Kobold ai gpu reddit Not only is chrome OS most likely incompatible, i dont think any models of chromebooks are powerful enough to host kobold. (newer motherboard with old GPU or newer GPU with older board) Your PCI-e speed on the motherboard won't affect koboldAI run speed. You can also add layers to the disk cache but that would slow it down even more. They don't no, at least not officially and getting that working isn't worth it. 2023-05-15 21:20:38 INIT | Searching | GPU support 2023-05-15 21:20:38 INIT | Not Found | GPU support 2023-05-15 21:20:38 INIT | Starting | Transformers" The model is loading into the RAM instead of my GPU. 7B-Nerys-v2 that would mean 32 layers on the GPU, 0 on disk cache. 5 version, I found the 1. By splitting layers, you can move some of the memory requirements around. Run out of VRAM? try 16/0/16, if it works then 24/0/8, and so on. I think I had to up my token length and reduce the WI depth to get it the model 6B worked for me only in specific conditions. bat to start Kobold AI. Set GPU layers to 25 and context to 8k. The k80 from my understanding registers as 2 gpu's by the OS, so I wondered if KoboldAI would be able to load into vram of both GPU or not. So I also was disappointed with Erebus 7b. This is a browser-based front-end for AI-assisted writing with multiple local & remote AI models. This is much slower though. 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. Anything less than 12gb will limit you to 6-7b 4bit models, which are pretty disappointing. When offloading some work to the GPU, it's mostly being bottlenecked by the CPU, so there's not much difference between frameworks. I've tried both koboldcpp (CLBlast) and koboldcpp_rocm (hipBLAS (ROCm)). I was picking one of the built-in Kobold AI's, Erebus 30b. Reply reply Dear-Ad-798 Disk cache will slow things down, it should only be used if you do not have the RAM to load the model. What happens is one half of the 'layers' is on GPU 0, and the other half is on GPU 1. The software for doing inference is getting a lot better, and fast. Context size 2048. I've used it with both the 13B and the 7B Vicuna models, and the 7B version runs about twice as fast. I have RTX 3060 12GB. g. Thank you This is incorrect. KoboldAI is not an AI on its own, its a project where you can bring an AI model yourself. With these, on a 13B (can settle with a lower model if this ain't possible), what is a good layer ratio between GPU and Disk Cache? Apr 10, 2024 · The GPU usage displayed only reflects the usage of the 3D engine; it does not show the utilization of AI acceleration computations. typically, people train a model as a huggingface transformer (do not ask me, I don't know how to set that up) and then convert it to GGUF or their preferred format. Memory usage probably is CUDA overhead. Don't fill the gpu completely because inference will run out of memory. And the AI's people can typically run at home are very small by comparison because it is expensive to both use and train larger models. I don't think you can, sadly your out of luck. It should open in the browser now. I currently rent time on runpod with a 16vcore CPU, 58GB ram, and a 48GB A6000 for between $0. I have a ryzen 5 5600x and a rx 6750xt , I assign 6 threads and offload 15 layers to the gpu . It's power is wholly reliant on volunteers onboarding their own PC to generate for others. is the "quantization" of the model. It offers the standard array of tools, including Memory, Author's Note, World Info, Save & Load, adjustable AI settings, formatting options, and the ability to import existing AI Dungeon adventures. ) The OS needs some memory as does everything else with a graphical element. Originally we had seperate models, but modern colab uses GPU models for the TPU. I recently bought an RTX 3070. Slows things down. There’s the layers thing in settings. Edit: I tried physically swapping the card positions, while the nvidia-smi shows the GPU IDs swapped, kobold still shows the 1070 as device 0 Share Add a Comment Sort by: 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. Try putting the layers in GPU to 14 and running it,, edit: youll have a hard time running a 6b model with 16GB of RAM and 8gb of vram. They are the best of the best AI models currently available. 