Since you mention using Ollama, you probably aren’t running actual deepseek on your pc. Ollama took a Qwen model that was finetuned using deepseek output and named it deepseek.
Those are pretty out of date models at this point. Right now, the model most people would recommend for consumer gaming hardware is Qwen 3.6 27b.
I actually tried Qwen 3.6 27B but it wouldn’t quite fit in my 6900XT so I had to go down to the 14B. I don’t have the tools or the skillset to really test the capabilities of an LLM but with some very rudimentary system prompts it felt quite natural to me. Shockingly natural considering that talking to a real LLM running on my own PC felt like it was smarter than the Majel Barrett computer on Star Trek:TNG…
Oh yeah, 16 GB of VRAM is a strange spot to be in. Most of the focus goes onto the models that fit in 24 GB cards.
Qwen 3.6 35b-a3b is pretty solid when you don’t have enough VRAM for a dense model. Most of the weights can be left in RAM and it still runs really quickly.
Since you mention using Ollama, you probably aren’t running actual deepseek on your pc. Ollama took a Qwen model that was finetuned using deepseek output and named it deepseek.
Those are pretty out of date models at this point. Right now, the model most people would recommend for consumer gaming hardware is Qwen 3.6 27b.
I actually tried Qwen 3.6 27B but it wouldn’t quite fit in my 6900XT so I had to go down to the 14B. I don’t have the tools or the skillset to really test the capabilities of an LLM but with some very rudimentary system prompts it felt quite natural to me. Shockingly natural considering that talking to a real LLM running on my own PC felt like it was smarter than the Majel Barrett computer on Star Trek:TNG…
Oh yeah, 16 GB of VRAM is a strange spot to be in. Most of the focus goes onto the models that fit in 24 GB cards.
Qwen 3.6 35b-a3b is pretty solid when you don’t have enough VRAM for a dense model. Most of the weights can be left in RAM and it still runs really quickly.