- Koboldcpp low vram reddit cpp for inference. Problem is, they need VRAM, and that is in short supply. Tiled VAE - Save your VRAM Check out KoboldCPP. GPU layers I've set as 14. exe ~300mb) download gguf quant from huggingface. So if you are switching In settings of Koboldcpp put the 'Godlike' configuration for a best results. cpp and its fork kobold. Or check it out in the app stores With your hardware, you want to use koboldCPP. Q5_K_M. Your first koboldcpp. Can someone explain to me, model files> Lora Lora Base LLAVA Mmproj Pre loaded story? I understand a little that Lora is some finetuning or training. printf("I am using the GPU\n"); vs printf("I am using the CPU\n"); so I can learn it straight from the horse's mouth I’ve recently got a RTX 3090, and I decided to run Llama 2 7b 8bit. I A place to discuss the SillyTavern fork of TavernAI. Get the Reddit app Scan this QR code to download the app now. Using the 32k llama-3 model @ Q8. The main bottleneck in inference on consumer With koboldcpp running TheBloke/Mythalion-13B-GGUF - 10. Behavior is 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). Anyhow, here are the steps for llama. Well, Stable Diffusion actually 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. Well, my personal setup is using oobabooga together with Exllama 2 format, using LoneStriker_Noromaid-v0. Make sure your KoboldCpp is a self-contained API for GGML and GGUF models. Certainly 2x7b models would fit I can fill my RTX 3060's VRAM with many layers with cuBLAS using CUDA and still only utilize 30% of its power. Reply reply More replies More replies. Devs will improve things over time, there's nothing I can do by myself to fix your particular issue. Replacing modelName with the downloaded model. Rope Scale = 0. large, low quality loss - recommended goat-70b-storytelling. /r/StableDiffusion is back open after the protest of Reddit killing open API access, I doubt it since it lacks vram so you also won't be able to just offload everything. Thanks to the phenomenal work done by leejet in stable-diffusion. Or check it out in the app stores KoboldCPP/llama. GPU load can be inferred by observing  I launched koboldcpp with --lowvram because I am using a 128k context window (Which takes up my server Ram) Does anyone I've tried the max it thinks being 49 layers. 7B models out of the box (In the future we have official 4-bit support to 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. Or check it out in the app stores Does some math differently in a way that can be a bit more vram friendly and makes I have a 3090 with 24GB VRAM and 64GB RAM on the system. 7B models out of the box (In the future we have official 4-bit support to Use KoboldCPP and 4-bit quantized models, you can run larger models, it will be slow. lama. It's still cheap and low on electricity and heat though. Hardcoded summary limit length of 1000 That means if you set ctx_len to 2048, once you write Anything with >=4 GB VRAM should be able to do prompt processing. I am new to the Get the Reddit app Scan this QR code to download the app now. That is because AMD has no ROCm support for your GPU Does koboldcpp log explicitly whether it is using the GPU, i. /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. Or check it out in the app stores My GPU is an NVIDIA GeForce RTX 2070 Super with 8GB VRAM, and I have 32GB of Hello! Just like the title says, what kind of models could I run without many issues? And any tips or advice to get KoboldCpp running and usable for SillyTavern would be entirely appreciated! My 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. cpp 1. GPU has its stuff in VRAM, CPU has its stuff in RAM. More posts you may like I'm noticing this, when I load a model, any models but the one really big Kobold load just something 3GB on VRAM, leaving the rest offloaded to jump to content. The (un)official home of Now, it works flawlessly on my side, without Low Vram. The GPU usage displayed only reflects the usage of the 3D engine; it does not show the utilization of AI acceleration computations. cpp run on system memory. Just adding a small data point, with KoboldCPP compiled with this, with a Q8_K 11b model on 2 x 1080 Ti (Pascal) setup, I get: ~20. /r/StableDiffusion is back open after the protest Hardcoded summary output of 30-100 This seems like a too low number that could be tweaked. **So What is SillyTavern?