Koboldcpp multi gpu reddit. py --useclblast 0 0 *** Welcome to KoboldCpp - Version 1.

Koboldcpp multi gpu reddit That is because AMD has no ROCm support for your GPU This is currently not possible for two reasons. The AI will also create multiple "characters" and just talk to itself, not leaving me a spot to interact. Also, the RTX 3060 12gb should be mentioned as a budget option. (newer motherboard with old GPU or newer GPU with older board) Your PCI-e speed on the motherboard won't affect koboldAI run speed. 1]$ python3 koboldcpp. Top. My PC specs are: FreeCAD on Reddit: a community dedicated to the open-source, extensible & scriptable parametric 3D CAD/CAM/FEM modeler. It’s disappointing that few self hosted third party tools utilize its API. in the end CUDA is built over specific GPU capabilities and if a model is fully loaded into RAM there is simply nothing to do for CUDA. Don't you have Koboldcpp that can run really good models without needing a good GPU, why didn't you talk about that? Yes! Koboldcpp is an amazing solution that lets people run GGML models and it allows you to run those great models we have been enjoying for our own chatbots without having to rely on expensive hardware as long as you have a bit With koboldcpp, you can use clblast and essentially use the vram on your amd gpu. on a 6800 XT. cpp (a lightweight and fast solution to running 4bit quantized llama models locally). However, that PCI-e is backwards compatible both ways. The infographic could use details on multi-GPU arrangements. Each will calculate in series. But it kinda suck at writing a novel. To run a model fast, you need to have all of its layers inside the GPU, or it will be sloooooow. Each GPU does its own calculations. But when running BLAS, I could see only half of the threads are busy in task manager, the overall CPU utilization was around 63% at most. Or check it out in the app stores Can you stack multiple P 40s if I don't the card never downclocks to 139mhz. I notice watching the console output that the setup processes the prompt * EDIT: [CuBlas]* just fine, very fast and the GPU does it's job correctly. Can't help you with implementation details of koboldcpp, sorry. true. The bigger the model, the more 'intelligent' it will seem. I try to leave a bit of headroom but Some things support OpenCL, SYCL, Vulkan for inference access but not always CPU + GPU + multi-GPU support all together which would be the nicest case when trying to run large models with limited HW systems or obviously if you do by 2+ GPUs for one inference box. 12 votes, 12 comments. In Task Manager I see that most of GPU's VRAM is occupied, and GPU utilization is 40-60%. i set the following settings in my koboldcpp config: CLBlast with 4 layers offloaded to iGPU 9 Threads 9 BLAS Threads 1024 BLAS batch size High Priority Use mlock Disable mmap Whether the difference is due to the processor, OS handling of multi-thread processing, AMD/NVIDIA GPU efficiency, or something else, is hard to say. I. 1 For command line arguments, please refer to --help *** Warning: CLBlast library file not found. cpp. cpp or koboldcpp. The only reason to offload is because your GPU does not have enough memory to load the LLM (a llama-65b 4-bit quant will require ~40GB for example), but the more layers you are able to run on GPU, the faster it will run. OpenCL is not detecting my GPU on koboldcpp . Click on the blue "BROWSE" button. With just 8GB VRAM GPU, you can run both a 7B q4 GGUF (lowvram) alongside any SD1. There must be enough space for KV cache, and cuda buffers. As for whether to buy what system keep in mind the product release cycle. I'd probably be getting more tokens per second if I weren't bottlenecked by the PCIe slot so Small pro tip, the latest version of Koboldcpp has a different binary mode in Linux with LLAMA_PORTABLE=1 that one will compile it for every arch instead of the GPU it can detect. More info Classic Koboldcpp mistake, you are offloading the amount of layers the models has, not the 3 additional layers that indicate you want to run it exclusively on your GPU. What happens is one half of the 'layers' is on GPU 0, and the other half is on GPU 1. And of course Koboldcpp is open source, and has a useful API as well as OpenAI Emulation. So OP might be able to try that. Use llama. I find the tensor parallel performance of Aphrodite is amazing and definitely worthy trying for everyone with multiple GPUs. In that case, you could be looking at around 45 seconds for a response of 100 tokens. @echo off echo Enter the number of GPU layers to offload set /p layers= echo Running koboldcpp. Sort by: