Kobold ai gpu reddit. and running on Linux, it seems.

Kobold ai gpu reddit I currently rent time on runpod with a 16vcore CPU, 58GB ram, and a 48GB A6000 for between $0. 7/31. I bought a HD to install Linux as a secondary OS just for that, but currently I've been using Faraday. 3GB. Or check it out in the app stores so instead of turning disk cache up turn the GPU slider down to fit it in ram. If you want to follow the progress, /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, GPU boots faster (2-3 minutes), but using TPU will take 45 minutes for a 13B model, HOWEVER, TPU models load the FULL 13B models, meaning that you're getting the quality that is otherwise lost in a quant. Or check it out in the app stores A nice clear tutorial for running Kobold AI with WizardLB-30B using an easy cloud gpu provider . This is a very helpful guide. I'd probably be getting more tokens per second if I weren't bottlenecked by the PCIe slot so 4-After the updates are finished, run the file play. 30/hr depending on the time of day. This bat needs a line saying"set COMMANDLINE_ARGS= --api" Set Stable diffusion to use whatever model I want. runpod. Each will calculate in series. It's not a waste really. Currently using m7-evil-7b-Q8 or SultrySilicon-7B-V1-Fix-Q4-K-S with virtualRealism_v12novae. Before even launching kobold/tavern you should be down to 0. If I were in your shoes, I'd consider the price difference of selling a Docker has access to the GPUs as I'm running a StableDiffusion container that utilizes the GPU with no issues. But with the GPU layers being used it should go from minutes to seconds if your GPU is good enough, just like the other transformers based solutions. As of a few hours ago, every time I try to load any model, it fails during the 'Load Tensors' phase. I later read a msg in my Command window saying my GPU ran out of space. I've tried both koboldcpp (CLBlast) and koboldcpp_rocm (hipBLAS (ROCm)). The only other option I have heard of for AMD GPU's is to get torch set up with AMD ROCM, however I have no experience with it, and I GPUs and TPUs are different types of parallel processors Colab offers where: GPUs have to be able to fit the entire AI model in VRAM and if you're lucky you'll get a GPU with 16gb VRAM, even 3 billion parameters models can be 6-9 gigabytes in size. Update: Turns out I'm a complete moron and by cutting and pasting my Kobold folder to a new hardrive instead of just biting the bullet and reinstalling, I must have messed stuff up. Thanks for the gold!) Running GPT-NeoX 20B model on RTX 3090 with 21 layers on GPU and 0 layers on Disk Cache but wondering if I should be using Disk Cache for faster generations Reddit is dying due to terrible leadership from CEO /u/spez. The model is also small enough to run completely on my VRAM, so I want to know how to do this. 5-Now we need to set Pygmalion AI up in KoboldAI. Looking for a Koboldcpp compatible LLM that will allow an image generator with 16 gb. articles on new photogrammetry software or techniques. Heres the setup: 4gb GTX 1650m (GPU) Intel core i5 9300H (Intel UHD Graphics 630) 64GB DDR4 Dual Channel Memory (2700mhz) The model I am using is just under 8gb, I noticed that when its processing context (koboldcpp output states "Processing Prompt [BLAS] (512/ xxxx tokens)") my cpu is capped at 100% but the integrated GPU doesn't seem to be doing These could include philosophical and social questions, art and design, technical papers, machine learning, where to find resources and tools, how to develop AI/ML projects, AI in business, how AI is affecting our lives, what the future may hold, and many other topics. 3-5 GB or so but after about 10 messages this increase starts to ramp up to about 1-2 GB sometimes, not all the time but just sometimes, but i watched it go from 2. If you're running a local AI model, you're going to need either a mid-grade GPU (I recommend at least 8GB VRAM) or a lot of RAM to run CPU inference. With minimum depth settings you need somewhat more than 2x your model size in VRAM (so 5. Click on the description for them, and it will take you to another tab. When you chose your model in the AI menu you can choose the distribution of layers between recognised GPUs, Shared GPU Memory: 1. 3 can run on 4GB which follows the 2. Or check it out in the app stores GPU access is given on a first-come first-serve basis, I open Kobold AI and I don't see Google Colab as a model, but number 8, Custom Neo, lists Horni. **So What is SillyTavern?** Tavern is a user interface you can install on your computer (and Android phones) that allows you to interact text generation AIs and chat/roleplay with characters you or the community create. 