Shadman Ahmed
Software Architect
Software architect who runs AI Bytes. Articles here are produced with AI assistance from the sources they cite; he oversees the site and handles corrections.
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All Articles (32 total)
Run DeepSeek V4.1-Flash Locally: The 2026 GPU Guide
A no-nonsense tutorial on running DeepSeek V4.1-Flash — a 552B-parameter multimodal MoE — on your own hardware, with real VRAM math, quantization tradeoffs, and honest benchmark takeaways.
Run Qwen 3.8-27B Locally: The 2026 GPU Guide
A practical walkthrough for running Qwen 3.8-27B on your own hardware, with VRAM math, quantization tradeoffs, and real throughput numbers from community benchmarks.
Install Ollama in 10 Minutes: Run Any LLM Locally Free
A no-fluff walkthrough for installing Ollama on Mac, Windows, or Linux and running open models like Llama 3.3 and DeepSeek V3 completely offline.
Local LLMs vs Cloud APIs: Cost, Privacy, and Performance
A practical comparison of local LLMs versus cloud APIs from OpenAI, Anthropic, and Google—covering real-world costs, privacy trade-offs, inference performance, and when each approach makes sense.
Run Mistral Large 3 Locally: GPU Setup & Real Benchmarks
A practical guide to running Mistral Large 3 on your own server hardware. VRAM math for the 675B MoE, vLLM setup, and what to expect from FP8 and NVFP4 deployments.
Run Qwen 3.6-27B Locally: 5-Step GPU Setup Guide
A practical tutorial for running Qwen 3.6-27B on your own GPU. Includes AWQ setup, vLLM serving, real tokens-per-second benchmarks, and the pitfalls that eat hours.
DeepSeek V4 Pro Local Setup: The 7-Step GPU Guide
A practical walkthrough for getting DeepSeek V4 Pro running on your own hardware, from picking the right GPU tier to squeezing real tokens-per-second out of quantized weights.
Homebench: The Local LLM Benchmark Tool Worth Your Time
Homebench measures speed, memory, and quality for local LLMs on your own hardware. Here's what the numbers actually reveal about running models at home.
Train a Kick Drum AI Model on 6GB VRAM: Full Linux Guide
A dusty GTX 1660 and a weekend are all you need. This tutorial walks through training a working kick drum diffusion model on 6GB of VRAM, from dataset prep to first generated sample.
Mistral Small 4 Local Install: GPU Specs + Benchmarks
A practical tutorial for running Mistral Small 4 locally, with the real hardware requirements for the 119B-parameter MoE model, Ollama and vLLM setup paths, quantization choices, and benchmarking guidance.
Local AI vs Frontier Labs: The Economics Flip in 2026
Outsourced inference plus local models is undercutting frontier APIs on price. Here's the real math on when self-hosting beats Claude, GPT, and Gemini.
llama.cpp vs Ollama vs LM Studio: GPU Speed Tested
Tokens per second across three popular local LLM runtimes. The winner isn't who you'd expect, and the gap is smaller than the marketing suggests.