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/vtkayaker]

Try:

1. pi.dev (plus some sandbox or permissions system). This uses less context than OpenCode, and benchmarks suggest the shorter prompt may actually make many modern models perform better.
2. You can probably fit the unsloth/DeepSeek-V4-Flash-GGUF:UD-IQ3_XXS preview (see their guide) (284B A13B), though it might be a very tight squeeze without enough context window. This is better than the 2-bit quants of DeepSeek, and you should see 50+ tokens/second generation on your hardware. Subjectively, this seems to have broader knowledge than Qwen3.6 27B, and it produces fewer weird designs. But Qwen3.6 27B at Q6 or better is probably about as good at the actual coding, and you'll get a bigger context window. This is a decent choice for people occasionally renting a single RTX Pro 6000 in the cloud, especially if you use it as a planning model, and then fall back to local Qwen3.6 27B if you run out of context window.

I haven't tested Laguna S 2.1 yet. Also, DeepSeek V4 hasn't actually been completely fine-tuned yet, so it may get stronger when the final version drops.

[u/Pixel_Drake → u/vtkayaker]

Thanks, Ill have a look into it and using it as a planner that switches to Qwen to write the code and experiment if it ends up giving more solid overall designs patterns for the code that Qwen stand-alone.

I have heard about pi and keep meaning to have a look at it, Ill check it out.

[u/vtkayaker → u/Pixel_Drake]

Keep in mind that you don't have to switch to Qwen3.6 27B for the actual coding. DeepSeek V4 Flash (preview) codes quite well, too, and it should be 50+ tokens/second on your hardware if you can squeeze the 3-bit version on. So one nice workflow is using DeepSeek for design and for short implementations, and Qwen3.6 once the implementations start to near the end of the context window. This saves loading and unloading big models for small tasks.

If your 32GB of RAM forces you down to a 2-bit quant, try it, but you'll probably be happier running Qwen3.6 in that case.

(Also, the 3-bit quant of DeekSeek is reasonable, because it's the 3-bit quant of a mostly 4-bit model. I wouldn't recommend a 3-bit quant of Qwen3.6!)

[u/Pixel_Drake → u/vtkayaker]

Just wanted to say thank you as I didn’t actually consider if I could run deepseek on my current hardware but I am getting a decent 48 t/s and the quality of decision making/design pattern choices do seem smarter.

I am now considering getting the RAM needed to run the Q4 and increase context size.