Using Local LLMs to Improve Generative AI Models

I got several DMs about running LLMs locally, and the most common question was about the hardware requirements. Here’s what I’m running and what each hardware component does. Dell Pro Max T2 tower: NVIDIA RTX Pro 6000 GPU with 96GB of VRAM, 128GB of system RAM, an Intel Ultra 9 285K, and 2X 3.5TB SanDisk NVMe SSDs. Local AI is a supply chain problem inside your computer case. If you don’t choose each component properly, even small models will run at 2 tokens per second, unusable for generative tasks like coding and more complex agentic use cases. If you want to run LLMs locally, whether for data privacy or to avoid API costs, you need to understand where the bottleneck actually lives. Here is how local inference consumes your system resources: 1. GPU Memory (VRAM). This is the single most critical constraint. It’s your warehouse floor. If your model is 24GB and your GPU only has 16GB of VRAM, you are effectively out of business. You have to offload layers to system RAM (see below), which destroys performance. You need enough VRAM to hold the entire model weights + the KV cache (context window). Consider capacity first, bandwidth second. If you’re running agentic use cases, context becomes a significant consideration for longer workflows. 2. GPU Processor. This is the manufacturing line. Once the model is loaded into VRAM, the GPU cores crunch the matrix multiplications to predict the next token. The RTX Pro 6000 hasn’t let me down yet. More cores = faster generation (tokens/sec). But if you don't have the VRAM to fit the model, all that compute power sits idle waiting for data. 3. System RAM. This is the overflow lot. When you run out of VRAM, the model spills over here. This is a disaster for latency. The bandwidth between your CPU and RAM is a fraction of what’s inside the GPU. Get more than you think you need. 128GB is enough for most generative workloads unless I really tax the context window or the agentic workflow requires context from several turns. 4. CPU. This is just the traffic controller. For pure GPU inference, the CPU mostly feeds data to the graphics card and handles the application logic. It barely moves the needle. Don’t overspend on a CPU. A mid-range CPU is usually sufficient unless you are doing heavy pre-processing or running entirely on the CPU (which I don't recommend). 5. NVMe SSD. The loading dock. Your SSD speed determines how fast you can get the model from cold storage into active memory. It only matters during the initial startup. Once the model is loaded, the SSD does nothing. Don't optimize here, expecting better inference speeds. In the world of local AI, VRAM is the biggest bottleneck. If you can't fit the model entirely on the GPU, no amount of CPU power or SSD speed will save you. However, the other pieces play a significant role in smooth operations. System RAM and GPU speed are a close second, with every other piece playing a supporting role. #DellProMax

Excited to announce my new (free!) white paper: “Self-Improving LLM Architectures with Open Source” – the definitive guide to building AI systems that continuously learn and adapt. If you’re curious how Large Language Models can critique, refine, and upgrade themselves in real-time using fully open source tools, this is the resource you’ve been waiting for. I’ve put together a comprehensive deep dive on: Foundation Models (Llama 3, Mistral, Google Gemma, Falcon, MPT, etc.): How to pick the right LLM as your base and unlock reliable instruction-following and reasoning capabilities. Orchestration & Workflow (LangChain, LangGraph, AutoGen): Turn your model into a self-improving machine with step-by-step self-critiques and automated revisions. Knowledge Storage (ChromaDB, Qdrant, Weaviate, Neo4j): Seamlessly integrate vector and graph databases to store semantic memories and advanced knowledge relationships. Self-Critique & Reasoning (Chain-of-Thought, Reflexion, Constitutional AI): Empower LLMs to identify errors, refine outputs, and tackle complex reasoning by exploring multiple solution paths. Evaluation & Feedback (LangSmith Evals, RAGAS, W&B): Monitor and measure performance continuously to guide the next cycle of improvements. ML Algorithms & Fine-Tuning (PPO, DPO, LoRA, QLoRA): Transform feedback into targeted model updates for faster, more efficient improvements—without catastrophic forgetting. Bias Amplification: Discover open source strategies for preventing unwanted biases from creeping in as your model continues to adapt. In this white paper, you’ll learn how to: Architect a complete self-improvement workflow, from data ingestion to iterative fine-tuning. Deploy at scale with optimized serving (vLLM, Triton, TGI) to handle real-world production needs. Maintain alignment with human values and ensure continuous oversight to avoid rogue outputs. Ready to build the next generation of AI? Download the white paper for free and see how these open source frameworks come together to power unstoppable, ever-learning LLMs. Drop a comment below or send me a DM for the link! Let’s shape the future of AI—together. #AI #LLM #OpenSource #SelfImproving #MachineLearning #LangChain #Orchestration #VectorDatabases #GraphDatabases #SelfCritique #BiasMitigation #Innovation #aiagents

I’ve been using Ollama to run local LLMs for some of my agentic workflows, primarily to keep costs under control. Like many others, I relied on a RAG pipeline to build a knowledge base over internal documents and improve context quality. While it helped, the results were often inconsistent—good in parts, but not always reliable. Recently, I experimented with Unsloth AI, and the experience was genuinely eye-opening. Fine-tuning local LLMs has become dramatically simpler. With Unsloth and Claude Code, you can now fine-tune models directly from your MacBook with a surprisingly smooth, developer-friendly workflow. No heavy infra, no unnecessary complexity—just clean, focused iteration. What stood out most: with the right dataset, a fine-tuned local model can outperform generic, prompt-engineered GPT-4 style usage for specific, well-scoped domains. This feels like a return to first principles—own your data, shape your model, and get deterministic behavior where it matters. If you’re working with RAG today and feeling its limits, fine-tuning is worth a serious look again. Setup guide here for anyone curious: https://lnkd.in/ek56S2f3 Local models are no longer just about cost savings—they’re becoming a real performance lever.

I’ve been exploring a question that has sparked endless debates in the AI community: how do we actually teach LLMs to reason, without leaning on endless human labels or black-box pipelines? My latest blog post dives into this problem head-on, walking through how I built a reasoning LLM completely from scratch - 100% local, no labeled data, no human feedback loops using Group Relative Policy Optimization (GRPO). For those unfamiliar, GRPO is a reinforcement fine-tuning method that replaces expensive human preference labels with deterministic reward functions. That makes it especially powerful for domains like math and logic, where correctness is inherently verifiable. I shared the full pipeline: loading a base open-weight model (Qwen3-4B-Base in my case), applying LoRA for parameter-efficient fine-tuning with UnslothAI, creating a structured reasoning dataset from the Open R1 Math corpus, and then defining simple but effective reward functions that check answers, numbers, and formats. With HuggingFace’s TRL library handling the GRPO trainer, the loop becomes elegant: generate → score → reinforce. The results speak for themselves—what started as a generic base model quickly transformed into a reasoning-focused specialist, capable of producing step-by-step solutions with consistency and accuracy. I also reflect on when to choose reinforcement fine-tuning over supervised fine-tuning, why reward shaping often matters more than raw model size, and how GRPO opens the door for label-free reasoning across other domains like code, logic, or theorem proving. For me, the biggest takeaway was how empowering it felt to run the entire workflow locally, with complete transparency and reproducibility. If you’re curious about the future of reasoning LLMs beyond the hype, this piece offers a grounded, hands-on narrative. https://lnkd.in/g2BdmPJr