Best. The most excellent JohannesGaessler GPU additions have been officially merged into ggerganov's game changing llama. In this case, it was always with 9-10 layers, but that's made to fit the context as well. I don't want to split the LLM across multiple I have 2 different nvidia gpus installed, Koboldcpp recognizes them both and utilize vram on both cards but will only use the second weaker gpu. Well I don't know if I can post the link here, more after my disappointment when using the normal version of koboltAI (due to excessive GPU spending leaving me stuck with "weak" models). Koboldcpp is a great choice, but it will be a bit longer before we are optimal for your system (Just like the other solutions out there). Open comment sort options. exe" file, and then run the batch file. It Zero install, portable, lightweight and hassle free image generation directly from KoboldCpp, without installing multi-GBs worth of ComfyUi, A1111, Fooocus or others. At no point at time the graph should show anything. Or check it out in the app stores Koboldcpp works fine with ggml GPU-offloading with parameters:--useclblast 0 0 --gpulayers 14 (more in your case)The speed is ~2 t/s for 30B on 3060ti Exllama has fastest multi-GPU inference, as far as I’m aware: https://github. 6, I am trying to run the Nerys FSD 2. When setting up the model I have tried setting the layers multiple ways and 0/32, all set on GPU seems to work fastest. GGUF file of your chosen model To determine if you have too many layers on Win 11, use Task Manager (Ctrl+Alt+Esc). A 20B model on a 6GB GPU you could be waiting a couple of minutes for a response. With the model loaded and at 4k, look at how much Dedicated GPU memory is used and Shared GPU memory is used. Very little data goes in or out of the gpu after a model is loaded (just your text and the AI output token rankings, which is measured in megabytes). Right now this is my KoboldCPP launch instructions. it shows gpu memory used. And huggingface now has the Open LLM leaderboard which does multiple tests. if you're interested in mass serving. /r/StableDiffusion is back open after the protest of Reddit killing Koboldcpp is better suited for him than LM Studio, performance will be the same or better if configured properly. KoboldCPP is the best that I have seen at running this model type. Ooba, ExUi, KoboldCPP. It looks like the heterogeneous backend (any combination of OS, GPU architecture, GPU model, GPU capability, one or many networked machines) RPC llama. SillyTavern should be obvious, but it is updated multiple times a day and is amazingly feature rich and modular. Also if you have Psychovisual tuning, lookahead and/or multipass turned on, that uses both 3D and CUDA resources too. Has in-flight batching of requests. will already run you thousands of dollars, so saving a couple hundred bucks off that, but getting a GPU that's much inferior for LLM didn't seem worth it. 5 image model at the same time, as a single instance, fully offloaded. ) using Vulkan. since I couldn't go back to any other frontend. If you're on Windows, I'd try this: right click taskbar and open task manager. exe --useclblast 0 0 --gpulayers %layers% --stream --smartcontext pause --nul. If I run KoboldCPP on a multi-GPU system, can I specify which GPU to use? Ordered a refurbished 3090 as a dedicated GPU for AI. Don't fill the gpu completely because inference will run out of memory. Only 30XX series has NVlink, that apparently image generation can't use multiple GPUs, text-generation supposedly allows 2 GPUs to be used simultaneously, whether you can mix and match Nvidia/AMD, and so on. . ExLlama and exllamav2 are inference engines. I can get ~600-1000 tokens/sec throughput with enough batches running to saturate using a 13b llama2 model and 2x 3090s. But I don't see such a big improvement, I've used plain CPU llama (got a 13700k), and now using koboldcpp + clblast, 50 gpu layers, it generates about 0. Even though they seemed to belong in the same Enjoy zero install, portable, lightweight and hassle free image generation directly from KoboldCpp, without installing multi-GBs worth of ComfyUi, A1111, Fooocus or others. On Faraday, it operates efficiently without fully utilizing the hardware's power yet still responds to my chat very quickly. The first one does its layers, then transfers the intermediate result to the next one, which continues the calculations. Well, exllama is 2X faster than llama. So I hope koboldcpp gets the GPU acceleration soon or I'll have It is koboldcpp compiled with the cublas flag. usually is 33 for a 7b-8b model. The high GPU usage could be due to the