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. When asking a question or stating a problem, please add as much detail as possible. I haven't really noticed a huge difference in the quality, though - some super surprising stuff has come out of 7B - and so I now tend to stick with 7B just becau So in my example there's three GPUs in the system, and #1 and #2 are used for the two AI servers. So I have a 4090 for my GPU (you didn't mention your current GPU, but it's 24gb VRAM like your 3090). We don't allow easy access to the smaller models on the TPU colab so people do not waste TPU's on them. So you can get a bunch of normal memory and load most of it into the shared gpu memory. , it's using GPU for analysis, but not for generating output. You For anyone struggling to use kobold Make sure to use the GPU collab version, and make sure the version is United. since I wrote that the gpu does not have enough memory or something like that. With kobold, batch size = 1. (There's also a 1. 7B), went into the agnai. 4GB), as the GPU uses 16-bit math. The model requires 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. I'm going to be installing this GPU in my server PC, meaning video output isn't a We would like to show you a description here but the site won’t allow us. The only difference is the size of the models. exe". 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. 4 and 5 bit are common. 3b model answers very wierd and long. With koboldcpp, you can use clblast and essentially use the vram on your amd gpu. com Discussion for the KoboldAI story generation client. Should I grab a different model? 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. Using multiple GPUs works by spreading the neural network layers across the GPUs. This uses CL blast (works for every GPU), see other command line options here. 16GB Ram. 5-3 range but doesn’t follow the colab recommendation of 6. Fit as much on the GPU as you can. I have three questions and wondering if I'm doing anything wrong. You can find them on Hugging Face by searching for GGML. This is the part i still struggle with to find a good balance between speed and intelligence. It is already supported by ST for both image and text generation. If I were in your shoes, I'd consider the price difference of selling a 2080S and buying a 3090. Then go to the TPU/GPU Colab page (it depends on the size of the model you chose: GPU is for 1. Offload 41 layers, turn on the "low VRAM" flag. If your answers were yes, no, no, and 32, then please post more detailed specs, because 0. The problem is that we're having in particular trouble with the multiplayer feature of kobold because the "transformers" library needs to be explicitly loaded before the others (e. 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. "kobold-client-plugin") can be used. 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. However, during the next step of token generation, while it isn't slow, the GPU use drops to zero. Anyone having experience doing that? What TTS has a cmdl parameter for selecting the GPU? 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. When running solely on GPU cuda tends to outperform OpenCL but only if you say generate 200 batches at the same time. This resulted in a minor but consistent speed increase (3 t/s to 3. Fan art, D&D game stories, discussions, strategy building, lore, or trap ideas are always welcome! -- Check the top bar or the pinned posts for the official Kobold Legion Discord server. I didn't find a way to use both CPUs. Kobold Ai collab link. If you’ve got more system memory, CPU generation does work just as well, just with 30-60 seconds per. GPU: GTX 1050 (up to 4gb VRAM) RAM: 8GB/16GB. Each will calculate in series. I have a ryzen 5 5500 with an RX 7600 8gb Vram and 16gb of RAM. 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. On my 1070 I need to pare right down what’s running to get even GPU generation to run. If we list it as needing 16GB for example, this means you can probably fill two 8GB GPU's evenly. ) You have found the old KoboldAI GPU colab! Because the legacy KoboldAI is incompatible with the latest colab changes we currently do not offer this version on Google Colab until a time that Jul 17, 2024 · Actually, I installed it in this machine to offload the TTS from the 4070. Using CUDA_VISIBLE_DEVICES: For one process, set CUDA_VISIBLE_DEVICES to your first gpu; First batch file: CUDA_VISIBLE_DEVICES=1 . Good contemders for me were gpt-medium and the "Novel' model, ai dungeons model_v5 (16-bit) and the smaller gpt neo's. 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. 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. For me it shows the same error, for the TPU edition google colab. Most 6b models are even ~12+ gb. If that happens, try again later, or consider grabbing a Pro subscription for $10/mo if you plan to use it frequently. 7B which gave me answers very quickly. There are two options: KoboldAI Client: This is the "flagship" client for Kobold AI. For system ram, you can use some sort of process viewer, like top or the windows system monitor. Great card for gaming. Ordered a refurbished 3090 as a dedicated GPU for AI. ggmlv3. 