59 GiB reserved in total by PyTorch) I take it from the message this is a VRAM issue. But if the shared memory shows some memory is used, then your model is being split between VRAM and RAM and it can slow it down a lot. I didn't find a way to use both CPUs. . Next more layers does not always mean performance, originally if you had to many layers the software would crash but on newer Nvidia drivers you get a slow ram swap if you overload the layers. So for now you can enjoy the AI models at an ok speed even on Windows, soon you will hopefully be able to enjoy them at speeds similar to the nvidia users and users of the more expensive 6000 series where AMD does have driver support. Ordered a refurbished 3090 as a dedicated GPU for AI. sh . One small issue I have with is trying to figure out how to run "TehVenom/Pygmalion-7b-Merged-Safetensors". I'm mainly interested in Kobold AI, and maybe some Stable Diffusion on the side. Can draw around 4500 watts though, which may be too much for a normal home circuit. I've heard using layers on anything other than the GPU will slow it down, so I want to ensure I'm using as many layers on my GPU as possible. We have ways planned we are working towards to fit full context 6B on a GPU colab. 00 MB Load Model OK: True Embedded Kobold Lite loaded. cuda. Right after you click on a model to load, you get two sliders, the first controls how many layers do you want on VRAM (GPU), the second how many on hard-disk, the rest goes to RAM. First I think that I should tell you my specs. You want to make sure that your GPU is faster than the CPU, which in the cases of most dedicated GPU's it will be but in the case of an integrated GPU it may not be. If you want to follow /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt I don't have token generation turned up very high or gens per action above 1. (newer motherboard with old GPU or newer GPU with older board) Your PCI-e speed on the motherboard won't affect koboldAI run speed. If you want performance your only option is an extremely expensive AI Without Linux you'd probably need to put a bit less on the GPU but it should definately work. bat to start Kobold AI. amd has finally come out and said they are going to add rocm support for windows and consumer cards. Some implementations (I use the oobabooga UI) are able to use the GPU primarily but also offload some of the memory and computation They don't no, at least not officially and getting that working isn't worth it. As the others have said, don't use the disk cache because of how slow it is. 30/hr, you’d need to rent 5,000 hours of GPU time to equal the cost of a 4090. I have a 12 GB GPU and I already downloaded and installed Kobold AI on my machine. While my GPU is at 60% and VRAM used, the speed is low for guanaco-33B-4_1 about ~1 token/s. The . If you load the model up in Koboldcpp from the command line, you can see how many layers the model has, and how much memory is needed for each layer. It was running crazy slow, no out put after more than 15 min other than 2 words and it was running off of cpu only. You can use it to write stories, Also know as Adventure 2. You don't train GGUF models as that would be worse since then your stuff is limited to GGUF and its libraries don't focus on training. Kobold runs on Python, which you cannot run on Android without installing a third-party toolkit like QPython. When I replace torch with the directml version Kobold just opts to run it on CPU because it didn't recognize a CUDA capable GPU. I'm using Docker via WSL, so that adds /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 The recent datacenter GPUs cost a fortune, but they're the only way to run the largest models on GPUs. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, In that case you won't be able to save your stories to google drive, but it will let you use Kobold and download your saves as json locally. Works fast with no issues. Hello. The issue this time is that I don't know how to navigate KoboldAI to do that. Subreddit for the in-development AI storyteller NovelAI. 4GB), as the GPU uses 16-bit math. 2/6GB for built in vram. I am new to the concept of AI storytelling software, sorry for the (possible repeated) question but is that GPU good enough to run koboldAI? For PC questions/assistance. As an addendum, if you get an used 3090 you would be able to run anything that fits in 24GB and have a pretty good gaming GPU or for anything else you wanna throw at it. I don't really know what suppose to be better Vulkan or ROCm, but I know Vulkan seems to work fine with older gpus. If you have a specific Keyboard/Mouse/AnyPart that is doing something strange, include the model number i. I read that I wouldn't be capable of running the normal versions of Kobold AI koboldcpp does not use the video card, because of this it generates for a very long time to the impossible, the rtx 3060 video card. Kobold AI and RTX 4090 - best options to use? /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. So in my example there's three GPUs in the system, and #1 and #2 are used for the two AI servers. I'll update this post to see how long I can use this wonderful AI. Fit as much on the GPU as you can. As far as I know half of your