GPU downclocking when doing general desktop tasks. On my laptop with just 8 GB VRAM, I still got 40 % faster inference speeds by offloading some model layers on the GPU, which makes chatting with the AI so much more enjoyable. It's good news that NVLink is not required, because I can't find much online about using Tesla P40's with NVLink connectors. com with the ZFS community as well. And GPU+CPU will always be slower than GPU-only. Also, although exllamav2 is the fastest for single gpu or 2, Aphrodite is the fastest for multiple gpus. View community ranking In the Top 10% of largest communities on Reddit. Note: You can 'split' the model over multiple GPUs. All these programs have decent enough multi-gpu support. Works with awq quantization. I usually leave 1-2gb free to be on the To determine if you have too many layers on Win 11, use Task Manager (Ctrl+Alt+Esc). com (koboldcpp rocm) I tried to generate a reply but the character writes gibberish or just yappin. it's not too bad. With just 8GB VRAM GPU, you can run both a 7B q4 GGUF There's a new, special version of koboldcpp that supports GPU acceleration on NVIDIA GPUs. I have a ryzen 5 5600x and a rx 6750xt , I assign 6 threads and offload 15 layers to the gpu . New /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers KoboldCpp can only run quantized GGUF (or the older GGML) models. I tried changing NUMA Group Size Optimization from "clustered" to "Flat", the behavior of KoboldCPP didn't change. None of the backends that support multiple GPU vendors such as CLBlast also support multiple GPU's at once. When I run the model on Faraday, my GPU doesn't reach its maximum usage, unlike when I run it on Koboldcpp and manually set the maximum GPU layer. Now, I've expanded it to support more models and formats. The GameCube (Japanese: ゲームキューブ Hepburn: Gēmukyūbu?, officially called the Nintendo GameCube, abbreviated NGC in Japan and GCN in Europe and North America) is a home video game console released by Nintendo in Japan on September 14, 2001; in North America on November 18, 2001; in Europe on May 3, 2002; and in Australia on May 17, 2002. I found a possible solution called koboldcpp but I would like to ask: Have any of you used it? It is good? Can I use more robust models with it?. /r/StableDiffusion is back open after the protest of Reddit Works well for single or multi-gpu inference. Yesterday I even got Mixtral 8x7b Q2_K_M to run on such a machine. They are equivalent to llama. Or check it out in the app stores   KoboldCPP ROCM is your friend here. Renamed to KoboldCpp. /r/StableDiffusion is back open after the protest of Reddit My question is, I was wondering if there's any way to make the integrated gpu on the 7950x3d useful in any capacity in koboldcpp with my current setup? I mean everything works fine and fast, but you know, I'm always seeking for that little extra in performance where I can if possible (text generation is nice, but image gen could always go faster). As far as Sillytavern, what is the preferred meta for 'Text completion presets?' Use a Q3 GGUF quant and offload all layers to GPU for good speed or use higher quants and offload less layers for slower responses but better quality. Its not overly complex though, you just need to run the convert-hf-to-gguf. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation, and exclude blind users from the site. Multiple GPU settings using KoboldCPP upvotes This subreddit has gone Restricted and reference-only as part of a mass protest against Reddit's recent API changes, which break third-party apps and moderation tools. 5. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will Koboldcpp is so straightforward and easy to use, plus it’s often the only way to run LLMs on some machines. And the one backend that I've just started using KoboldCPP and it's amazing. So second part of the question, it is correct that in CPU bound configurations the prompt processing takes longer than the generations, this is a helpful i'm running a 13B q5_k_m model on a laptop with a Ryzen 7 5700u and 16GB of RAM (no dedicated GPU), and I wanted to ask how I can maximize my performance. Its at the high context where Koboldcpp should easily win due to its superior handling of context shifting. ExLlama supports 4bpw GPTQ models, exllamav2 adds support for exl2 which can be quantised to fractional bits per weight. 