Koboldcpp multi gpu reddit. When both enabled, 2080 makes barely any difference at all.

Koboldcpp multi gpu reddit You simply select a VM template, then pick a VM to run it on, and put in your card details, and it runs and in the logs you normally get a link to a web UI after it has started (but that mostly depends on what you're running, not on runpod itself; it's true for running KoboldAI -- you'll just get a link to the KoboldAI web app, then you load your model etc. It's like cooking two dishes - having two stoves won't make one dish cook faster, but you can cook both dishes at the same time. I’d love to be able to use koboldccp as the back end for multiple applications a la OpenAI. Just make a batch file, place it in the same folder as your "koboldcpp. Multi or single GPU for stable diffusion . I use 32 GPU layers. My budget allows me to buy a 16Gb GPU (RTX 4060Ti or a Quadro P5000, which is a cheaper option for the 4060Ti) or upgrade my PC to a maximum of 128Gb RAM. org/cpp to obtain koboldcpp. Get the Reddit app Scan this QR code to download the app now. More info: I have a multi GPU setup (Razer Blade with RTX2080 MaxQ) + external RTX 4070 via Razer Core. The only reason to offload is because your GPU does not have enough memory to load the LLM (a llama Settings were the same for both. It basically splits the workload between CPU + ram and GPU + vram, the performance is not great but still better than multi-node inference. 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). But with Kobold United, I don't see such arguments that I can pass for aiserver. Its not overly complex though, you just need to run the convert-hf-to-gguf. Most of the loaders support multi gpu, like llama. Q&A. If you set them equal then it should use all the vram from the GPU and 8GB of ram from the PC. RTX 3070s blowers will likely launch in 1-3 months. 5, I do not know how to check this. 4GB. The bigger/faster the GPU VRAM you have is, the faster the same model will generate a response. ROCm/HIP is AMD's counterpart to Nvidia's CUDA. 0 x16 Lambda's RTX 3090, 3080, and 3070 GPU Workstation Guide. It’s disappointing that few self hosted third party tools utilize its API. 5-2x faster on my work M2 Max 64GB MBP. On your 3060 you can run 13B at full Can you stack multiple P 40s I use koboldcpp and llamafile for vegetative generative text and a1111/webforge for Stevie stable diffusion Anyway full 3d GPU usage is enabled here) koboldcpp CUBLas using only 15 layers (I asked why the chicken cross the road): model: G: Works well for single or multi-gpu inference. 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. e. 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. We focus on education, discussion, and sharing of entry and mid level separate & multi component audio systems. When setting up the model I have tried setting the layers multiple ways and 0/32, all set on GPU seems to work fastest. (New reddit? Click 3 dots at end of this message) Privated to protest Reddit's upcoming API changes. This is self contained distributable powered by 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. Has in-flight batching of requests. You may also have tweak some other settings so it doesn't flip out. Low VRAM option enabled, offloading 27 layers to GPU, batch size 256, smart context off. exe as it doesn't Honestly, I would recommend this with how good koboldcpp is. 42. I'm not using koboldcpp but the PR of llama. 5 quite nicely with the --precession full flag forcing FP32. If using multiple GPUs, it might be cheaper than one with This is currently not possible for two reasons. You could try and use koboldcpp and when running it use openblas. More info: It supports multi-gpu training, plus automatic stable fp16 training. If KoboldCPP crashes or doesn't say anything about "Starting Kobold HTTP Server" then you'll have to figure out what went wrong by visiting the wiki . 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. PCI-e is backwards compatible both ways. The model requires 16GB of Ram. 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. Join us and be the first to discover all the news and find many useful tips! KoboldCpp updated to v1. cpp (a lightweight and fast solution to running 4bit quantized llama models locally). py. With just 8GB VRAM GPU, you can run both a 7B q4 The gpu options seem that you can select only one gpu when using OpenBLAST. cpp, The infographic could use details on multi-GPU arrangements. You can run multiple instances of the script, each running on a different gpu and speed up your processing that way. 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). exe" file, and then run the batch file. In this case, it was always with 9-10 layers, but that's made to fit the context as well. Blower GPU versions are stuck in R & D with thermal issues. shoutout to yellowrosecx :D! it's probably by far the best bet for your card, other than using I gave up after multiple minutes, not GRUB, when using ZFS on host for getting IOMMU groups working (GPU pass thru) upvote r/Proxmox. It's good news that NVLink is not required, because I can't find much online about using Tesla P40's with NVLink connectors. The GP100 GPU is the only Pascal GPU to run FP16 2X faster than FP32. 