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? Hey, did you get the k80 working? I'm still curious how those would work. 0 better but haven't tested much. As far as I know half of your system memory is marked as "shared GPU memory". You can claw back a little bit more performance by setting your cpu threads/cores and batch threads correctly. This is the place to break free from the typical kobold stereotypes and show the world kobolds are strong in their own ways. I actually prefer 2. This bat needs a line saying"set COMMANDLINE_ARGS= --api" Set Stable diffusion to use whatever model I want. I think mine is set to 16 GPU and 16 Disk. If you set them equal then it should use all the vram from the GPU and 8GB of ram from the PC. 7b-neo horni run on the cpu vs a smaller one run on gpu A place to discuss the SillyTavern fork of TavernAI. Myabe I'm doing it wrong, idk, but seems webui is much easier for me. I can't use oobabooga for some reason, but KoboldCpp works very well. 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. If you choose minus one you choose to give the GPU (the fastest person of the group) all the work and let the others do nothing. Kobold runs on Python, which you cannot run on Android without installing a third-party toolkit like QPython. In 99% of scenarios, using more GPU layers will be faster. as a result, the AI was pretty stupid with such settings. For PC questions/assistance. 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. In the Top right there Should be three lines also known as a burger menu, click on it. Now there are ways to run AI inference at 8-bit (int8) and 4-bit (int4). e. 53 votes, 61 comments. cpp for inference. See full list on github. Each GPU does its own calculations. I want to make an AI assistant (With TTS and STT). 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. When I'm generating, my CPU usage is around 60% and my GPU is only like 5%. Lowering the "bits" to 5 just means it calculates using shorter numbers, losing precision but reducing RAM requirements. however, llamacpp does actually have the capacity to train models using the "train-text-from-scratch. Are the GPU layers maxed? For let's say OPT-2. And if that fastest person can handle the amount of work that is the best option for Kobold because of how fast those GPU's are. 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. 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. I. My cpu is at 100% For GPU users you will need the suitable drivers installed, for Nvidia this will be the propriatary Nvidia driver, for AMD users you will need a compatible ROCm in the kernel and a compatible GPU to use this method. After some testing and learning the program, I currently am using the 8GB Erebus model. AI, ChatGPT, Bing, etc) all need a powerful GPU to run on. 5-2 tokens per second seems slow for a recent-ish GPU and a small-ish model, and the "pretty beefy" is pretty ambiguous. 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 By the way, the original kobold ai client (not cpp) is still available on github, and it does have an exe installer for Windows. I set my GPU layers to max (I believe it was 30 layers). I looked at Quadro m6000 as another alternative, though pricier I can find for $500 and it's a single card with 24gb vram. **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. Either that, or just stick with llamacpp, run the model in system memory, and just use your GPU for a bit of trivial acceleration. and so I moved the sliders to disk and cpu . unlike 2. 10K subscribers in the KoboldAI community. 30/hr, you’d need to rent 5,000 hours of GPU time to equal the cost of a 4090. The AI Horde is a FOSS cluster of crowdsourced GPUs to run Generative AI. 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. Hey all. We would like to show you a description here but the site won’t allow us. 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). You'll have the best results with PCIE 4. I have --useclblast 0 0 for my 3080, but your arguments might be different depending on your hardware configuration. Yes, I use it. 00 MB Load Model OK: True Embedded Kobold Lite loaded. I’ve already tried setting my GPU layers to 9999 as well as to -1. Its not guaranteed to make it faster, but on a dedicated GPU this is indeed the way to get the best speed. 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. GPU layers I've set as 14. You can use google colab and link kobold from there, but I have no clue how to do that. Before you set it up there is a lot of confusion about the kind of hardware people need because AI is a lot heavier to run than video games. It's a simple executable that combines KoboldLite UI with llama. Discussion for the KoboldAI story generation client. It requires GGML files which is just a different file type for AI models. 