0 with a fairly old Motherboard and CPU (Ryzen 5 2600) at this point and I'm getting around 1 to 2 tokens per second with 7B and 13B parameter models using Koboldcpp. py in the Koboldcpp repo (With huggingface installed) to get the 16-bit GGUF and then run the quantizer tool on it to get the quant you want (Can be compiled with make tools on Koboldcpp). Can someone say to me how I can make the koboldcpp to use the GPU? thank you so much! also here is the log if this can help: [dark@LinuxPC koboldcpp-1. practicalzfs. Using multiple GPUs works by spreading the neural network layers across the GPUs. Low VRAM option enabled, offloading 27 layers to GPU, batch size 256, smart context off. Great card for gaming. If anyone has any additional recomendations for SillyTavern settings to change let me know but I'm assuming I should probably ask over on their subreddit instead of here. So on linux its a handful of commands and you have your own manual conversion. koboldcpp Does Koboldcpp use multiple GPU? If so, with the latest version that uses OpenCL, could I use an AMD 6700 12GB and an Intel 770 16GB to have 28GB of Set the GPU type to "all" and then select the ratio with --tensor_split. OBS still needs to compile the video before sending it to the encoder, and all of that is done on the GPU. But with GGML, that would be 33B. The following is the command I run. I see in the wiki it says this: How do I use multiple GPUs? Multi-GPU is only available when using CuBLAS. 8 T/s with a context size of 3072. Do not use main KoboldAi, it's too much of a hassle to use with Radeon. I tried to make a new instalation of the koboldcpp on my Arch Linux but for some reason when I try to run the AI it shows to me a strange error: Battlefield 4's technical director about possible use of Mantle API: "low More or less, yes. It runs pretty fast with ROCM. Just make a batch file, place it in the same folder as your "koboldcpp. KoboldCPP uses GGML files, it runs on your CPU using RAM -- much slower, but getting enough RAM is much cheaper than getting enough VRAM to hold big models. I have a 4070 and i5 13600. Matrix multiplications, which take up most of the runtime are split across all available GPUs by default. So I recently decided to hop on the home-grown local LLM setup, and managed to get ST and koboldcpp running a few days back. I use 32 GPU layers. Assuming you didn't download multiple versions of the same model or something. Also, regarding ROPE: how do you calculate what settings should go with a model, based on the Load_internal values seen in KoboldCPP's terminal? Also, what setting would x1 rope be? using multiple GPUs? I recently bought an RTX 3070. The bigger/faster the GPU VRAM you have is, the faster the same model will generate a response. For system ram, you can use some sort of process viewer, like top or the windows system monitor. A regular windows search window will appear, from here find and select the . Laptop specs: GPU : RTX 3060 6GB RAM: 32 GB CPU: i7-11800H I am currently using Mistral 7B Q5_K_M, and it is working good for both short NSFW and RPG plays. Both GPTQ and exl2 are GPU only formats meaning inference cannot be split with the CPU and the model must fit entirely in VRAM. Because its powerful UI as well as API's, (opt in) multi user queuing and its AGPLv3 license this makes Koboldcpp an interesting choice for a local or remote AI server. You can offload some of the work from the CPU to the GPU with KoboldCPP, which will speed things up, but still is quite a bit slower that just using the graphics card. e. py and have that launcher GUI. This is self contained distributable powered by We would like to show you a description here but the site won’t allow us. When not selecting a specific GPU ID after --usecublas (or selecting "All" in the GUI), weights will be distributed across all detected Nvidia GPUs automatically. But it's fair to say that a lot more goes into determining your model output speed than just Get the Reddit app Scan this QR code to download the app now. They need to catch up though, there's Is Multi GPU possible via Vulkan in Kobold? I am quite new here and don't understand how all of this work, so I hope you will. (GPU: rx 7800 xt CPU: Ryzen 5 7600 6 core) Share Add a Comment. bin. I’d love to be able to use koboldccp as the back end for multiple applications a la OpenAI. SLI depends on GPU support and the 3070 does not support it. q8_0. However, the launcher for KoboldCPP and the Kobold United client should have an obvious HELP button to bring the user to this resource. exe with %layers% GPU layers koboldcpp. It Get the Reddit app Scan this QR code to download the app now. Which is fine for a 4GB gpu, windows 10 desktop is heavy enough to need that. Open the performance tab -> GPU and look at the graph at the very bottom, called "Shared GPU memory usage". If the software you're using can