Which quantization method is best for DeepSeek performance?

Question

So I finally got a chance to play around with the new DeepSeek models this weekend and honestly Im pretty hyped. Ive been running local LLMs for a couple years now, mostly sticking to Llama derivatives, but the performance on these new ones is just wild for the size. Right now Im trying to squeeze them onto my home server setup which has 2x 3090s, so Im limited to 48GB of VRAM total. I need to keep the context window pretty large for my RAG pipeline because Im indexing about 200 PDFs for a work project due next week, and honestly the memory overhead is killing me.

I started off with some standard GGUF quants at Q4_K_M just to test the waters, but the output feels a bit inconsistent compared to the full precision runs I saw in the benchmarks. Its kinda frustrating because I know I have the compute to push a bit higher, maybe Q6 or even IQ4_XS if the perplexity holds up, but I really dont want to spend the next three days re-running evals if there is already a community consensus on what hits the sweet spot for these architectures. Is everyone sticking to EXL2 for faster inference or is GGUF still the way to go for the best output quality on these specific weights? I really dont wanna lose that reasoning capability just to save a few GB of memory if I can avoid it. Also, has anybody noticed specific issues with K-quants vs I-quants on the smaller DeepSeek variants, or is it basically negligible at this point for general coding tasks? Honestly just trying to figure out if I should just bite the bullet and go for the higher bit-rate or if the gains are diminishing so fast that it isnt even worth the extra load time...

rmrhglntzh · Accepted Answer

Honestly, I switched to Turboderp EXL2 for my dual NVIDIA GeForce RTX 3090 24GB setup and the speed increase is insane. It keeps those reasoning capabilities sharp for RAG, seriously.

VincentTrill · Answer

> Is everyone sticking to EXL2 for faster inference or is GGUF still the way to go for the best output quality Honestly, if you have dual NVIDIA GeForce RTX 3090 24GB cards, you should absolutely be using EXL2. Over the years, I've found GGUF is great for portability, but for raw speed and keeping that large context window for your RAG, EXL2 is just better on Ampere hardware. I've been running the 4.65bpw or 5.0bpw EXL2 quants on my own rig and the reasoning holds up way better than the Q4_K_M GGUFs. GGUF feels sluggish when you start pushing the context limit. Just stick to EXL2 and you'll get way more throughput without sacrificing the quality you're worried about. Honestly, just skip the IQ4 stuff and go for the highest bitrate EXL2 that fits in your 48GB. It's the sweet spot.

spb-generic.ru · Answer

Late to the party but definitely agree with the others on the EXL2 front. I spent all of last night re-indexing my own local knowledge base and the difference in throughput between that and my old GGUF setup is night and day. Honestly, when you are pushing 200 PDFs, the bottleneck usually shifts to context management pretty fast anyway. In my experience, sticking to the higher bit-rates like 6.0 or 8.0 is totally worth it if you have the space. I used to obsess over the perplexity scores of the 4-bit quants, but once I actually started using the model for real work, the reasoning drop-off at lower bits became super obvious during complex RAG tasks. It just started hallucinating bits of the source material or skipping steps in the logic chain. If you have those dual cards, dont bother trying to squeeze every last byte. Just find a high bit-rate that fits your context comfortably and stick with it. I found that I get better results by slightly lowering the quantization precision than I do by trying to force a massive context window into a model that cant actually handle the complexity anymore. It is way less annoying to have slightly faster inference than to spend your whole weekend debugging why the model is outputting nonsense. Just keep it simple and focus on that context space.