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…
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.…
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