3 can run on 4GB which follows the 2. unfortunately, it's very poorly documented and I know this because I was fiddling around with it a few days ago out of Note: You can 'split' the model over multiple GPUs. It's how the model is split up, not GB. I'm using mixtral-8x7b. Start Kobold (United version), and load AMD's dumpster fire in the GPU market is going to cripple them in the AI market. 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. My old video card is a GTX970. . chat settings, changed the default AI service to Kobold, left the default preset to none (have also tried horde), popped in my cloudfare. com URL when running remote (yes I know it changes), and left the 3rd party format to kobold. The AI always takes around a minute for each response, reason being that it always uses 50%+ CPU rather than GPU. Enjoy. /play. 0 x16 GPU, because prompt ingestion bottlenecks to PCIE bus bandwidth. The JAX version can only run on a TPU (This version is ran by the Colab edition for maximum performance), the HF version can run in the GPT-Neo mode on your GPU but you will need a lot of VRAM (3090 / M40, etc). I start Stable diffusion with webui-user. 5GB (I think it might not actually be that consistent in practice but close enough for estimating the layers to put onto GPU). You may also have tweak some other settings so it doesn't flip out. The biggest reason to go Nvidia is not Kobold's speed, but the wider compatibility with the projects. Just set them equal in the loadout. 18 and $0. But luckily for you the post you replied to is 9 months old and a lot happens in 9 months. It's a measure of how much the numbers have been truncated to make it smaller. A phone just doesn't have the computational power. For hypothetical's sake, let's just say 13B Erebus or something for the model. So you will need to reserve a bit more space on the first GPU. 3b models. 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). You can then start to adjust the number of GPU layers you want to use. Check out KoboldCPP. But I assume it is a dead project, because it has not been updated in more than a year, and everybody has moved on to koboldcpp anyway. I have a 8GB 3060Ti, you should be able to input at least 36 I have a 6 core CPU with 12 threads, I set the threads to the number of cores. it shows gpu memory used. I have a 12 GB GPU and I already downloaded and installed Kobold AI on my machine. Beware that you may not be able to put all kobold model layers on the GPU (let the rest go to CPU). First I think that I should tell you my specs. true. The result will look like this: "Model: EleutherAI/gpt-j-6B". Overall AMD are not going anywhere due to their solid CPU sales and the fact that (in the short term at least) they can ride NVIDIA's coat tails into the AI market. Smaller versions of the same model are dumber. 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 . If you do not have Colab Pro, GPU access is given on a first-come first-serve basis, so you might get a popup saying no GPUs are available. I later read a msg in my Command window saying my GPU ran out of space. Welcome to the official subreddit of the PC Master Race / PCMR! All PC-related content is welcome, including build help, tech support, and any doubt one might have about PC ownership. ) Large AI models, such as Pygmalion (and even Character. (Or it can run Kobold but no models. 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. It was a decent bit of effort to set up (maybe 25 mins?) and then takes a decent bit of effort to run (because you have to prompt it in a more specific way, rather than GPT-4 I really want to try kobold but there doesn't seem to be a way to load gguf models. Hi everyone, I'm new to Kobold AI and in general to the AI generated text experience. With minimum depth settings you need somewhat more than 2x your model size in VRAM (so 5. bat . At the bare minimum you will need an Nvidia GPU with 8GB of VRAM. The model is also small enough to run completely on my VRAM, so I want to know how to do this. And likewise we only list models on the GPU edition that the GPU edition can run. sh . 