my continuous learning, I compiled technical notes covering the complete workflow involved in LC-MS/MS-based amino acid analysis, including: 🔹 Analytical workflow: Sample preparation → Chromatographic separation → ESI ionization → MRM detection 🔹 Selection of precursor and product ions for high selectivity and sensitivity 🔹 Optimization of LC parameters (column chemistry, mobile phase composition, gradient profile, flow rate, injection volume) 🔹 MS/MS source optimization (capillary voltage, gas flow, source/desolvation temperature, collision energy) 🔹 Calibration strategy using internal standards and weighted linear regression 🔹 Critical validation parameters in line with analytical quality requirements: • Specificity & Selectivity • Linearity (R² ≥ 0.995) • Accuracy & Recovery • Precision (Intra-day & Inter-day) • LOD & LOQ • Matrix Effect • Carryover • System Suitability Testing (SST) Developing a reliable LC-MS/MS method requires balancing chromatographic resolution, ionization efficiency, matrix suppression, sensitivity, and reproducibility to generate accurate and defensible analytical results. Learning never stops in analytical science. Every method optimized and every note documented contributes to becoming a better scientist. 💬 I welcome feedback and discussions from fellow analytical chemists on best practices for LC-MS/MS method development, optimization, and validation. Disclaimer: These notes are intended for educational and revision purposes and summarize key analytical concepts. They are not a substitute for a validated laboratory SOP or regulatory method. #LCMSMS #MassSpectrometry #AnalyticalChemistry #MethodDevelopment #MethodValidation #MRM #ESI #AminoAcids #FoodTesting #PharmaceuticalAnalysis #Bioanalysis #AnalyticalMethodValidation #ICHQ2 #ISO17025 #NABL #QualityControl #LaboratoryScience #ScientificLearning #ContinuousImprovement

Researchers from Oxford University just achieved a 14% performance boost in mathematical reasoning by making LLMs work together like specialists in a company. In their new MALT (Multi-Agent LLM Training) paper, they introduced a novel approach where three specialized LLMs - a generator, verifier, and refinement model - collaborate to solve complex problems, similar to how a programmer, tester, and supervisor work together. The breakthrough lies in their training method: (1) Tree-based exploration - generating thousands of reasoning trajectories by having models interact (2) Credit attribution - identifying which model is responsible for successes or failures (3) Specialized training - using both correct and incorrect examples to train each model for its specific role Using this approach on 8B parameter models, MALT achieved relative improvements of 14% on the MATH dataset, 9% on CommonsenseQA, and 7% on GSM8K. This represents a significant step toward more efficient and capable AI systems, showing that well-coordinated smaller models can match the performance of much larger ones. Paper https://lnkd.in/g6ag9rP4 — Join thousands of world-class researchers and engineers from Google, Stanford, OpenAI, and Meta staying ahead on AI http://aitidbits.ai