For your situation, ngl the 8GB on that NVIDIA GeForce RTX 3070 8GB is a struggle. I tried running DeepSeek-Coder-33B-Instruct at Q4_K_M but it was basically a slide show... super disappointing. Honestly, you're better off with the DeepSeek-Coder-6.7B-Instruct at Q6_K or even Q8_0. It stays snappy and the logic is actually solid for Python. Cramming a 33B into Q2_K just makes it halluinate like crazy, so it's kinda useless for coding imo. Stick to the 7B models!

For your situation, I've spent years messing with VRAM limits and honestly, 8GB is that awkward middle ground where you gotta be smart about it. Basically, quantization isn't just about shrinking the file; it's about how the model 'remembers' weights. When you drop to Q2 or Q3, you lose the 'coherence' because the math gets too fuzzy.

On an NVIDIA GeForce RTX 3070 8GB, here's what I suggest for the best balance:

- Stick to the DeepSeek-Coder-6.7B-Instruct at Q5_K_M or Q6_K. It's way more reliable for Python than a heavily lobotomized 33B.
- If you really wanna try the bigger models, look for the DeepSeek-V2-Lite-Chat GGUF versions.
- Make sure to use 'Flash Attention' in LM Studio to save a bit of memory.

I mean, a Q4_K_M on a 7B model is usually the 'gold standard' for speed, but for coding, that extra bit of precision in Q6 actually helps with syntax. Q8 is literally overkill for 8GB and just chokes your OS. gl!

sooo I went through this last year... honestly, its such a struggle trying to balance that 8GB VRAM limit. iirc i spent way too much time trying to squeeze everything out of my rig. I eventually just stuck with the GGUF format for everything cuz its basically the only way to stay sane without crashing. I mean, going with any of the quantized versions from the big names on HuggingFace is usually the play. tbh just stick with GGUF files and youll be fine. gl!

Ngl, I totally agree that the 6.7B at a higher quant is the way to go. > Stick to the DeepSeek-Coder-6.7B-Instruct at Q5_K_M or Q6_K. From what I've seen looking at the current LLM market, DeepSeek-Coder-6.7B is definately the efficiency king for us 8GB users. If you compare it to something like Meta Llama 3 8B or Mistral-7B-v0.3, DeepSeek realy punches above its weight in Python logic specifically. The market is basically split right now between "big but dumb" (heavily quantized 30B+ models) and "small but sharp" (high-quant 7B models). The reason the 33B fails at Q2 is basically because the "perplexity" shoots through the roof—basically, the model gets confused about which word comes next because the weights are too fuzzy. Iirc, the "sweet spot" for reasoning vs VRAM is almost always a Q5 or Q6 on a 7B model rather than a lobotomized Q2 on a 33B. It’s realy about that logic-to-size ratio. I’ve found that for coding, DeepSeek models are just better optimized for these mid-range Nvidia cards than some of the other brands out there. Anyway, hope that helps clarify the why!

I went through this exact thing a few days ago. I really wanted the smarter logic of the bigger models for my scripts, so I tried a very low Q3 quant of a 33B model. It was a bit of a nightmare tbh. It looked like it was working, but it kept introducing these tiny, annoying bugs in my code that were hard to catch. It felt kinda unsafe to rely on it for anything important because I was spending more time debugging the AI than actually writing code. Eventually i just decided to play it safe and stick with the 7B model at a higher Q5 or Q6 bit rate. My current setup runs it smoothly and the logic feels way more solid and predictable. I learned the hard way that a smaller, healthy model is much better than a big one that has been squeezed too much. Its just not worth the risk of it hallucinating something that breaks your whole project.

Honestly, working within that 8GB limit is basically a puzzle, but it’s doable if you take the DIY route instead of relying on default loaders. I spent way too much time manually offloading layers to find the exact breaking point on my current setup. One thing I learned the hard way is that it’s not just about the model weight—it’s the KV cache (the context window) that silently kills ur VRAM, especially during long Python debugging sessions. A few things that helped me tune the performance:
- Look for VRAM calculators or community spreadsheets that breakdown memory usage per layer. It helps you see exactly how much room you have left for context.
- Experiment with context length. Limiting it to 4k or 8k can sometimes free up enough VRAM to let you run a higher quantization level like Q5 or Q6.
- Check perplexity benchmarks for the specific quants you're eyeing. Sometimes the logic gap between Q4_K_M and Q5 isn't as big as the jump from Q3 to Q4. I’ve pretty much realized that if you tune the context and layers yourself, you can get way more coherence out of the 7B models without ur system lagging. It's all about that manual balance.