Fahd Mirza

Overview

Fahd Mirza is an AI educator and content creator specializing in local AI deployment, large language model (LLM) fine-tuning, and inference optimization. His work focuses on making advanced AI capabilities accessible through local hardware, leveraging tools like llamacpp and unsloth.

Recent Content & Tutorials

Local Inference & Optimization

• GLM-5.2 MoE RAM Inference: Demonstrated running the 744B parameter GLM-5.2 Mixture-of-Experts model locally in RAM without GPU acceleration.
• Speculative Decoding: Guides on TurboQuant and DFlash speculative inference; advanced stacked speculative decoding (MTP + Ngram) in llamacpp for Qwen3.6.
• Router Mode: Introduction to llamacpp’s new Router Mode for native hot-swappable local LLM switching.
• Quantization: Tutorials on local deployment of MiniMax-M2.7 via llamacpp quantization.
• Efficient Models: Review of MiniCPM-1B for efficient on-device hybrid reasoning.

Fine-Tuning & Training

• Gemma-4 E2B: Tutorials on fine-tuning using custom datasets and the unsloth library.
• SmolLM3: Reviews and analysis of the SmolLM3 model.

Specialized Applications & Frameworks

• EdgeQuake: High-performance local Rust Graph-RAG system using Ollama.
• Structured Data Extraction: Review of Lift by Datalab for schema-constrained local structured data extraction from PDFs.
• Multimodal & Audio:
  • Video LLM techniques.
  • Analysis of NVIDIA’s Audex-2B unified audio-text model from the Nemotron family.
  • Introduction to Bonsai Image for local 1-bit image generation.
• DeepSeek V4: DwarfStar for native Flash inference.

Model Comparisons & Benchmarks

• Concrete Plant Simulator Coding Challenge: Comparative performance analysis of kimi-k3, claude-fable-5, and glm-52 on a complex coding task involving a self-contained concrete plant simulator.
  • See detailed notes: AI Model Comparison: Concrete Plant Simulator Coding Challenge Performance

References

• AI Model Comparison: Concrete Plant Simulator Coding Challenge Performance

Source Notes

• 2026-07-17: AI Model Comparison: Concrete Plant Simulator Coding Challenge Performance · ▶ source
• 2026-07-02: LongCat 2.0: China’s Nvidia-Free 1.6T AI Model Achieves Top Performance · ▶ source
• 2026-06-20: Fine-Tuned Qwen3.6-27B Pi-Reasoning GGUF for Local Agentic Code Debugging · ▶ source
• 2026-06-14: Key AI Developments: Claude Fable 5 Export Controls, DiffusionGemma, Coding Agents · ▶ source
• 2026-06-10: Google QAT vs. Unsloth QAT: Gemma 4 12B Performance Comparison · ▶ source
• 2026-06-10: Gemma 4 Chat Template Fix: Preserving Reasoning for Enhanced Agentic Performance · ▶ source
• 2026-06-09: Gemma 4 Was Broken for Agents - Google Just Fixed It · ▶ source
• 2026-06-06: Miso TTS 8B Emotive Text-to-Speech Model: Installation and Performance Review · ▶ source
• 2026-06-05: NVIDIA Nemotron 3 Ultra: Open LLM Agent Optimizes Fast API Performance · ▶ source
• 2026-06-03: Adaptive PFlash and Hermes Agent: Self-Tuning LLM Prefill for Long Contexts · ▶ source
• 2026-06-02: NVIDIA Cosmos 3: Omnimodal World Model for Physical AI Robotics · ▶ source
• 2026-06-01: MiniMax M3: Open-Weight LLM’s Frontier Coding, Native Multimodality, and Sparse Attention · ▶ source
• 2026-05-30: Weekly AI Developments: Opus 4.8, Step Audio 3, Bonsai Image (May 2026) · ▶ source
• 2026-05-28: DwarfStar: Native DeepSeek V4 Flash Local Inference with Persistent KV Cache · ▶ source
• 2026-05-28: Bonsai Image: Local 1-bit Image Generation Through Extreme Quantization · ▶ source
• 2026-05-26: MiniCPM-1B: Efficient 1B-Parameter LLM for On-Device Hybrid Reasoning · ▶ source
• 2026-05-22: llama.cpp Router Mode: Native Hot-Swappable Local LLM Switching · ▶ source
• 2026-05-22: EdgeQuake: Local Rust Graph-RAG with Ollama for Improved Knowledge Retrieval · ▶ source
• 2026-05-20: MTP + Ngram Stacked Speculative Decoding in Llama.cpp for LLM Inference · ▶ source
• 2026-05-18: GPU Deployment via llama.cpp Quantization · ▶ source
• 2026-05-13: TurboQuant & DFlash: Accelerating Local LLM Inference with Enhanced Context · ▶ source
• 2026-05-11: NVIDIA Nemotron Elastic: Bundling Three LLMs for Flexible Deployment · ▶ source
• 2026-05-10: ERNIE 5.1: Baidu’s AI Model - High Performance, Cost-Efficient, Multimodal Capabilities · ▶ source
• 2026-05-09: Local Deep Research: Local AI Assistant for Comprehensive Private Information Synthesis · ▶ source
• 2026-05-06: Google Gemma 4 MTP Drafters: Accelerating Inference Speed with Speculative Decoding · ▶ source
• 2026-05-03: Luce PFlash: 10x Faster AI Model Prompt Prefill on Local GPUs · ▶ source
• 2026-04-19: Qwen 36-35B Full Precision vs Ollama Quantized Performance-Memory Trade-off · ▶ source
• 2026-04-12: MiniMax M27 Open Source LLM Technical Overview and Deployment Summary · ▶ source
• 2026-04-10: Gemma 4-E2B LLM Fine-Tuning Custom Dataset Unsloth Local Tutorial · ▶ source
• 2026-04-08: Gemma 4-E2B LLM Fine-Tuning: Custom Dataset & Unsloth Local Tutorial · ▶ source
• 2026-04-07: Gemma 4-E2B LLM Fine-Tuning: Custom Dataset & Unsloth Local Tutorial · ▶ source