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. I'm not familiar with that mobo but the CPU PCIe lanes are what is important when running a multi GPU rig. 0), going directly to the CPU, and the third in x4 (PCIe 4. works great for SDXL @echo off echo Enter the number of GPU layers to offload set /p layers= echo Running koboldcpp. Not much more, but still more. 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). py --useclblast 0 0 *** Welcome to KoboldCpp - Version 1. 5 image model at the same time, as a single instance, fully offloaded. However, In the older versions you would accomplish it by putting less layers on the GPU. Take the A5000 vs. As Use a Q3 GGUF quant and offload all layers to GPU for good speed or use higher quants and offload less peripheral branded audio solutions. As well to help those with common tech support issues. With 7 layers offloaded to GPU. Use the regular Koboldcpp version with CLBlast, that one will support your GPU. There are two options: KoboldAI Client: This is the "flagship" client for Kobold AI. Renamed to KoboldCpp. (GPU: rx 7800 xt CPU: Ryzen 5 7600 6 core) Share Controversial. It's a bit wonky if you set DeepSpeed Zero stage 1 or 3. View community ranking In the Top 10% of largest communities on Reddit. As far as I am aware. Each will calculate in series. Note: You can 'split' the model over multiple GPUs. Take off the 20% for overhead, and you're left with 5. Slow though at 2t/sec. The (un)official home of #teampixel and the #madebygoogle lineup on Reddit. cpp. 8t/s. However, it should be noted this is largely due to DX12/Vulcan fucking driver level features up the ass by forcing multi gpu support to be implemented by the application. Q2: Dependency hell Well, exllama is 2X faster than llama. q8_0. Question Hi there, I am a medical student conducting some computer vision research and so forgive me if I am a bit off on the technical details. But with GGML, that would be 33B. And of course Koboldcpp is open source, and has a useful API as well as OpenAI Emulation. henk717 • Needs more info like the model you are using and the version of Koboldcpp you are using. 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). Considering that the person who did the OpenCL implementation has moved onto Vulkan and has said that the future is Vulkan, I don't think clblast will ever have multi-gpu support. r/Proxmox. A place dedicated to discuss Acer-related news, rumors and posts. Just today, a user made the My setup: KoboldCPP, 22 layers offloaded, 8192 context length, MMQ and Context Shifting on. The expensive part is the GPU. 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). Also, mind you, SLI won't help because it uses frame rendering sharing instead of expanding the bandwidth 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. Or check it out in the app stores Dell R730 Multi-GPU Config? Solved > start_windows. It's a multi-GPU front-end for the Auto1111 SD repo. it almost never lags behind the upstream kcpp for more than a few days too, which is nice. I tried to Battlefield 4's technical director about possible use of Mantle API: "low-latency multi-GPU rendering for VR". During prompt processing the primary GPU is utilized as expected but once token generation begins both GPU's show 0% utilization In comparison I tried llama 3 8B which will fit entirely on one GPU. So technically yes, NvLink, NvSwitch potentially could speedup workload. However, in reality, koboldcpp is using up the entire 64GB RAM and 24GB VRAM, leading to program crashes due to insufficient RAM. As for whether to buy what system keep in mind the product release cycle. Or check it out in the app stores KoboldCPP ROCM is your friend here. Works with awq quantization. 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 of patience waiting for It may be interesting to anyone running models across 2 3090s that in llama. Works pretty well for me but my machine is at its limits. Whether you buy it, or rent it on a cloud, it's expensive. With the model loaded and at 4k, look at how much Dedicated GPU memory is used and Shared GPU memory is used. 0) going through the Chipset. Reply reply /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. Question - Help Hello everyone, /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. There is a way to specify gpu number to use and port number. 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? Koboldcpp is better suited for him than LM Studio, performance will be the same or better if configured properly. The Researcher Seeking Guidance on Multi-GPU setup + Parallelization . However, the launcher for KoboldCPP and the Kobold United client should have an obvious HELP button to bring the user to this resource. Share Add a Comment. Right now this is my KoboldCPP launch instructions. ) using Vulkan. 