use multiple GPUs then you could get another 3070 and put it in an x16 slot, sure. Assuming you have an nvidia gpu, you can observe memory use after load completes using the nvidia-smi tool. With a 13b model fully loaded onto the GPU and context ingestion via HIPBLAS, I get typical output inference/generation speeds of around 25ms per token (hypothetical 40T/S). Get the Reddit app Scan this QR code to download the app now Riddle/Reasoning GGML model tests update + Koboldcpp 1. Slow though at 2t/sec. Overall, if model can fit in single gpu=exllamav2, if model fits on multiple gpus=batching library(tgi, vllm, aphrodite) Edit: multiple users=(batching library as well). So forth. You'll need to split the computation between CPU and GPU, and that's an option with GGML. Posted by u/amdgptq - 29 votes and 7 comments This sort of thing is important. /r/StableDiffusion is back open after the protest of Reddit I went with a 3090 over 4080 Super because the price difference was not very big, considering it gets you +50% VRAM. When I was shopping around for GPUs I couldn't find anything from Team Red or Team Green that was reasonably priced while having a lot of VRAM, but Intel ARC seems to be just that. A 13b q4 should fit entirely on gpu with up to 12k context (can set layers to any arbitrary high number) you don’t want to split a model between gpu and cpu if it comfortably fits on gpu alone. 8tokens/s for a 33B-guanaco. With just 8GB VRAM GPU, you can run both a 7B q4 Multi-GPU is only available when using CuBLAS. Enjoy zero install, portable, lightweight and hassle free image generation directly from KoboldCpp, without installing multi-GBs worth of ComfyUi, A1111, Fooocus or others. It kicks-in for prompt-generation too. A n 8x7b like mixtral won’t even fit at q4_km at 2k context on a 24gb gpu so you’d have to split that one, and depending on the model that might Seems to be a koboldcpp specific implementation but, logically speaking, CUDA is not supposed to be used if layers are not loaded into VRAM. So once your system has customtkinter installed you can just launch koboldcpp. But not so much for a 24GB one, where reserving 5GB or so is pretty wasteful; the desktop doesn't actually need that much. Aphrodite-engine v0. 7B Hybrid adventure model. This is a good General KoboldCpp question for my Vega VII on Windows 11: Is 5% gpu usage normal? My video memory is full and it puts out like 2-3 tokens per seconds when using wizardLM-13B-Uncensored. 0 brings many new features, among them is GGUF support. you can do a partial/full off load to your GPU using openCL, I'm using an RX6600XT on PCIe 3. Loads into both the VRAM, but won't split Zero install, portable, lightweight and hassle free image generation directly from KoboldCpp, without installing multi-GBs worth of ComfyUi, A1111, Fooocus or others. With koboldcpp I can run this 30B model with 32 GB system RAM and a 3080 10 GB VRAM at an average around 0. Reply reply More replies More replies More replies However, lately I have been getting into AI and more specifically AI training. Backend: 99% of the time, KoboldCPP, 1% of the time (testing EXL2 etc) Ooba Front End: Silly Tavern Why: GGUF is my preferred model type, even with a 3090. Requirements for Aphrodite+TP: Linux (I am not sure if WSL for Windows works) Exactly 2, 4 or 8 GPUs that supports CUDA (so mostly NVIDIA) Some time back I created llamacpp-for-kobold, a lightweight program that combines KoboldAI (a full featured text writing client for autoregressive LLMs) with llama. You don't get any speed-up over one GPU, but you can run a bigger model. Pytorch appears to support a variety of strategiesfor spreading workload over multiple GPU's, which makes me think that there's likely no technical reason that inference wouldn't work over PCI-e 1x. ggmlv3. 23 beta is out with OpenCL GPU support! Other First of all, look at this crazy mofo: Koboldcpp 1. cpp (a lightweight and fast solution to running 4bit quantized llama I have added multi GPU support for llama. Works pretty well for me but my machine is at its limits. Having trouble getting that to work with RTX 4070 12GB and RTX 1050Ti 4GB. This also means you can use much larger model: with 12GB VRAM, 13B is a reasonable limit for GPTQ. using multiple GPUs? I recently bought an RTX 3070. A 13b 4bit model should be 7-9GB, and you should have no trouble at all running it entirely on a 4090. Had some headaches before trying to get multi gpu systems to work because of slightly different versions, a month or so apart from each other but one already deprecated/legacy, the other almost. , it's using GPU for analysis, but