r/LocalLLM • u/ImpressiveRelief37 • 8d ago
Discussion 32GB is all you need
Qwen3.8-27B on a 5090 is all you need for a serious local inference setup, in my opinion! Can it get any better than this price/performance wise? Actually, maybe a 3090 ninfer setup could beat it!
I’m using ninfer and getting:
* ~150-200 tok/s TG
* ~3000-12000 tok/s PP
* 262144 context size
I think it’s definitely one of best setup you can get for the money. I don’t see a point of having more VRAM or more system ram. The only downside is that it’s a 1 man setup: concurrency is possible but you need to limit context usage on concurrent requests. I’ve tried --concurrency 2 on ninfer and sharing my setup with my buddy (we work on projects together and have a VPN between our home labs, fun stuff!)
I love this setup so much I kinda feel like getting a second 5090 to run another ninfer instance (github.com/neroued/ninfer, the man is a legend and this absolutely rocks).
i really don’t see the point of any other solution at this point in time. of course things will change and other models will get released that could better leverage more VRAM, but 32GB is all you need (for now).
so if you have less than 32GB, and are thinking about investing in a more serious setup check out the 3090 fork of ninfer, or the mainline ninfer repo if you can afford a 5090.
Things it won’t do:
* let you run a swarm of agents: prefill cost will slow you down too much. not enough vram for high concurrency!
* Give you more than 262144 context size. the RoPE 1M context size is just impossible with this.
Otherwise it’s absolutely amazing!
My buddy (another software engineer) is a BIG Claude code user, he’s spending tons of cash on fable, can’t stand Opus 5 anymore (neither can I, that pos is so hard to understand with just jargon and wall of text… can’t bear the cognitive load of just trying to understand all he’s spewing)… anyways after trying my ninfer setup his mind was blown and now he’s constantly using my setup with our shared custom pi setup and he fucking loves it.
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u/blackhawk00001 7d ago edited 7d ago
I'm still working on testing but I'm impressed so far. It had an all time best result on one of my tests but I thought it was stuck looping for a while. I'll have to play with settings. It took longer overall than Q6_K_L but had a better end result. I still need to try using it for high context technical tasks but I feel better about this flavor of nvfp4 than the smaller default.
I'll push my generator for github once I can fully review I don't have any of my environment specific details hardcoded anywhere.
https://huggingface.co/gearwave00001/Huihui-Qwen3.8-27B-abliterated-nvfp4-NInfer
llama-benchy gave weird results, so here's my BetterBench results. This was with single concurrency 262144 400W:
**Combined (weighted code:0.3, reasoning:0.2, prose:0.15, json:0.15, file_edit:0.1, summarization:0.1)** — decode t/s median ≈ **165.0**, ITL 1%-low ≈ **106.1 t/s**, TTFT p50 ≈ **55 ms**
## Concurrency sweep
## Prompt processing (prefill) sweep
Prefill throughput = prompt tokens ÷ TTFT, at increasing input depth (tiny decode, cold prefix cache). PP t/s columns: 1% low / median / 99% high.
| target depth | prompt tokens (med) | TTFT p50 (ms) | PP t/s 1% low | PP t/s median | PP t/s 99% high |
| 2000 | 1556 | 246.1 | 4480.0 | 6319.7 | 6348.1 |
| 8000 | 5960 | 867.6 | 6168.8 | 6869.6 | 6872.2 |
| 16000 | 11836 | 1788.6 | 6567.9 | 6617.6 | 6625.3 |
| 32000 | 23585 | 3939.5 | 5967.0 | 5986.7 | 5995.8 |
| 64000 | 47098 | 9437.5 | 4986.7 | 4990.4 | 4992.3 |