I remember when I first tried cramming a massive model onto my NVIDIA GeForce RTX 3060 12GB, honestly it's a total headache trying to balance speed and smarts. For DeepSeek, I would suggest sticking with EXL2 if you can fit it, but here's the technical breakdown:

1. EXL2 Quantization: This is actually the gold standard for NVIDIA cards like yours. It's built on ExLlamaV2 and it's literally so much faster than GGUF because it keeps everything on the GPU. If you can find a 3.5-bit or 4.0-bit EXL2 version that fits in your 12GB VRAM, the speed will be night and day.
2. GGUF (GPT-Generated Unified Format): Use this as ur backup. The main perk is that you can offload some layers to your system RAM if the model is too big, but be careful because once you start using system RAM, the generation speed drops highkey.
3. AWQ (Activation-aware Weight Quantization): It's reallyyy solid for accuracy at 4-bit, but I've found it's sometimes pickier with specific hardware setups compared to EXL2.

Basically, try to find an EXL2 version first to keep that reasoning edge without the lag, but if it OOMs (out of memory), GGUF is your safety net. gl!

Ok so, for your situation, I've spent years messing with budget builds and I've found that DeepSeek-V3 is a beast to tame on a single NVIDIA GeForce RTX 3060 12GB. Since the others already mentioned GGUF and AWQ, honestly, you should look into HQQ (Half-Quadratic Quantization) if you can find the weights.

I mean, I've seen HQQ hold onto that 'reasoning edge' way better than standard 4-bit methods when VRAM is tight. It's basically a lifesaver for mid-range cards cuz it reduces the math errors that make models go loopy at low bitrates. If that's too much of a headache, just grab a 3.5bpw or 4.0bpw version of DeepSeek-Coder-V2-Lite-Instruct—it's way more realistic for 12GB than trying to squeeze the full-fat model. You'll actually get decent tokens per second without your system feeling like it's gonna melt lol.

In my experience, 3.5bpw is the 'sweet spot' where you don't lose much accuracy but still fit the KV cache in your VRAM. Good luck, hope you get it running smoothly!!

Basically, you gotta use DeepSeek-V3 GGUF at Q4_K_M or Q3_K_L... i mean honestly GGUF is your best bet on the NVIDIA GeForce RTX 3060 12GB cuz you can offload layers to system RAM if you run out of VRAM.

Ok so, I TOTALLY agree that HQQ or EXL2 is the move if you want to avoid that lobotomized feeling with lower bits. Looking at the market right now from a research perspective, it’s basically a race between a few major optimization ecosystems to see who can squeeze the most logic into tiny buffers. If ur trying to stay on the bleeding edge without losing ur mind, you should look at these general directions: - Go with anything from the **Unsloth** ecosystem; they are basically the gold standard for memory efficiency right now.
- Stick to the **AutoGPTQ** brand of quants if you want something that’s super stable across different backends.
- Just get any of the **community-optimized** versions from the big names on Hugging Face. Honestly, the market is shifting so fast that the "brand" of the optimizer matters almost as much as the format. Tbh, as long as you stay away from the generic stock implementations and stick with the specialized optimization groups, you’ll get much better results on a mid-range card. It’s all about that VRAM-to-logic ratio!