not for generating output. cpp even when both are GPU-only. 42. Click Performance tab, and select GPU on the left (scroll down, might be hidden at the bottom). With 7 layers offloaded to GPU. But you would probably get better results by getting a 40-series GPU instead. Zero install, portable, lightweight and hassle free image generation directly from KoboldCpp, without installing multi-GBs worth of ComfyUi, A1111, Fooocus or others. works great for SDXL koboldcpp is your friend. If you run the same layers, but increase context, you will bottleneck the GPU. When it comes to GPU layers and threads how many should I use? I have 12GB of VRAM so I've selected 16 layers and 32 threads with CLBlast (I'm using AMD so no cuda cores for me). For immediate help and problem solving, please join us at https://discourse. Best. cpp stuff I aforementioned has merged so I'll give it a try ASAP with a mix of older NV GPUs / A770s spread among a few local family systems and see what I get. But if you go the extra 9 yards to squeeze out a bit more performance, context length or quality (via installing rocm variants of things like vllm, exllama, or koboldcpp's rocm fork), you basically need to be a linux-proficient developer to figure everything out. Some time back I created llamacpp-for-kobold, a lightweight program that combines KoboldAI (a full featured text writing client for autoregressive LLMs) with llama. I have both streaming and recording set to NVIDIA Nvenc(tried all types), This happens when not minimized too but it takes alot less from my GPU - 3D( 20% or When I started KoboldCPP, it showed "35" in thread section. Of course, if you do want to use it for fictional purposes we have a powerful UI for writing, adventure games and chat with different UI modes suited to each use case including character card support. py --useclblast 0 0 *** Welcome to KoboldCpp - Version 1. Make a note of what your shared memory is at. 8GB of VRAM is a huge bottleneck for me, and I don't feel like renting a GPU. We focus on education, discussion, and sharing of Ill address a non related question first, the UI people are talking about below is customtkinter based. When not selecting a specific GPU ID after --usecublas (or selecting "All" in the GUI), weights will be distributed across all detected Nvidia KoboldCpp is an easy-to-use AI text-generation software for GGML and GGUF models, inspired by the original KoboldAI. When recording, streaming, or using the replay buffer, OBS Studio when minimized uses 70 - 100% of my GPU - 3D according to task manager instead of of using the Video Encode Nvenc. A beefy modern computer with high-end RAM, CPU, etc. Get the Reddit app Scan this QR code to download the app now. I heard it is possible to run two gpus of different brand (AMD+NVIDIA for ex. With that I tend to get up to 60 second responses but it also depends on what settings your using on the interface like token amount and context size . Or check it out in the app stores Exploring Local Multi-GPU Setup for AI: Harnessing AMD Radeon RX 580 8GB for Efficient AI Model I'm curious whether it's feasible to locally deploy LLAMA with the support of multiple GPUs? If yes how and any tips Share Add a Comment. I can run the whole thing in GPU layers, and leaves me 5 GB leftover. However, during the next step of token generation, while it isn't slow, the GPU use drops to zero. Anyway full 3d GPU usage is enabled here) koboldcpp CUBLas using only 15 layers (I asked why the chicken cross the road): AMD GPUs can now run stable diffusion Fooocus (I have added AMD GPU support) - a newer stable diffusion UI that 'Focus on prompting and generating'. I do have a few questions, though: 1) How can I speed up text generation? I'm using an Intel Honestly, I would recommend this with how good koboldcpp is. So while this model indeed has 60 layers, to also offload everything else You can have multiple models loaded at the same time with different koboldcpp instances and ports (depending on the size and available RAM) and switch between them mid-conversation to get different responses. Alpin of the Aphrodite team is working on getting parallelism working on his mass serving API. I mostly use koboldcpp. Now start generating. You can run Mistral 7B (or any variant) Q4_K_M with about 75% of layers offloaded to GPU, or you can run Q3_K_S with all layers offloaded to GPU. /How to offload a model onto The infographic could use details on multi-GPU arrangements. (New reddit? Click 3 dots at end of this message) Privated to protest Reddit's upcoming API changes. 23 beta. uqy syb cmftfz qdvq cmfyk lts iyw ymh bldm bwctsb