Which GPU is best for local LLM inference?

Question

I’m trying to pick a GPU specifically for running local LLM inference (not training) and I’m getting a bit lost in the specs and brand debates. My main goal is to run quantized models smoothly on my own machine for coding help and general chat, ideally without constant VRAM errors or super slow token generation.

Right now I’m torn between prioritizing VRAM capacity vs raw speed. For example, I keep seeing people recommend 12GB cards because they’re cheaper, but others say 16GB+ is basically mandatory if you want to run anything bigger than 7B/8B comfortably (even with 4-bit). I’m also confused about how much CUDA matters here versus newer features like tensor cores, and whether AMD is realistically viable for local inference in 2026 without fighting driver/tooling issues.

Constraints: I’d like to stay under about $800 if possible, I’m fine buying used, and I care more about stable performance than chasing the absolute max benchmark.

Given those priorities, which specific GPU models are the best value right now for local LLM inference, and how should I weigh VRAM size vs GPU generation/features when choosing?

ektihwohxq · Accepted Answer

In my experience, for local LLM inference VRAM wins first, speed second… i feel u. I went from a faster 12GB setup to a slower 16GB-ish one and the VRAM errors basically vanished.
- Option A (more VRAM): fewer OOMs, smoother 13B-ish 4-bit, better context; downside: raw tok/s maybe lower
- Option B (faster/less VRAM): great 7B/8B, but you’ll hit walls on context/model size and start juggling offload
- CUDA vs AMD: honestly CUDA tooling is still the “it just works” path; AMD can run, but i kept burning time on weird edge stuff
So yeah: prioritize VRAM + stability, then newer gen/tensor stuff for speed. good luck

iqtdznwwkp · Answer

Ok so quick question before I steer you wrong: what’s the biggest model + context you actually wanna run most days (like 7B/8B vs 13B+, and 8k vs 32k tokens)? And are you on Windows or Linux?

In general though, under $800 for inference, I’d go NVIDIA if you want the least headache. CUDA stack + tooling is still the “it just works” path, and VRAM is the first hard wall you hit. I’d treat 12GB as “mostly 7B land” (fine for coding helpers, shorter context), and 16GB+ as the point where life gets way less babysitting. Tensor cores help, sure, but if you’re swapping to RAM you’re cooked anyway. AMD can be viable, but driver/tooling variance is still a thing… depends how much you enjoy tinkering lol

gowuorqezt · Answer

In my experience, for local LLM inference VRAM wins first, speed second… i feel u. I went from a faster 12GB setup to a slower 16GB-ish one and the VRAM errors basically vanished.
- Option A (more VRAM): fewer OOMs, smoother 13B-ish 4-bit, better context; downside: raw tok/s maybe lower
- Option B (faster/less VRAM): great 7B/8B, but you’ll hit walls on context/model size and start juggling offload
- CUDA vs AMD: honestly CUDA tooling is still the “it just works” path; AMD can run, but i kept burning time on weird edge stuff
So yeah: prioritize VRAM + stability, then newer gen/tensor stuff for speed. good luck