Knowledge catalogue
TL;DR: I run a mismatched Tesla V100-PCIE pair—one 16 GB card and one 32 GB card, 48 GB total—in a Proxmox/LXC-based local-inference lab. The practical winner so far is a recent CUDA build of llama.cp
Da un po mi sono ritrovato in garage un hp z840 da 64gb ddr5 montate (ora) e 2 processori con una gpu un po scarsina una nvidia quadro k680 (da aggiornare) avrei bisogno di un consiglio: quale modello
I've got an refurb Dell R740 running Proxmox that I put a Tesla T4 in, mainly to run some CTC local transcription work, but thought it would be fun to try DS4 when it came out, and it was appalling at
This is nothing impressive but, i had so much fun i wanted to share my experience with this model. (yes this post is written by human) I made an FPS with local Q4_K_XL 3.8 Flash Next (256k context) (i
The NVIDIA Tesla K80 is a legacy GPU that can run modern LLMs through Ollama with community-maintained patches, since official Ollama dropped support for CUDA Compute Capability 3.7 hardware. Each K80
Hey guys! I have been excited to share this here. This is a project consisting of kernel optimizations for the Tesla p100 series graphics card ($80). I want to start by saying I am 17 years old and do
Early this year when I was first looking at building up my inference capability you could get the 16GB Tesla P100s for between 60 and 80. Asked claude about it, told me absolutely not worth it. No ten
Looking for V100 users to share your config and it's performance. GPU: Tesla V100 PCIE 32Gb Qwen3.6 27B Q4_K_M + Q8_0 MTP 128K context length Pi coding agent llama.cpp model preset: [*] spec-default =
Hi guys! I got a problem with one of my V100 SXM2 Modules. In 'nvidia-smi' its shows with 'Pwr:Usage/Cap' as 'N/A / N/A'. When I try to use it i get the following error in dmesg: NVRM: Xid (PCI:0000:0
tl;dr: running Qwen3.8FN Q4 locally is cheaper for me than every cloud model except Luna and the newer Haiku at mid-peak and peak electricity pricing where I live, and becomes the absolute cheapest mo
This table to help anyone looking to build a budget Data Center homelab. I copied the bulk of value based, mid level, decent speed results GPUs and feed it to AI or SI and here are the recommended res
This post was made with AI. I tried to remove as much slop as possible and keep it straight to the point to save your time as I know how annoying AI slop posts can be, but I still wanted to retain all
After seeing the benchmark results for the full release of DS v4 Flash 0731, I replaced my 2 x 16GB DDR4 ram sticks with 2 x 32GB DDR4 ram sticks to get a max supported of 128 GB RAM, in hope to be ab
you read about gpu's like the Tesla P40, V100, AMD M150 etc that people pick up for cheap. probably many others as well. Most are either server gpu's being phased out or mining discards, right? The pr
Years ago I built a server, shelved it after some time, but recently realized I have it sitting there. I'm looking to pop in a Tesla V100 32GB card (with proper cooling). But are the component too old
good morning, has anyone already used qwen 3.8 with the tesla v100 or sxm2 or pcie? any information is useful to me context token/s thanks for the help. submitted by /u/Full-Chicken2110 [link] [commen
Four Tesla V100s from 2017 matched my RTX 5090 on single-request Qwen 3.8 decode. Repo: https://github.com/dnv2003/v100-skinny https://i.redd.it/5ws2ak3uqckh1.gif The 5090 was not being held back. It
Press My earlier prediction that Tesla would buy them completely missed the mark. With AMD focusing heavily on the enterprise side, the idea of consumer-facing hot-swappable AI model chips looks prett
Help with this list. Give me your recommendation on 'not supported anymore' GPUs. Looking for budget and Vulkan friendly options. Most of the GPU are not supported by latest CUDA / ROCm. Often with so
I forked Strata and worked with Opus 5.5 with some heavy changes to it to make it work on an IBM AC922 I have access to. The IBM AC922 is a 2018 era beast with two POWER9 20 core SMT4 CPUs that are co
I can't stand kv cache quantization. Even at q8_0, I can feel the difference. But realistically, when running Qwen3.8-27B-UD-Q4_K_XL on my 32 GB GPU, I only have room for ~170k tokens (with mtp and mm
I've been tinkering with local LLMs since the beginning of the year when I had an Intel Arc B580 and 32 GB of DDR5. Curiosity got the best of me and I bought the first R9700 about half a year ago, als
TL;DR: switching KV cache to f16 may give a boost in speed if using MTP and ngrams. I have a self-built 'AI mega-cluster' with 2x P40s on a cheap Chinese motherboard and a Xeon CPU (around $1,100 to b
The latest updates in Ollama have dropped support for the Pascal line of NVIDIA cards. I have a laptop with an NVIDIA 1070 which is no longer used by Ollama. Does anyone know of other frameworks like
Hi, Back in late march I begun playing around with Qwen 3.6 27b and found like everyone else that it's notoriously good at tool calls, where every model I tried before just derailed after a few turns
I was fascinated by Nanbeige's outstanding performance for its size, so I started digging into how much a model can improve its own representation just by looping over itself (for fun). My prototype w
I've made a few posts recently, just talking about the hardware I've got and the model I use. Pair of P100s, Unsloth Qwen3.6 35b A3B UD Q4 K XL. There's been a ton of talk of model and hardware specif
Referring to specifically Unsloth's UD_Q4_K_XL quant because that's going to be a question, and is relevant regardless. It's old now. It's not great at coding medium sized or even small-ish projects.
Hey Reddit. This is a post asking for advice / user experience. The goal is simple: deploy a small private server for a developer to run a harness that rivals/beats Claude Opus (in perf/intelligence,
Hi all! I finally just got around to testing out Qwen 3.8 27b. I'm using Unsloth's UD-Q8_K_XL quant as a sit-in replacement to Qwen 3.6 27b, same quant size. Wow -- this thing isn't messing around. I
I don't know what's happening. So, when i use models in openwebui everything works, ollama uses the GPU fully, everything's perfect. Then, I use Hermes, and loads the model in VRAM (fully, no offload
Llama.cpp currently uses cpu based sampling for user with mtp enabled. The PR moves sampling to the gpu, which on a 5090 boasts an 8% increase in tok/s for qwen3.6:35b. I tested it on my P40 and obser