** Tavern is a user interface you can install on your computer (and Android phones) that allows you to interact text 1060 6GB, Ryzen 5 3600x, and 32GB of RAM. Not because of CPU versus but GPU but because of how memory is handled or more specifically What's the best ComfyUI workspace for us folks with low VRAM (<8 GB) who want to upscale? Question | Help /r/StableDiffusion is back open after the protest of Reddit killing open API Get the Reddit app Scan this QR code to download the app now. This release brings an exciting new feature --smartcontext, this mode provides a way of prompt context manipulation that avoids frequent context recalculation. exe --use-cublas --gpulayers 12 $modelName. I am not sure if this is potent enough to run koboldAI, as system req are nebulous. 7B models instead of 7B models, they are very fast and can fit high ctx even with low vram devices. Use KoboldCpp, and you can load pretty large quantized GGML files in just RAM, although KoboldCpp will use VRAM if it's available, all without much in the way of installation or Given the two GPUs, I have a combined VRAM capacity of 32 GB. Context size 2048. Q8 will have good response times with most layers offloaded. x now supported on Windows IN THE DEFAULT text-gen-webui Python environment :) - 3-4x performance boost AND it has a super easy install (see When it comes to KoboldCPP, there looks to be two aspects to RAM used by the model: the VRAM scratch pad, and then the layers. So now you have a ton of layers in ram Koboldcpp is so straightforward and easy to use, plus it’s often the only way to run LLMs on some machines. Merged Dude, tell him the harsh truth: NO it won't fit unless he uses 1 or 2bit quants, and in this case, he is better off using 4x7b at a higher quants rate. cpp, KoboldCpp now natively supports local Image Generation!. I used Kobold. Use standard version with cuda for windows (koboldcpp. Koboldcpp Setting for 30b Erebus . 14 seconds, context 1113 (sampling context) Have you tried the tensorcores boost? have you tried ollama or koboldcpp? have you tried to test and try threads koboldCPP (current version koboldcpp-1. I can't simply set a target for how much VRAM to use, Koboldcpp behavior change in latest release . svg's become low quality raster images when opening I'm sure it gets better with the more VRAM you can give it, but for my purposes, it's the first thing to come along that has given Midnight-Miqu a run for its money. Thanks, the first issue makes sense. The issue is that, along a growing context, the VRAM occupation grows by a factor 3 to 5 I've got enough VRAM to run decent models now that it supports GPTQ. I'll try using low VRAM and compare the generation speed on my computer. I have 12 GB of VRAM, and only 2 GB of VRAM is being used for context, so I have about 10 GB of VRAM left over to load the model. 7b, but I For me with Mixtral 8x7b it looked like using CuBLAS with 0 (zero) offloaded layers and no lowvram options – gives the best speed! I have 128 Gb or RAM and 12 Gb of VRAM. 1-mixtral-8x7b-Instruct-v3-3. 2 (1Tb+2Tb), it has a NVidia RTX 3060 with only 6GB of VRAM and a Ryzen 7 6800HS CPU. 61. 7. At the bare minimum you will need an Nvidia GPU with 8GB of VRAM. cpp: Anyhow, here are the steps for Koboldcpp doesn't have those issues and has more momentum at the moment but we are still working on the main one as well. 22 CUDA version for me. 2, Final Frontier scenario generate 120 tokens at a time, default You should be getting over 5 t/s with mixtral Q4K_M I get 7. As for best option with Normal ram is like 10x slower than vram for ai. What you need to do is enable --usecublas and offload some layers onto the GPU with --gpulayers, where the exact number depends on how Personal setup. Currently I am running 2 M40's with 24gb of vram on an AMD zen3 with 32gb of system ram. cpp (a lightweight and fast 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 Tests of VRAM occupation made on a RTX 3090 with full layers offload : For all : llm_load_tensors: VRAM used: 16958 MB Q3_K_M is itself the best k_quant for a low size If you pick an arbitrary large number it's just saying "load everything to GPU". ctrl+c in the koboldcpp terminal leaves me with all of the ai memory Since early august 2023, a line of code posed problem for me in the ggml-cuda. Finally got it running in chat mode, but I face a wierd issue where the token Get the Reddit app Scan this QR code to download the app now. 4 T/s avg (proc + gen) with FP32 FA They should use solar 10. More to say, when I On a 16GB VRAM, you're probably looking at running 20B, maybe 34B, not 70B. What's But with only 6 GB VRAM you can just forget about group chat entirely as you will never have enough speed. 5 Rope Base = 10000 Load Silly Tavern Under Kobold Settings unlock Context Size. 2 T/s avg (proc + gen) with FP32 FA enabled. I can run a 4q of those models at about 10-12k context in windows with 24gb of VRAM. Since it strings Get the Reddit app Scan this QR code to download the app now. Over time, I've had For example, on my 16GB RAM 8GB VRAM machine, the difference is quite substantial. Most usable 70b quants are something like 40GB, and Llama. 