I’ve Lost 60% of My Leads in 7 Months — Here’s What I’m Doing Wrong 7 months of freelancing in Algeria, countless ghosted conversations, and one hard truth about how I’m communicating with clients. I ran a simple experiment last…
Quantization is the process of reducing a model’s precision from 16-bit floating point to 4-bit integers, making it small enough to fit in VRAM. But not all 4-bit formats are equal. The difference between Q4_K_M, IQ4_XS, and UD-IQ4_NL isn’t just…
Not every model that claims to be “good for coding” actually is. I tested 9 different LLMs on my RTX 3060 Ti, ran 32 benchmark configurations, and rejected 3 of them outright. The failures taught me more about what matters…
I used to run one model for everything. ChatGPT for code generation, debugging, architecture questions, writing boilerplate – all through a single $20/month subscription. The model was good at some things and mediocre at others, but I didn’t think about…
I tested three model families on the same hardware, same quantization, and same coding tasks. The results surprised me – the “best” model depends entirely on what you’re asking it to do. Gemma 4 is the fastest. Qwen2.5-Coder is the…
When I first tried running a 35B model at 128k context on my 8GB RTX 3060 Ti, the KV cache ate more VRAM than the model itself. The model weights in UD-IQ4_NL quantization fit in about 4GB. The KV cache…
I was stuck at 18 tokens per second on my local 35B model. Usable, but not fast. Every coding task involved waiting – paste a prompt, watch the tokens trickle out, wait for the response. Then I enabled multi-token prediction…
Every local LLM guide tells you to pick a quantization level for your model weights. Almost none of them mention the KV cache – the memory structure that stores every token the model has seen in the current conversation. On…
I tested every model that claims to be good for local coding. Gemma 4, Qwen3-Coder, Qwen3.6 MTP – all of them. The model that actually earned a permanent spot on my machine is one most people haven’t heard of: Ornith-1.0-35B-MTP-APEX-I-Mini.…
I spent three weeks trying to run large language models locally on an RTX 3060 Ti. Every guide I found either assumed I had 24GB of VRAM or told me to buy a bigger GPU. Neither option was happening –…
Every “best local LLM” article in 2026 tells you what to download. None of them show you the actual numbers. They say “this model is fast” without telling you how fast on YOUR hardware, with YOUR quantization, at YOUR context…