system memory is marked as "shared GPU memory". safetensors file should be about 4. I was picking one of the built-in Kobold AI's, Erebus 30b. Valheim; Genshin sure but I think if you let it spill into "shared GPU memory" then it's going to have to swap out out to get the gpu to process it, where if you offload layers to cpu then the cpu I downloaded the smaller x64-nocuda version and in the GUI set the preset to "Vulkan NoAVX2 (Old CPU)", then maxed the GPU layers (if possible). You don't get any speed-up over one GPU, but you can run a bigger model. isavailable(). The model requires 16GB of Ram. I had a failed install of Kobold on my computer I'm new to Koboldai and have been playing around with different GPU/TPU models on colab. Right now I have an RX Would used k, p, and m series Tesla GPU's be suitable for such? And how much VRAM would i be looking at to run a 30b model? Just as the title says, it takes 27 seconds on gpu and 18 seconds on cpu (generating a longer version) even on the same prompt. Try closing other programs until your GPU no longer uses the shared memory. e. But the 2. Someone posted this in response to some questions of ive downloaded, deleted and redownloaded Kobold multiple times, turned off my antivirus, and followed every instruction, however when i try and run the "play" batch file, it'll say "GPU support not found" is there way i can get my GPU working so i dont have to allocate all layers to my CPU? Start by trying out 32/0/0 gpu/disk/cpu. I'm going to be installing this GPU in my server PC, meaning video output isn't a KoboldAI is originally a program for AI story writing, The problem is that these guides often point to a free GPU that does not have enough VRAM for the default settings of VenusAI or We're now read-only indefinitely due to Reddit Incorporated's poor management and decisions related to third party platforms and content Yes, I'm running Kobold with GPU support on an RTX2080. You can also add layers to the disk cache but that would slow it down even more. /play. 7B models will work better speed wise since those will fit completely. KoboldAI join leave 12,075 readers. If Your PC can handle it, You can also use 4bit LLAMA models for Your PC, which uses the same amount of processing power but just plain better. EDIT: Problem was solved. r/NovelAi. you can do a partial/full off load to your GPU using openCL, I'm using an RX6600XT on PCIe 3. So, I found a pytorch package that can run on Windows with an AMD GPU (pytorch-directml) and was wondering if it would work in KoboldAI. Great card for gaming. depending on your cpu and model size the speed isn't too bad. Run out of VRAM? try 16/0/16, if it works then 24/0/8, and so on. I have 32GB RAM, Ryzen 5800x CPU, and 6700 XT GPU. I didn't leave room for other stuff on the GPU. For system ram, you can use some sort of process viewer, like top or the windows system monitor. kobold. Or Kobold didn't used my GPU at all, just my RAM and CPU. I have three questions and wondering if I'm doing anything wrong. it shows gpu memory used. anyone know if theres a certain version that allows this or if im just being a huge idiot for not enabling some CPU: i3 10105f (10th generation) GPU: GTX 1050 (up to 4gb VRAM) RAM: 8GB/16GB. I am not sure if this is potent enough to run koboldAI, as system req are nebulous. So you can get a bunch of normal memory and load most of it into the shared gpu memory. And probably the best option is Hello, TLDR: with clblast generation is 3x slower than just CPU. downloaded the latest update of kobold and it doesn't show my CPU at all. There are two options: KoboldAI Client: This is the "flagship" client for Kobold AI. :3 Get the Reddit app Scan this QR code to download the app now. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, 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. For watherver reason Kobold can't connect to my GPU, here is something funny though It used to work fine. 6b works perfectly fine but when I load in 7b into KoboldAI the responses are very slow for some reason and sometimes they just stop working. 42 MiB free; 7. It's pretty cheap for good-enough-to-chat GPU horsepower. I’ve already tried setting my GPU layers to 9999 as well as to koboldcpp is your friend. You can use it to write stories, blog posts, Not all GPU's support Kobold. 9 GB and so on and so forth, it seems every back and forth increases my memory usage by . GPU layers I've set as 14. If you want to try kobold and haven't yet, Shop Collectible Avatars; Get the Reddit app Scan this QR code to download the app now. It should open in the browser now. I used to have a version of kobold that let me split the layers between my GPU and CPU so i could use models that used more VRAM than my GPU could handle, and now its completely gone. Similarly the CPU implementation is limited by the amount of system RAM you have. I'm wondering what the differences will be. When I offload layers to the GPU, can I specify which GPU to offload them to, or is it always going to default to GPU0? 