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. cpp even when both are GPU-only. If you rent it on the cloud, it's less expensive in the short term, more expensive in the long term. I would try exllama first, it can run 65B parameter model in 40 to 45 gigabyte of vram on two GPUs. Not even from the same brand. I set my GPU layers to max (I believe it was 30 layers). To actually use multiple gpu's for training you need to use accelerate scripts manually and do things without a UI. 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. The following is the command I run. py . With just 8GB VRAM GPU, you can run both a 7B q4 GGUF (lowvram) alongside any SD1. The last time I looked, the OpenCL implementation of llama. 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. Now, I've expanded it to support more models and formats. With the model I was using I could fit 35 out of 40 layers in using CUDA. 24, Supports accelerated prompt processing GPU offloading via CLBlast (LLAMA only). With koboldcpp I can run this 30B model with 32 GB system RAM and a 3080 10 GB VRAM at an average around 0. If you run the same layers, but increase context, you will bottleneck the GPU. 1 x PCIe 4. Reply reply Welcome to /r/AcerOfficial, Reddit's biggest acer related sub. 1 was this also /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app Posted by u/amdgptq - 29 votes and 7 comments 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. However, that KoboldCpp allow offloading layers of the model to GPU, either via the GUI launcher or the --gpulayers flags. If your model fits a single card, then running on multiple will only give a slight boost, the real benefit is in larger models. 6, I am trying to run the Nerys FSD 2. cpp didn't support multi-gpu. bat --model <modelhere> --gpu-split 20,24 And with KoboldCPP, that same type of solution can also be done as well for koboldcpp. Only the CUDA implementation does. Your best option for even bigger models is probably offloading with llama. Remember that the 13B is a reference to the number of parameters, not the file size. And this is using LMStudio. 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. The model appeared to still load fully in system RAM and VRAM, but during token generation the GPU did show activity and generation was many times faster than pure CPU as Locate the GPU Layers option and make sure to note down the number that KoboldCPP selected for you, we will be adjusting it in a moment. 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. Hope that helps And that's just the hardware. To clarify: Cuda is the GPU acceleration framework from Nvidia specifically for Nvidia GPUs. On the software side, you have the backend overhead, code efficiency, how well it groups the layers (don't want layer 1 on gpu 0 feeding data to layer 2 on gpu 1, then fed back to either layer 1 or 3 on gpu 0), data compression if any, etc. the 3090. I'm looking to install one or two GPU's with both a Nvidia GTX 780 and a Nvidia GTX 1080 standing Get the Reddit app Scan this QR code to download the app now. At least with AMD there is a problem, that the cards dont like when you mix CPU and Chipset pcie lanes, but this is only a problem with 3 cards. Keeping that in mind, the 13B file is almost certainly too large. 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. So if you have an AMD GPU, you need to go with ROCm, if you have an Nvidia Gpu, go with CUDA. 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. This also means you can use much larger model: with 12GB VRAM, 13B is a reasonable limit for GPTQ. Also can you scale things with multiple GPUs? 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'. If we list it as needing 16GB for example, this means you can probably fill two 8GB GPU's evenly. If EXLlama let's you define a memory/layer limit on the gpu, I'd be interested on which is faster between it and GGML on llama. But whenever I plug the 3rd gpu in, the PC won't even boot, thus can't access the BIOS either. 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. 8tokens/s for a 33B-guanaco. Make a note of what your shared memory is at. But it kinda suck at writing a novel. I would suggest to use one of the available Gradio WebUIs. exe with %layers% GPU layers koboldcpp. And kohya implements some of Accelerate. For system ram, you can use some sort of process viewer, like top or the windows system monitor. There are fewer multi-gpu systems because of the lack of support in games and game developers don't put in the effort for multi-gpu support because of the lack of multi-gpu users. Yesterday I even got Mixtral 8x7b Q2_K_M to run on such a machine. I later read a msg in my Command window saying my GPU ran out of space. Open the performance tab -> GPU and look at the graph at the very bottom, called "Shared GPU memory usage". This is self contained distributable powered by There's a new, special version of koboldcpp that supports GPU acceleration on NVIDIA GPUs. Trying to figure out what is the best way to run AI locally. I mostly use koboldcpp. The context is put in the first available GPU, the model is split evenly across everything you select. Still, speed (which means the ability to make actual use of larger models that way) is my main concern. So on linux its a handful of commands and you have your own manual conversion. So while this model indeed has 60 layers, to also offload everything else 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. Just set them equal in the loadout. 