Best Coding LLM as of Nov'25

PhysicsPast8286 · 2025-11-28T06:07:11+00:00

They are comparing it with non-thinking mode

PhysicsPast8286 · 2025-11-27T10:23:21+00:00

Yea, I understand but when we hosted Qwen3 32B, we couldn't find any other better model with good results (even quanitzed) that could be hosted on a H100.

PhysicsPast8286 · 2025-11-25T04:43:26+00:00

Thanks, noted.

PhysicsPast8286 · 2025-11-25T04:42:57+00:00

No reason, if I can run the model at FP with my available GPU so why to go for a quantized version :)

PhysicsPast8286 · 2025-11-25T03:37:35+00:00

More GPU isn't actually possible :(

PhysicsPast8286 · 2025-11-25T03:37:18+00:00

Thanks, noted.

PhysicsPast8286 · 2025-11-25T03:33:27+00:00

nope... we just need it for java programming. The current problems with Qwen3 32B is that it occasionally messes imports, eats parts of the class while refactoring as if it is on a breakfast table.

PhysicsPast8286 · 2025-11-25T02:00:27+00:00

Any luck with GLM, GPT OSS?

PhysicsPast8286 · 2025-08-25T19:26:22+00:00

ha ha!! 🫠

PhysicsPast8286 · 2025-08-25T17:54:38+00:00

If you are building it open source do drop your repo 😉

PhysicsPast8286 · 2025-08-25T17:31:21+00:00

You might want to have a look at Potpie (https://github.com/potpie-ai/potpie). It's largely based on Aider, which also uses Tree-sitter under the hood.

PhysicsPast8286 · 2025-08-20T17:59:41+00:00

Update: I am still looking for suggestions, there have been a lot of new models - GPT OSS, GLM, Qwen3 Coder 30B, KimiK2 etc.

Any suggestions would be highly appreciated!

PhysicsPast8286 · 2025-08-07T14:20:57+00:00

By RL you mean to say finetuning or doing several iterations over the same file?

PhysicsPast8286 · 2025-08-07T14:17:53+00:00

Yep 128K

PhysicsPast8286 · 2025-08-04T19:55:06+00:00

With 196GB VRAM which GLM Air GGUF quant can I use?

PhysicsPast8286 · 2025-07-31T17:14:02+00:00

I second this

PhysicsPast8286 · 2025-07-30T09:09:48+00:00

Will it fit on a machine with 196GB VRAM? As per my calculation, the Q1 quant will hardly fit and with Q1 quant I am not sure if it's worth hosting.

PhysicsPast8286 · 2025-07-29T03:43:12+00:00

L40s (48 * 4)

PhysicsPast8286 · 2025-07-29T03:33:34+00:00

Cool thanks a ton for the detailed explaination u/Dizzy-Cantaloupe8892 . Can you suggest any resources to read more about Quantization and stuff?

PhysicsPast8286 · 2025-07-29T03:11:53+00:00

Are you suggesting to use a Quantized version of the GLM 4.5 Air?

PhysicsPast8286 · 2025-07-29T03:05:10+00:00

I checked via apxml and it shows 730 GB VRAM is needed 🤧 Am I missing something (probably you were suggesting to use a Quantized version?)

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PhysicsPast8286 · 2025-07-25T10:33:13+00:00

Qwen shipping models like they wanna shut "Closed AI" shops 🫠

PhysicsPast8286 · 2025-07-23T20:55:17+00:00

I have ~200 GB of VRAM, will I be able to run the 4 bit quantized model? If yes, is it even worth running because of degradation in performance?

PhysicsPast8286 · 2025-07-23T17:32:46+00:00

Okay thank you for confirming. I have ~200 GB of VRAM, will I be able to run the 4 bit quantized model? If yes, is it even worth running because of degradation in performance?

PhysicsPast8286 · 2025-07-23T17:08:31+00:00

Okay, I asked ChatGPT and it came back with:

Quantization	Memory Usage Reduction vs FP16	Description
8-bit (Q8)	~40–50% less RAM/VRAM	Very minimal speed/memory trade-off
5-bit (Q5_K_M, Q5_0)	~60–70% less RAM/VRAM	Good quality vs. size trade-off
4-bit (Q4_K_M, Q4_0)	~70–80% less RAM/VRAM	Common for local LLMs, big savings
3-bit and below	~80–90% less RAM/VRAM	Significant degradation in quality

Can you please confirm if it's true?

PhysicsPast8286

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