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. Instead use something like Axolotl, personally I would opt for Lora training since its cheaper and then merging it to base. I set my GPU layers to max (I believe it was 30 layers). g. Kobold AI utilises my GPU and can respond to something that takes Kobold AI Lite 2-3 minutes, in under 10 seconds. You can distribute the model across GPUs and the CPU in layers. My cpu is at 100% Share Add a Comment. We don't allow easy access to the smaller models on the TPU colab so people do not waste TPU's on them. Basically it defaults to everything on the GPU but you can take some layers from the GPU and not assign them to anything and that will force it to use some of the system ram. Internet Culture (Viral) Amazing; 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, Kobold will give You the option to split between GPU/CPU and RAM (Don't use disk cache). KoboldAI uses this command, but when I tried this command out on my normal python shell, it returned true, however, the aiserver doesn't. Actions take about 3 seconds to get text back from Neo-1. Your computer is probably faster than a lot of the 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). 5-3B/parameter so if I had to guess, if there’s an 8-9 billion parameter model it could very likely run that without problem and it MIGHT be able to trudge through the 13 billion parameter model if you use less intensive settings (1. The "Max Tokens" setting I can run is currently 1300-ish, before Kobold/Tavern runs out of memory, which I believe is using my ram(16GBs), so lets just assume that. A slightly older Cray CS-Storm supports 8 GPUs and is closer to $300. https://lite. To do that, click on the AI button in the KoboldAI browser window and now select the Chat Models Option, in which you should find all PygmalionAI Models. Reply reply It's just that I didn't want to accept that a GPU I bought recently and spent so much on I recently bought an RTX 3070. So if you're loading a 6B model which Kobold estimates at ~16GB VRAM used, each of those 32 layers should be around 0. Context size 2048. Reply reply Dear-Ad-798 The reason its not working is because AMD doesn't care about AI users on most of their GPU's so ROCm only works on a handful of them. Start Kobold (United version), and load For hypothetical's sake, let's just say 13B Erebus or something for the model. Q2: Dependency hell A place to discuss the SillyTavern fork of TavernAI. AI, human enhancement, etc. get reddit premium. And likewise we only list models on the GPU edition that the GPU edition can run. Then, make sure you’re running the 4 bit kobold interface, and have a 4bit model of pygb. 18 and $0. When I'm generating, my CPU usage is around 60% and my GPU is only like 5%. Even at $. I'm using mixtral-8x7b. I usually leave 1-2gb free to be on the I'd personally hold off on buying a new card in your situation as Vulkan is in the finishing stages and should allow the performance on your GPU to increase a lot in the coming months without you having to jump trough ROCm hoops. Until I hit the context limit I need about a minute per reply for 13B. Hey all. I have a ryzen 5 5600x and a rx 6750xt , I assign 6 threads and offload 15 layers to the gpu . This /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will I want to use a 30b on my RTX 6750 XT + 48GB RAM. Look at the shared GPU memory. I followed the readme to the letter, but was EDIT 2: Turns out, running aiserver. 5GB (I think it might not actually be that consistent in practice but close enough for estimating the layers to put onto GPU). The offline routines are completely different code than the one for the colab instance, and while the colab instance loads the model directly into the GPU ram while supporting the half mode that makes it ram friendly, the local routines seem to load Yes, Kobold cpp can even split a model between your GPU ram and CPU. I am using the downloaded version of Kobold AI and a 2. So given your large budget get a 3090 (I'd personally wait until you can get them closer to msrp because right now you'd spend your entire budget while you should be spending half that in a normal market). https://www. Am I missing something super obvious? Or should I just get used to the long response time? Thanks for any Get the Reddit app Scan this QR code to download the app now. 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 Hi everyone I have a small problem with using kobold locally. I start Stable diffusion with webui-user. Just set them equal in the loadout. 