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) Multi GPU works with all quantization types unless there is a bug somewhere. 48GB. Limited to 4 threads for fairness to the 6-core CPU, and 21/41 layers offloaded to GPU resulting in ~4GB VRAM used. It In that case, you could be looking at around 45 seconds for a response of 100 tokens. Accelerate is. What happens is one half of the 'layers' is on GPU 0, and the other half is on GPU 1. 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. Also, with CPU rendering enabled, it renders much slower than on 4070 alone. If you want to run the full model with ROCM, you would need a different client and running on Linux, it seems. py --listen --chat --wbits 4 --groupsize 128. 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 koboldcpp-rocm works flawlessly for my RX6800 (non-xt) on windows. So if you want multi GPU, amd is a better option if your hearts set on it, there are games still despite what people say that get multi GPU support, two 6800xt's double a 3090's 4k framerates in rise of the tomb raider with raytracing and no upscaling. 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. 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 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. Each GPU does its own calculations. 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. 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. To determine if you have too many layers on Win 11, use Task Manager (Ctrl+Alt+Esc). My cpu is at 100%. Sort by: Best. 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. At no point at time the graph should show anything. That depends on the software, and even then, it can be iffy. Or check it out in the app stores Keep in mind that there is some multi gpu overhead, Therefore, I thought my computer could handle it. , it's using GPU for analysis, but not for generating output. ). This sort of thing is important. It's 1. And GPU+CPU will always be slower than GPU-only. /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. There must be enough space for KV cache, and cuda buffers. When attempting to run a 70B model with a CPU (64GB RAM) and GPU (22GB), the runtime speed is approximately 0. Since early august 2023, a line of code posed problem for me in the ggml-cuda. I do have a few questions, though: 1) How can I speed up text generation? I'm using an Intel Does the official KoboldCpp template support multiple GPUs in RunPod? I want to try the KoboldCpp template on RunPod. I think mine is set to 16 GPU and 16 Disk. The reason of speed degradation is low PCI-E speed, I believe. KoboldCpp-ROCm is an easy-to-use AI text-generation software for GGML and GGUF models. Do you think it's feasible and worthwhile to look at MULTI-GPU with your repo as a I went with a 3090 over 4080 Super because the price difference was not very big, considering it gets you +50% VRAM. This is a good 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. Use llama. Assuming you have an nvidia gpu, you can observe memory use after load completes using the nvidia-smi tool. true. Welcome to 4K Download Reddit! 4K Download software is the most effective tool for downloading content from YouTube, Vimeo, Twitch, Instagram and other popular sites. Aphrodite-engine v0. Lambda is working closely with OEMs, but RTX 3090 and 3080 blowers may not be possible. My original idea was to go with Threadripper 3960x and 4x Titan RTX, but 1) NVidia released RTX 3090, and 2) I stumbled upon this ASRock motherboard with 7 PCIe 4. I'm looking to build a new multi-gpu 3090 workstation for deep learning. A 20B model on a 6GB GPU you could be waiting a couple of minutes for a response. But as Bangkok commented you shouldn't be using this version since its way more VRAM hungry than Koboldcpp. It seems like a MAC STUDIO with an M2 processor and lots of RAM may be the easiest way. 0 x16 slots. cpp, exllamav2. 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. I want to run bigger models but i don't know if i should get another GPU or upgrade my RAM. Using multiple GPUs works by spreading the neural network layers across the GPUs. As far as I now RTX 3-series and Tensor Core GPUs (A-series) only. . So forth. I did all the steps for getting the gpu support but kobold is using my cpu instead. 