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) Make sure you start Stable diffusion with --api. But, in the long-term, AMDs lack of investment in GPU technology is going to continue to dog the Thanks, I opened a thread the other day on the LLM subreddit (I'm AMD bound, 6750 XT with an I7 4770s) and people did recommend me Kobold, but I didn't quite understand why I wuold have to use Kobold since Kobold to my knowledge was online bound and quite slow. Specs: GTX 3070 with 8 gigs of Vram and 8 gigs of shared memory. I tried Pygmalion-350m and Pygmalion-1. AMD GPU driver install was confusing, this youtube video explains it well "How To Install AMD GPU Drivers In Ubuntu ( AMD Radeon Graphics Drivers For Linux )" by SSTec Tutorials When creating a directory for KoboldAI, do not use "space" in the folder name!!!! I named my folder "AI Talk" and nothing worked, I renamed my folder to "AI-Talk" and I fired it up as remote, loaded the model (have tried both 13B and 6. Intel i7-10700k. If the responses are dry then you can change the model or you can add your own by pressing "show code" (you need to use desktop in order to edit the models). You should see 2 Tabs. There are Multiple possibilities, I will only state the options for Local Kobold AI, but the steps should be similar. Sometimes 1. nvidia-smi -i 1 -c EXCLUSIVE_PROCESS nvidia-smi -i 2 -c EXCLUSIVE_PROCESS. So if you're loading a 6B model which Kobold estimates at ~16GB VRAM used, each of those 32 layers should be around 0. I usually leave 1-2gb free to be on the PCI-e is backwards compatible both ways. I have a RX 6600 XT 8GB GPU, and a 4-core i3-9100F CPU w/16gb sysram Using a 13B model (chronos-hermes-13b. This means you'll need a very high end GPU, but you can still run it on consumer-grade hardware. Start Kobold (United version), and load So if you want GPU accelerated prompt ingestion, you need to add --useclblast command with arguments for id and device. Now we are going to Connect it with Kobold AI. Second batch file: The best bet for a (relatively) cheap card for both AI and gaming is a 12GB 3060. I read that I wouldn't be capable of running the normal versions of Kobold AI with an AMD GPU so I'm using Koboldcpp is this true? There's really no way to use Kobold AI with my specs? 6b model won't fit on an 8gb card unless you do some 8bit stuff. 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. it looks that's not the case. A place to discuss the SillyTavern fork of TavernAI. Disk cache can help sure, but its going to be an incredibly slow experience by comparison. Models seem to generally need (for recommendation) about 2. 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? With your specs I personally wouldn't touch 13B since you don't have the ability to run 6B fully on the GPU and you also lack regular memory. If you want to run the full model with ROCM, you would need a different client and running on Linux, it seems. Hey all, ive been having trouble with setting up Kobold ai the past few days. Start by trying out 32/0/0 gpu/disk/cpu. KoboldCpp allow offloading layers of the model to GPU, either via the GUI launcher or the --gpulayers flags. 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. I don't recommend advertising it unless you're just sharing with a friend, because the horde functions on an already extremely imbalanced ratio of hosters to users, and there haven't been enough hosters ever since it was flooded with gpu-poor people who don't host after character ai, chatgpt, and Claude banned roleplay. I'm mainly interested in Kobold AI, and maybe some Stable Diffusion on the side. Assuming you have an nvidia gpu, you can observe memory use after load completes using the nvidia-smi tool. Yes, Kobold cpp can even split a model between your GPU ram and CPU. 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. 2 t/s) with primary GPU show tiny bits of activity during inference and secondary GPU still showing none. My overall thoughts on kobold are - the writing quality was impressive and made sense in about 90% of messages, 10% required edits. I'd like some pointers on the best models I could run with my GPU. Although even then, everytime I try to continue and type in a prompt, the screen goes grey and i lose connection! Only if you have a low VRAM GPU like an Nvidia XX30 series with 2GB or so. q4_ Man, I didn't realize how used to having access to the TPU I was, I'm literally testing it a couple of times a day to see if it's working again. 6B already is going to give you a speed penalty for having to run part of it on your regular ram. But I have more recently been using Kobold AI with Tavern AI. Sadly my tiny laptop cannot run Kobold AI or I'd do it myself. 6-Chose a model. 3 and up to 6B models, TPU is for 6B and up to 20B models) and paste the path to the model in the "Model" field. Hello. 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? typically, people train a model as a huggingface transformer (do not ask me, I don't know how to set that up) and then convert it to GGUF or their preferred format. 