6 GB after a single back and forth If the GPU is like the AI's brain, its very possible my gtx 1080 just can not handle the job of making sense of anything. GPU Recommendations upvotes 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. Haven't been able to get Kobold to recognize my GPU . Hello, I recently bought an RX 580 with 8 GB of VRAM for my computer, I use Arch Linux on it and I wanted to test the Koboldcpp to see how the results looks like, the problem isthe koboldcpp is not using the ClBlast and the only options that I have available are only Non-BLAS which is not using the GPU and only the CPU. You can't run high end models without a tpu. 6-Chose a model. In GPU mode 16GB of system ram could squeeze it in your GPU but 32GB gives you space for the rest of your system. 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 . I also don't know much about the Cray, some of those old servers might require licensing to run so do some homework first. Most 6b models are even ~12+ gb. 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 be 20 layers or 40. You can rent GPU time on something like runpod. Anyway! got the layer adjustment working by downloading only this particular make of Kobold. Slows things down. The AI always takes around a minute for each response, reason being that it always uses 50%+ CPU rather than GPU. Note: You can 'split' the model over multiple GPUs. With 10 layers on the GPU my response times are around 1 minute with a 1700X overclocked to 3,9GHz. In my experience, the 2. This is a community to share and discuss 3D photogrammetry modeling. net. In 99% of scenarios, using more GPU layers will be faster. 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. It has the same, if not better, community input as NovelAI, as you can talk directly to the devs at r/KoboldAI with suggestions or problems. I've reisntalled both kobold and The unofficial but officially recognized Reddit community discussing the latest LinusTechTips, TechQuickie and other LinusMediaGroup Only if you have a low VRAM GPU like an Nvidia XX30 series with 2GB or so. A few days ago, Kobold was working just fine via Colab, and across a number of models. 00 GiB total capacity; 7. If you want to follow the progress, /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, Get the Reddit app Scan this QR code to download the app now. When choosing Presets: Use CuBlas or CLBLAS crashes with an error, works only with Hello everyone, I am thinking of buying a new video card for the AI which I primary use for chatting and storytelling. The timeframe I'm not sure. when you load the model, load in 22 layers in GPU, and set your context token size in tavern to 1500, and your response token limit to about 180. A second question would be - I assume that I will need to updgrade to using paid AWS "instances" - is it worth it ? I've seen its possible to install a kobold ai on my pc but considering the size of the NeoX Version even with my RTX4090 and 32GB Ram I think I will be stuck with the smaller modells. If you want to follow the progress, come join our Discord server /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will My GPU/CPU Layers adjusting is just gone to be replaced by a "Use GPU" toggle instead. My old video card is a GTX970. View community ranking In the Top 10% of largest communities on Reddit. It doesn't use the GPU or its memory. 6b ones, you scroll down to the gpu section and press it there. So now its much closer to the TPU colab, and since TPU's are often hard to get, don't support all models and have very long loading times this is just nicer to use for people. If you want to run the full model with ROCM, you would need a different client and running on Linux, it seems. Please use our Discord server instead of supporting a company that acts against its users and unpaid Get the Reddit app Scan this QR code to download the app now. I'm looking into getting a GPU for AI purposes. Tried to allocate 100. io. You won't get a message from google, but the Cloudfare link will lose connection. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, But at this stage there is no ai/model loaded, so you'll need to click on the AI button/tab at the top and select one you want. it turns out torch has this command called: torch. I'm pretty new to this and still don't know how to use a AMD GPU. Welcome to KoboldAI on Google Colab, GPU Edition! KoboldAI is a powerful and easy way to use a variety of AI based text generation experiences. You may also have tweak some other settings so it doesn't flip out. Recently i downloaded Kobold AI out of curiosity and to __main__:device_config:916 - Nothing assigned to a GPU, reverting to CPU only mode You are using a model of type gptj to instantiate a model of type gpt_neo. 00 MiB (GPU 0; 10. So doable? Absolutely if you have enough VRAM. You can split the If you have a beefy PC with a good GPU, you can just download your AI model of choice, install a few programs, and get the whole package on your own PC so you can play offline. A new card like a 4090 or 4090 24GB is useful for things other than AI inference, which makes them a better value for the home gamer. However, during the next step of token generation, while it isn't slow, the GPU use drops to zero. Dedicated players will have noticed