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. 81% of that is 6. Its at the high context where Koboldcpp should easily win due to its superior handling of context shifting. Even lowering the number of GPU layers (which then splits it between GPU VRAM and system RAM) slows it down tremendously. You don't get any speed-up over one GPU, but you can run a bigger model. When not selecting a specific GPU ID after --usecublas (or selecting "All" in the GUI), weights will be distributed across all detected Nvidia Zero install, portable, lightweight and hassle free image generation directly from KoboldCpp, without installing multi-GBs worth of ComfyUi, A1111, Fooocus or others. bin. 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. 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). I've switched from oobabooga's text-generation-webui to koboldcpp because it was easier, faster and more stable for me, and I've been recommending it ever since. 1 For command line arguments, please refer to --help *** Warning: CLBlast library file not found. 1]$ python3 koboldcpp. I can get ~600-1000 tokens/sec throughput with enough batches running to saturate using a 13b llama2 model and 2x 3090s. 12 votes, 12 comments. A beefy modern computer with high-end RAM, CPU, etc. But if you set DeepSpeed Zero stage 2 and train it, it works well. 0 x16 SafeSlot (x16) [CPU] 1 x PCIe 3. The large models are loaded in the VRAM of the GPU to do inference, so you can't run them locally unless you have a GPU that can handle them. So you will need to reserve a bit more space on the first GPU. ggmlv3. None of the backends that support multiple GPU vendors such as CLBlast also support multiple GPU's at once. Click Performance tab, and select GPU on the left (scroll down, might be hidden at the bottom). Single node, multiple GPUs. So clearly there's a /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 is it possible to utilise multi gpu’s when working with tools like roop and Stable diffusion? I7-3770 P8Z77-WS 32GB DDR3 on 1600MHz 1000W PSU 1TB SSD Hey, thanks for all your work on koboldcpp. Don't fill the gpu completely because inference will run out of memory. I have a 4070 and i5 13600. Press Launch and keep your fingers crossed. Some say mixing the two will cause generation to be significantly slower if even one layer isn’t offloaded to gpu. Yet a good NVIDIA GPU is much faster? Then going with Intel + NVIDIA seems like an upgradeable path, while with a mac your lock. 8 T/s with a context size of 3072. Newer GPU's do not have this limitation. Using OpenCL I When I started KoboldCPP, it showed "35" in thread section. cpp/koboldcpp there's a performance increase if your two GPUs support peering with one another (check with nvidia-smi topo -p2p r) - it wasn't working with my particular motherboard, so I installed an nvlink bridge and got a performance bump in token generation (an extra 10-20% with 70b, more with The above command puts koboldcpp into streaming mode, allocates 10 CPU threads (the default is half of however many is available at launch), unbans any tokens, uses Smart context (doesn't send a block of 8192 tokens if not needed), sets the context size to 8192, then loads as many layers as possible on to your GPU, and offloads anything else onto your CPU and system ram. koboldcpp - multiple generations? in the original KoboldAI, there was an option to generate multiple continuations/responses and to be able to pick one. (Note, I went in a wonky order writing the below comment - I wrote a thorough reply first, then wrote the appended new docs guide page, then went back and tweaked my initial message a bit, but mostly it was written before the new docs were, so half of the comment is basically irrelevant now as its addressed better by the new guide in the docs) Even with full GPU offloading in llama. So I am not sure if it's just that all the normal Windows GPUs are this slow for inference and training (I have RTX 3070 on my Windows gaming PC and I see the same slow performance as yourself), but if that's the case, it makes a ton of sense in getting Multi-GPU from DX12 requires explicit support from the game itself in order to function, and cannot be forced like SLI/Xfire. Is there any way to use dual gpus with OpenCL? I have tried it with a single AMD card and two I've just started using KoboldCPP and it's amazing. Anyways, currently pretty much the only way SLI can work in a VR game is if it 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. If you're on Windows, I'd try this: right click taskbar and open task manager. It kicks-in for prompt-generation too. A reddit dedicated to the profession of Computer System Administration. The 3070 has only 8GB VRAM. Also, although exllamav2 is the fastest for single gpu or 2, Aphrodite is the fastest for multiple gpus. That means at least a 3090 24gb. Get support, learn new information, and hang out in the subreddit dedicated to Pixel I’ve been using TheBloke’s