4-After the updates are finished, run the file play. 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 anything whatsoever. That's it, now you can run it the same way you run the KoboldAI models. For webui, you just download to the models folder and they are populated into a model list in the program. Offload 24 layers to GPU. When I try to start a 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 have 32GB RAM, Ryzen 5800x CPU, and 6700 XT GPU. You can try 8. Let's assume this response from the AI is about 107 tokens in a 411 character response. 5-Now we need to set Pygmalion AI up in KoboldAI. I know collab is a thing, but I prefer to keep my stories in a local environment and not online. The context is put in the first available GPU, the model is split evenly across everything you select. Even at $. Then we got the models to run on your CPU. 2 t/s generation which makes me suspicious the CPU is still processing it and using the GPU purely as RAM. 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. If you have a specific Keyboard/Mouse/AnyPart that is doing something strange, include the model number i. Removing all offloading from the secondary GPU resulted in the same 3. You should be seeing Jun 24, 2023 · Using the Easy Launcher, there's some setting names that aren't very intuitive. Speeds are similar as to when your windows runs out of ram, but unlike Windows running out of ram you can keep the rest of your PC speedy, and it can be used on other systems like Linux even if swap is not setup. 5 minutes for a response from one. While the P40 is for AI only. 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. Pygmalion is relatively smaller than the others, so it can run on about 16GB of VRAM normally. The first one does its layers, then transfers the intermediate result to the next one, which continues the calculations. GPU load can be inferred by observing changes in VRAM usage and GPU temperature. Docker has access to the GPUs as I'm running a StableDiffusion container that utilizes the GPU with no issues. But as is usual sometimes the AI is incredible, sometime it misses the plot entirely. 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. The subreddit for all things related to Modded Minecraft for Minecraft Java Edition --- This subreddit was originally created for discussion around the FTB launcher and its modpacks but has since grown to encompass all aspects of modding the Java edition of Minecraft. The Active one Characters and the second one Settings, click on settings. I am not sure if this is potent enough to run koboldAI, as system req are nebulous. I was unaware that support for AI frameworks on AMD cards is basically non-existent if you're running something like KoboldAI on a Windows PC, though. I have 64gb RAM, and with the model loaded I'm now reaching about 57gb RAM used. 30/hr depending on the time of day. The Q4/Q5 etc. 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. For kobold, it seems the model shows up, but there's no way to actually select/load it. it is an option though, while you wait for janitorllm 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. As far as models go, I like Midnight Miqu 70B q4_k_m. If you want performance your only option is an extremely expensive AI card with probably 64 gb vram. Am I maybe using an outdated link to the colab or has this issue still not been… Hey, I'm super new to AI in general but I got recommended the tiefighter model, I've got a RTX 3080TI 12Gig and I've been using the F16 gguf file and it's super slow when generating text. For Erebus 20b, right now I'm using split of I think 16 to the GPU. Q2: Dependency hell So is this possible or am I wasting my time? Do I need to do it via command line to make sure it uses the nvidia gpu and integrated amd gpu (gui won't let you select both or I am unsure how to make this possible, I think at least in command line, using 0 or not specifying a number let's you select both)? Should I be using Vulkan or something else? You can also run a cost benefit analysis on renting gpu time vs buying a loca GPU. You don't get any speed-up over one GPU, but you can run a bigger model. Running it on the 1050's CUDA's made a big impact on useability combined with my RX580. Starting Kobold HTTP Server on port 5001 60 votes, 60 comments. I'm looking into getting a GPU for AI purposes. 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 did all the steps for getting the gpu support but kobold is using my cpu instead. AMD's dumpster fire in the GPU market is going to cripple them in the AI market. kffzqqtmirbdmbbximanzkozcbadwwhgcljlgnsdptrrygy