this be available already, i also already saw the link shared out before on the Reddit. The software for doing inference is getting a lot better, and fast. Even lowering the number of GPU layers (which then splits it between GPU VRAM and system RAM) slows it down tremendously. I think I had to up my token length and reduce the WI depth to get it Windows takes at least 20% of your GPU (and at least 1GB). To do that, click on the AI button in the Kobold ai Browser window and now select The Chat Models Option, in which you should find all PygmalionAI Models chose a model that fits in your RAM or VRAM if you have a Supported Nvidia GPU. Originally we had seperate models, but modern colab uses GPU models for the TPU. There was no adventure mode, no scripting, no softprompts and you could not split the model between different GPU's. I'd like some pointers on the best models I could run with my GPU. Make sure the one you choose will fit on your gpu, each model will tell you how vram (gpu ram) it needs. I think mine is set to 16 GPU and 16 Disk. When I used up all threads of one CPU, the command line window and the refreshing of the line graph in task manager sometimes 'frozen', I must manually press Enter in cmd window to keep the koboldAI program processing. bat . I've been trying to get flash attention to work with kobold before the upgrade for at least 6 months because I knew it would really Subreddit for the in-development AI storyteller NovelAI. The biggest reason to go Nvidia is not Kobold's speed, but the wider compatibility with the projects. 3B. sh. py by itself lets the gpu be detected, but K80, K40, other Nvidia Kepler GPU people, Subreddit for the in-development AI storyteller NovelAI. io along with a brief walkthrough / tutorial . And according to my task manager, I am not even using all of my GPU or CPU when generating. 5-3 range but doesn’t follow the colab KoboldCpp allow offloading layers of the model to GPU, either via the GUI launcher or the --gpulayers flags. 5 minutes for a response from one. I would advise against ever touching the second slider, especially if you run on an SSD. Get the Reddit app Scan this QR code to download the app now. There is dedicated and shared gpu memory, however I do not really understand the difference. I'm not really into any particular style, I would just like to experiment with what this technology can do, so no matter if it's SFW or not, geared toward adventure, novel, chatbot, I'd just like to try the best models that my GPU can I've already tried forcing KoboldAI to use torch-directml, as that supposedly can run on the GPU, but no success, as I probably don't understand enough about it. Discussion for the KoboldAI story Get the Reddit app Scan this QR code to download the app now. I. It does require about 19GB of VRAM for the full 2048 context size, so it may be tough to get this running without access to a 3090 or better. 58 GiB already allocated; 98. https: /r/StableDiffusion is back open after the I'm gonna mark this as NSFW just in case, but I came back to Kobold after a while and noticed the Erebus model is simply gone, along with the other one (I'm pretty sure there was a 2nd, but again, haven't used Kobold in a long time). Kobold Horde is mostly designed for people without good GPUs. Share your Termux configuration, custom utilities and usage experience or help others troubleshoot issues. dev, which seems to use RAM and the GPU on windows. Edit 2: Using this method causes the GPU session to run in the background, and then the session closes after a few lines. s. If you want to run the 2. Members Online /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app Hey i recently tried useing the google colab for kobold ai bc i'm to stupid to understand how to run a server and ai system off my native hardware Coins 0 coins Models seem to generally need (for recommendation) about 2. System specs: 13600KF, RX 6700xt Whenever I run an LLM in Kobold, despite theoretically having all of the layers on the GPU, my CPU seems to be doing Subreddit to discuss about Llama, the large language model created by Meta AI. More info: Horde will allow you to contribute your own GPU (or any other Kobold instance) to the community so others can use it to power KoboldAI. Those will use GPU, and not tpu. But luckily for you the post you replied to is 9 months old and a lot happens in 9 months. This should work with an AMD Polaris GPU. I currently use MythoMax-L2-13B-GPTQ, which maxes out the VRAM of my RTX 3080 10GB in my gaming PC without blinking an eye. As i am an AMD user I need to focus on RAM, you can check both Kobold is automatically leveraging both cards for compute, and I can watch their VRAM fill up as the model loads, but despite pushing all 33 layers onto the GPU(s) I've also seen the system memory get maxed out as well. As a beginner to chat ai's I really appreciate it you explaining everything in so much detail. 