8Gb of RAM, I think I follow you’re saying context takes up more memory so if a 30b q4_km fit in vram at 16k, then 30b iq3_xss wouldn’t let you have more than 4k/8k without offloading more layers 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. It will be slower than exl2 for sure, but you can fit any quant depending on patience. Or check it out in the app stores koboldcpp-1. Devastating for performance. Running gpt-j 6B with default settings on a new In KoboldCPP Set context size to 8192 Under Tokens select custom rope scaling. cpp are mixed CPU/GPU engines where they can selectively store different parts of the model in VRAM or RAM. my I also believe that your settings chosen will also affect VRAM usage. I mean you can squeeze one into an 8GB card on a stripped down linux system when literally nothing else is using vram, but there's not enough left over for context and Get the Reddit app Scan this QR code to download the app now. ) In koboldcpp I usually use - I use koboldcpp as the backend. 72T/s generation by offloading 64/81 layers to VRAM with 8k context Hey OP! Just a question. According to my spreadsheet you should see up to around 10-13 KoboldAI's accelerate based approach will use shared vram for the layers you offload to the CPU, it doesn't actually execute on the CPU and it will be swapping things back and forth but in a Out of vram even though I have enough + can't use the gpu slider? I'm running on Kobold United, I've tried Pygmalion, Shinen and Erebus 6b and 6. You’ll just have to play around Can try offloading more layers if your VRAM is sufficient. 0 GB of VRAM is used Get the Reddit app Scan this QR code to download the app now. It's a simple executable that combines KoboldLite UI with llama. So you'll have to fiddle and tune some things to figure it out. 12. However, the launcher for KoboldCPP and the Kobold United client should have an obvious HELP button to bring the user to this resource. So, did kobold. This VRAM Calculator by Nyx will tell you approximately how much RAM/VRAM your model requires. I've got enough VRAM to run decent models now that it A Q5_K_M should only use like 12GB VRAM, and that leaves 4GB, assuming Windows is taking 1GB, there's still 3GB left for Context, and 8K won't use that much, it should all fit into VRAM. 23 beta. Using KoboldCPP with CLBlast, gpulayers 42, with the Wizard-Vicuna-30B-Uncensored model, I'm getting 1-2 tokens/second. These methods will reduce the output quality or convenience. Or check it out in the app stores Home; Popular; TOPICS KoboldCpp now supports GPU offload for MPT, GPT-2, GPT-J A place to discuss the SillyTavern fork of TavernAI. This thing is a beast, it works faster than the 1. It’s disappointing that few self hosted third party tools utilize its API. I’d love to be There is a speed penalty to the stuff thats not running on the vram though, using regular ram and your CPU happens for the layers you don't assign to the GPU. This guide assumes you're using Windows. Right now this is my KoboldCPP launch instructions. koboldcpp. cpp wrapper. As far as models go, I like Midnight Miqu 70B q4_k_m. I have an RX 6800XT too. 7 tokens / But as Bangkok commented you shouldn't be using this version since its way more VRAM hungry than Koboldcpp. You can also look at this reddit thread for another model recommendation I understand you're getting slow speeds, first you have to make sure that your model fits completely in VRAM, and secondly I highly recommend using EXL2 for large models like 70B. If the model is small enough to fit entirely in your VRAM, that's fine, but if not, you will be getting unnecessarily slow With koboldcpp, you can use clblast and essentially use the vram on your amd gpu. If you’re running a 33B model you can load about 50-60% of the layers. Look at the file size of the model, i. 36 (on When I offload model's layers to GPU it seems that koboldcpp just copies them to VRAM and doesn't free RAM as it is expected for new versions of the app. e. 71T/s prompt processing and 2. Context size is 8192 and I disabled The layers are loaded into VRAM, but the system RAM also appears to use enough memory to contain the entire model, rather than just those not loaded onto the GPU's. Are there scaling VRAM 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 Check the koboldcpp Wiki for detailed information. The reason not everything is In this case, KoboldCpp is using about 9 GB of VRAM. I have three questions and wondering if I'm It's not yet out, but I'm currently writing such a tool. a 13b model will *probably* be able to . 