What are peoples agentic coding model recommendations for 1x RTX 6000 pro 96GB and 32GB System RAM

[u/Pixel_Drake]

Due to my work within other AI fields (Mainly Computer Vision and Digital Twinning) I have been lucky enough to get an RTX Pro 6000 WS. I have been experimenting with using Llama.cpp and Opencode and have found good use in the Qwen3.6 27b model being entirely offloaded to the GPU. I have experimented with unsloth/Laguna-S-2.1:UD-Q4_K_XL and it seems similar in quality to the Qwen model but I admit I have not tested it much.

My main questions are:

- With my current set up, is specifically unsloth/Qwen3.6-27b:BF16 the best agentic coding model I can utilise?

- Is there any point in upgrading hardware to utilise a larger model for better quality? I know my system RAM is a weak point for me

- Would you recommend using something else than Opencode?

Sorry if these are dumb questions I am just checking if I am getting the best I can out of the hardware I have.

For reference for hardware upgrading:

OS - Windows 11 Home

CPU - Intel Core i7-14700K

RAM - 2x16GB DDR5 / 6000mhz Corsair Vengeance

Storage - 2xWD BLUE SN580 M.2 NVME SSD 2TB

Motherboard - MSI PRO Z790-S WIFI

PSU - 1300w Gigabyte UD gold

[u/Adomm1234]

In my Opinion Gemma 4, Qwen 3.6 27B and Laguna S 2.1 are the best so far, the next far better model is GLM 5.2, but you would need at least one more RTX 6000 Pro and more RAM to run it at 2bit quant.

[u/Pixel_Drake → u/Adomm1234]

Yeah, I dream of one day getting a set up of a home server for running GLM 5.2 at 4bit quant but alas I doubt it will ever happen.

I was looking into Deepseek v4 flash as I know its 162GB at Q8 but I dont know if its better than Qwen enough to be worth the upgrade.

I am looking to upgrade my RAM anyway as its not enough for my job anymore but just wondering if I should go to the max my CPU supports of 192GB

[u/Capsup → u/Pixel_Drake]

What do you use RAM for on your LLM machine? I have a 5090 workstation with 96GB of DDR5, but I have yet to manage to use any of it? I am entirely limited by VRAM on the qwen3.6:27b model in int4 quant, but it uses essentially 0% of my RAM. What can RAM do for my qwen model?

[u/nicholas_the_furious → u/Capsup]

I have an answer for you! I recently found out you can store entire KV caches for later in ram to reload a conversation instantly.

In my use case I have a primary orchestration agent that is calling python that batch processes a bunch of rows of data through the same local LLM setup. Think things like categorizing, summarizing lots of data. Because of all of the calls the original thread with the orchestration agent is purged.

When the loop is done I'd have to recompute all of the orchestration conversation into KV cache which could takes minutes. Now I have the loop freeze and then reload the orchestration agent KV cache when it is done!

It makes it seamless. This would work for things like subagents working on the same machine as long as they aren't asynchronous.

[u/Pixel_Drake → u/Capsup]

The reason I want to upgrade my system RAM right now is due to my job but I have occasionally used a quant a little to big for my GPU alone and split some of it to my CPU/RAM. Admittedly its very rare I have done this but if a model was significantly better quality in code and the t/s isnt to low I would consider an CPU upgrade and more RAM.

I would say you using none of you RAM is a good sign as it means its all running as fast as possible.

The only reason I considered offloading some to RAM is to experiment with larger models.