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). In my case I am able to get 10 tokens per second on a 3090 on a 30B model without the long processing times, because I can fit the entire model in my GPU. Beware that you may not be able to put all kobold model layers on the GPU (let the rest go to CPU). Let's assume this response from the AI is about 107 tokens in a 411 character response. If we list it as needing 16GB for example, this means you can probably fill two 8GB GPU's evenly. With a 4090, you are well positioned to just do all this locally. koboldai. 7B this is a clone of the AI Hi, thanks for checking out Kobold! You can host the model on Google Colab, which will not require you to use your GPU at all. Hello Kobolds! KoboldAI is now over 1 year old, and a lot of progress has been done since release, only one year ago the biggest you could use was 2. I put up a repo with the Jupyter Notebooks I've been using to run KoboldAI and the SillyTavern-Extras Server on Runpod. This is mainly just for people who may already be using SillyTavern with OpenAI, Horde, or a local installation of KoboldAI, and are ready to pay a few cents an hour to run KoboldAI on better hardware, but just don't know Then also make sure not much is using the GPU in the background beforehand. You can then start to adjust the number of GPU layers you want to use. Running on GPU is much faster, but you're limited by the amount of VRAM on the graphics card. Assuming you have an nvidia gpu, you can observe memory use after load completes using the nvidia-smi tool. Internet Culture (Viral) Amazing Kobold ai isn't using my gpu . The gpt4-x-alpaca model for it is the best model I ever used. With koboldcpp, you can use clblast and essentially use the vram on your amd gpu. Google changed something, can't quite pinpoint on why this is suddenly happening but once I have a fix ill update it for everyone at once including most unofficial KoboldAI notebooks. You can use kobold lite and let other kind folks in the horde do the generation for you. cpp works pretty well in windoes and seems to use the gpu to some degree. nvidia-smi -i 1 -c EXCLUSIVE_PROCESS nvidia-smi -i 2 -c EXCLUSIVE_PROCESS. /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. Originally the GPU colab could only fit 6B models up to 1024 context, now it can fit 13B models up to 2048 context, and 20B models with very limited context. ai which was able to run stable diffusion in GPU mode for You can also run a cost benefit analysis on renting gpu time vs buying a loca GPU. The only difference is the size of the models. But I have more recently been using Kobold AI with Tavern AI. I use it on my laptop, good depends on the CPU speeds you can get. 7B. But its finally to a point i consider it stable, with a stable method to distribute the model file (Hopefully Reddit won't crash it :P). 7 GB model. Either or both. Reply reply e. In terms of GPUs, that's either 4 24GB GPUs, or 2 A40/RTX 8000 / A6000, or 1 A100 plus a 24GB card, or one Subreddit for the in-development AI storyteller NovelAI. Pretrains are insanely expensive and can easily cost someones entire savings to do on the level of Llama2. By splitting layers, you can move some of the memory requirements around. You can claw back a little bit more performance by setting your cpu threads/cores and batch threads correctly. Token Streaming (GPU/CPU only) by one-some. Then I saw SHARK by Nod. As I understand it you simply divide the total memory requirement by the number of layers to get the size of each layer. When asking a question or stating a problem, please add as much detail as possible. 4 GB to 4. If you set them equal then it should use all the vram from the GPU and 8GB of ram from the PC. nah is not really good to run the program let alone the models as even the low end models requiere a bigger gpu, you have to use the collabs though if you want to do that i recommend using the tpu collab as is bigger and it gives better responses than the gpu collab in short 4gb is way to low to run the program using the collabs are the only way to use the api for janitor ai in In today's AI-world, VRAM is the most important parameter. Or check it out in the app stores     TOPICS. In my case I have a directory just called "AI" Go to the directory in Terminal and type Kobold comes with its own python and automatically installs the correct dependencies if you use play-rocm. If it's 0, then your GPU is running the model in VRAM and it should work fine. So you will need to reserve a bit more space on the first GPU. I don't want to split the LLM across multiple GPUs, but I do want the 3090 to be my secondary GPU and leave my 4080 as the primary available for other things. Keeping that in mind, the 13B file is A place to discuss the SillyTavern fork of TavernAI. I have a ryzen 5 5500 with an RX 7600 8gb Vram and 16gb of RAM. llama_model_load_internal: offloading non-repeating layers to GPU llama_model_load_internal: offloaded 33/33 layers to GPU llama_model_load_internal: total VRAM used: 3719 MB llama_new_context_with_model: kv self size = 4096. To full offload leave everything default but with 99 layers. 