text-generation-web UI template and in general I’m super happy with it, but for running mixtral, it would be significantly cheaper to pick a system with a smaller GPU and only partially offload layers, and based on my research it seems like I’d be happy with the generation speeds. As the others have said, don't use the disk cache because of how slow it is. With just 8GB VRAM GPU, you can run both a 7B q4 GGUF Zero install, portable, lightweight and hassle free image generation directly from KoboldCpp, without installing multi-GBs worth of ComfyUi, A1111, Fooocus or others. It wasn't really a lie but it's something the developers themselves have to implement and that takes time and resources. I usually leave 1-2gb free to be on the 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. Seems to be a koboldcpp specific implementation but, logically speaking, CUDA is not supposed to be used if layers are not loaded into VRAM. It's quite amazing to see how fast the responses are. cpp with OpenCL support. 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. To clarify, Kohya SS isn't letting you set multi-GPU. 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? When the KoboldCPP GUI appears, make sure to select "Use hipBLAS (ROCm)" and set GPU layers. Enjoy zero install, portable, lightweight and hassle free image generation directly from KoboldCpp, without installing multi-GBs worth of ComfyUi, A1111, Fooocus or others. Using koboldcpp: Model used for testing is Chronos-Hermes 13B v2, Q4_K_M GGML. I. Over time, I've had several people call me everything from flat out wrong to an idiot to a liar, saying they get all sorts of numbers that are far better than what I have posted above. When both enabled, 2080 makes barely any difference at all. On Faraday, it operates efficiently without fully utilizing the hardware's power yet still responds to my chat very quickly. 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. 5. I try to leave a bit of headroom but When it comes to rendering, using multiple GPUs won't make the process faster for a single image. that's sad, now I have to go buy an eGPU enclosure to put the 3rd GPU in, hope it works this time koboldcpp is your friend. another idea i had was looking for a case with vertical gpu mounting and buying pcie extensions/raisers but idk a lot about that pcie specs of my mobo are: Multi-GPU CFX Support. I tried changing NUMA Group Size Optimization from "clustered" to "Flat", the behavior of KoboldCPP didn't change. If the software you're using can use multiple GPUs then you could get another 3070 and put it in an x16 slot, sure. Koboldcpp behavior change in latest more vram per layer but as a result you now have the benefit of proper acceleration for those layers that are on the GPU. it shows gpu memory used. Now that koboldcpp has GPU acceleration which has increased generation speed by 40 so close to GPU, but 40 t/s on the 30B is something else for sure. OpenCL is not detecting my GPU on koboldcpp . Or check it out in the app stores # Run Web UI # With GPU python server. It's an AI inference software from Concedo, maintained for AMD GPUs using ROCm by YellowRose, that builds off llama. Or check it out in the app stores Exploring Local Multi-GPU Setup for AI: Harnessing AMD Radeon RX 580 8GB for Efficient AI 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 We would like to show you a description here but the site won’t allow us. exe (or koboldcpp_nocuda. Adding an idle GPU to the setup, resulting in CPU (64GB RAM) + GPU (22GB) + GPU (8GB), properly distributed the workload across both GPUs. More info: 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. exe --useclblast 0 0 --gpulayers %layers% --stream --smartcontext pause --nul. Both are based on the GA102 chip. And the one backend that That is because AMD has no ROCm support for your GPU in Windows, you can use https://koboldai. SLI depends on GPU support and the 3070 does not support it. I heard it is possible to run two gpus of different brand (AMD+NVIDIA for ex. I'm reasonably comfortable building PCs and DIY, but server stuff is a bit new and I'm worried I'm missing something obvious, hence The current setup available only uses one gpu. I have a RTX 3070Ti + GTX 1070Ti + 24Gb Ram. Open comment sort /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt Zero install, portable, lightweight and hassle free image generation directly from KoboldCpp, without installing multi-GBs worth of ComfyUi, A1111, Fooocus or others. If it works that way (multiple devices used at the same time in parallel; CPU & GPU), then it should give more processing power. This is why a 1080ti GPU (GP104) runs Stable Diffusion 1. Do not use main KoboldAi, /How to offload a model onto multiple gpu's without using system ram? Get the Reddit app Scan this QR code to download the app now. Now start generating. 