10 users here now. This subreddit has gone Restricted and reference-only as part of a mass protest against Reddit's recent API changes, I was wondering if Kobold AI supports memory pooling through NVLink or spreading the VRAM load over As you load your model you will be asked how you wish to apportion the model across all detected/supported GPUs and CPU /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will For anyone struggling to use kobold Make sure to use the GPU collab version, For kobold ai the token size the number has to be less then 500 which is usually why the responses are shorter comspre to openai /r/GuildWars2 is the primary community for Guild Wars 2 on Reddit. Now we need to set Pygmalion AI up in Kobold AI. This is much slower though. in general with gguf 13b the first 40 layers are the tensor layers, these are the model size split evenly, the 41st layer is the blas buffer, and the last 2 layers are the kv cache (which is about 3gb on its own at 4k context) It's how the model is split up, not GB. I did all the steps for getting the gpu support but kobold is using my cpu instead. Db0 manages it, so he will ultimately be the arbiter of the rules as far as a need for contributions. If you want to follow the progress, Okay, so I made a post about a similar issue, but I didn't know that there was a way to run KoboldAI Locally and use that for VenusAI. For non-headless linux, Attempting Janitor AI & it says out of memory—GPT2 did not give me the option to use anything other than GPU. PCI-e is backwards compatible both ways. GPUs are limited on how much they can take on by their VRAM and the CPU will use system memory. It's usable. What happens is one half of the 'layers' is on GPU 0, and the other half is on GPU 1. I have a i7, 12 g ram, Nvidia gtx1050 I've been installing kobold ai to use the novel models. Don't fill the gpu completely because inference will run out of memory. It is also more Welcome to KoboldAI on Google Colab, GPU Edition! KoboldAI is a powerful and easy way to use a variety of AI based text generation experiences. Using CUDA_VISIBLE_DEVICES: For one process, set CUDA_VISIBLE_DEVICES to your first gpu; First batch file: CUDA_VISIBLE_DEVICES=1 . (P. Official Reddit community of Termux project. Links to different 3D models, images, articles, and videos related to 3D photogrammetry are highly encouraged, e. Is a 3080 not enough for this? I knowthat best solution Will be running kobold on Linux WITH AMD GPU, but i must run on Mac. Gaming. If you're getting this error, and you've simply moved your Kobold folder, then you're best reinstalling to that folder directly instead. Now there are ways to run AI inference at 8-bit (int8) and 4-bit (int4). So here is the formal release, ColabKobold 6B Edition. While the P40 is for AI only. Starting Kobold HTTP Server on port 5001 This one is pretty great with the preset “Kobold (Godlike)” and just works really well without any other adjustments. , it's using GPU for analysis, but not for generating output. Is there any alternative to get the software required for Kobold AI? Skip to main you should be able to run with all layers in GPU, and get replies in about 15-45 seconds, depending on how long We're now read-only indefinitely due to Reddit Incorporated's poor management and decisions related to third party platforms and I used the readme file as an instruction, but I couldn't get Kobold Ai to recognise my GT710. A phone just doesn't have the computational power. There still is no ROCm driver, but we now have Vulkan support for that GPU available in the product I linked which will perform well on that GPU. If I put that card in my PC and used both GPUs, would it improve performance on 6B models? Right now it takes approx 1. Second batch file: There’s the layers thing in settings. I do not think it can deal with anything over 8. 7B models take about 6GB of VRAM, so they fit on your GPU, the generation times should be less than 10 seconds (on my RTX 3060 is 4 s). You can get used rack servers with GPU support on ebay fairly cheap. The context is put in the first available GPU, the model is split evenly across everything you select. The session closes because the GPU session exits. It's almost always at 'line 50' (if that's a thing). /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, Subreddit for the in-development AI storyteller NovelAI. My cpu is at 100% Get an ad-free experience with special benefits, and directly support Reddit. With Token Streaming enabled you can now get a real time view of what the AI is generating, don't like where it is going? You can abort the generation early so you do not have to wait for the full generation to complete. Make sure you start Stable diffusion with --api. gdvhiz pmtzxb ivwjco lpgsg lvphvu hga tlb iwasy lvzwr uxaeg