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 Multi-GPU is only available when using CuBLAS. You will have to ask other people for clients that I don't use. 7B Hybrid adventure model. So OP might be able to try that. My recommendation is to have a single, quality card. For immediate help and problem solving, Yes, using a slower GPU may actually result in a lower average speed. A while back, I made two posts about my M2 Ultra Mac Studio's inference speeds: one without cacheing and one using cacheing and context shifting via Koboldcpp. But you would probably get better results by getting a 40-series GPU instead. you can do a partial/full off load to your GPU using openCL, I'm using an RX6600XT on PCIe 3. But there is only few card models are currently supported. However, during the next step of token generation, while it isn't slow, the GPU use drops to zero. It runs pretty fast with ROCM. Use cases: Great all-around model! Best I've used for group chats since it keeps the personalities of each character distinct (might also be because of the ChatML prompt template used here). If you're running windows, that gobbles up 19% of your available VRAM on your display adapter (the GPU(s) that run(s) your monitor(s)) as well. does this exist in koboldcpp? i can't seem to find it in Settings. Given SLI/Xfire were a solution to the problem of underpowered GPUs, which is no longer a problem in the current market, it would be pointless for companies to spend time (and thus money) for developers to include support for a solution to a problem that I was picking one of the built-in Kobold AI's, Erebus 30b. My PC specs are: Ram: 32 GB, 1067 MHz, 4 sticks, all four slots. (newer motherboard with old GPU or newer GPU with older board) Your PCI-e speed on the motherboard won't affect koboldAI run speed. cpp, it takes a short while (around 5 seconds for me) to reprocess the entire prompt (old koboldcpp) or ~2500 tokens (Ooba) at 4K context. Can't help you with implementation details of koboldcpp, sorry. 4x GPUs workstations: 4x RTX 3090/3080 is not practical. 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). 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 Koboldcpp is so straightforward and easy to use, plus it’s often the only way to run LLMs on some machines. With accelerate, I found that you don't need to code boilerplate code. The more batches processed, the more VRAM allocated to each batch, which led to early OOM, especially on small batches supposed to save. on a 6800 XT. cpp with gpu layers amounting the same vram. I find the tensor parallel performance of Aphrodite is amazing and definitely worthy trying for everyone with multiple GPUs. Old. Or check it out in the app stores These days you want to use the offline installer for KoboldAI United or Koboldcpp. However, the speed remains unchanged at 0. In Task Manager I see that most of GPU's VRAM is occupied, and GPU utilization is 40-60%. How to reduce ram usage (NOT vram)/How to offload a model onto multiple gpu's without using system ram? Question | Help Good day everyone, I recently bought a second Tesla P40 for my 3090+P40 system (combined 72GB of vram) and have been running into a funny issue with ooba where I still have enough vram for the model and more context but my system ram (64GB) is You'll need to split the computation between CPU and GPU, and that's an option with GGML. 0 brings many new features, among them is GGUF support. My own efforts in trying to use multi-GPU with KoboldCPP didn't work out, despite supposedly having support. Using koboldcpp with cuBLAS btw. Now with this feature, it just processes around 25 tokens instead, providing instant(!) replies. The bigger the model, the more 'intelligent' it will seem. More info: I've found the exact opposite. Not having the entire model on vram is a must for me as the idea is to run multiple models and have control over how much memory they can take. CPU: AMD Ryzen 5 2600 Six-Core GPU: AMD Radeon RX 5700 XT There is a fork out there that enables multi-GPU to be used. I think I had to up my token length and reduce the WI depth to get it 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. This can be partially mitigated by setting the "main gpu" which is the GPU number that is passed in with --usecublas which will be used to store KV, then manually setting --tensor_split to allocate layers onto the secondary GPU. I can put more layers into the GPU with OpenCL than with CUDA. 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. 62. cu of KoboldCPP, which caused an incremental hog when Cublas was processing batches in the prompt. I'd probably be getting more tokens per second if I weren't bottlenecked by the PCIe slot so The new part is that they've brought forward multi-GPU inference algorithm that is actually faster than a single card, and that its possible to create the same coherent image across multiple GPUs as would have been created on a single GPU while being faster at generation. If its 1. Mine is the same, x8/x8 (PCIe 5. iqrkoo jndp ebaydta kqunek vpwwc efps dckbzm tovuqq crix vnfocr