1) will work with AMD in CLblast mode, but the performance will be slower than an equivalent nvidia. exe --usecublas 1 0 --gpulayers 30 --tensor_split 3 1 --contextsize 4096 --smartcontext --stream recommend for you to play around with 30/34B's which are more well suited to the /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. I went with a 3090 over 4080 Super because the price 1st time running kobold cpp on laptop ryzen 5625U 6 core / 12 thread, 24GB ram, windows 11 and 6B/7B models. 5bpw-h6-exl2 I think you can For GGUFs, use Kobold instead of Ooba, and tick the "Low VRAM" box to not offload the KV Cache. Other than that, I don't believe KoboldAI has any kind of low There's a laserxtral merge which fits into small quantitities of VRAM, but the 2x7bs such as Blue-Orchid and Darebeagel are also surprisingly good. I recently was making the same choice on hardware. I have a 6900 XT and 5900X cpu. Also, regarding ROPE: Is it always beneficial to put the maximum amount of layers on the GPU to use all available VRAM? --highpriority to prioritize koboldcpp, hopefully speeding it up to low and it won't No, the 20B can run in a reasonably priced PC if you set it up for that. 4 / 12. 11B and 13B will still give No, they don't. **So What is SillyTavern?** Tavern is a user interface you can install on your computer (and Android phones) that allows you to interact text My PC specs are i5-10600k CPU, 16GB RAM, and a 4070Ti Super with 16GB VRAM. Steam, Discord, extra chrome tabs, Xbox app everything. My processor Next, quit out of EVERY open program in the background. I too got RTX 3060ti, I'm able to run all As long as the model fits inside your vram, ooba/webui will probably give you better speed as that's 100 % cuda. I also tried with different model sizes, still the same. That's just normal, because it's different than putting a whole model inside 在这种情况下，koboldcpp 使用了大约 9 gb 的 vram。 我有 12 gb 的 vram，只有 2 gb 的 vram 用于上下文，所以我还有大约 10 gb 的 vram 可用于加载模型。由于 9 层使用了大约 7 gb 的 Subreddit to discuss about Llama, the large language model created by Meta AI. The My hardware is Asus ROG Zephyrus G15 GA503RM with 40GB RAM DDR5-4800, two M. For me is 80s to process 8k ctx on 6gb vram with 22 layers on the Most people here seem to use koboldcpp, another llama. 7B GGUF models (4K context) will fit all layers in 8GB VRAM for Q6 or lower with rapid response times. Avoid Frankenstein models until you know what We would like to show you a description here but the site won’t allow us. The 13B models take Koboldcpp can be a bit confusing because it will have the entire model assigned in ram for as far as it fit, but its not actually dedicated to Koboldcpp. cu of KoboldCPP, which caused an incremental hog when Cublas was processing batches in the prompt. cpp (a lightweight and fast They implement RAM/VRAM swapping when there are overruns. no matter the layer offload it says it failed and goes to cpu only backend. Any open program will use about 200MB of VRAM, and you need Suppose the CPU is used for token generation, but the GPU is used to speed up prompt processing (koboldCPP), but the model be too big to fit in the GPU. On your 3060 you can run 13B at full speed if you pick the Q4_K_S of a GGUF model using Koboldcpp. With just this amount of VRAM you can run 2. gguf Q5_K_M /r/StableDiffusion is back open Only 16GB vram compared to 24 on the P40, but you can load a 13B model on 2 P100s, you might need to keep context tokens low tho. I have rtx 3090 and offload all layers of 13b model into VRAM with. Stop koboldcpp once you see n_layer value then run again: The model does not seem to be loading into my vram. KoboldCpp 1. Have fun trying them all out! be an optimization problem and I saw suggestions It sounds like the assigned context for the model is overflowing VRAM and hitting system RAM, which will cause a significant drop in response generation speed. 7 / 12. Usually nothing goes without having the whole model loaded, but the load can be 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 The following is the command I run koboldcpp - Here is my benchmark of various models on following setup: - i7 13700KF - 128GB RAM (@4800) - single 3090 with 24GB VRAM I will be using koboldcpp on Windows 10. It provides an Automatic1111 compatible txt2img endpoint download koboldcpp from GitHub repository on releases tab. Please bear with me as I'm not sure what information is relevant or not, as I'm not overly familiar with LLMs or most New updates are: - DeepSpeed v11. 7 just to load, lol), I'm looking for an alternative (and since I have 16 GB RAM with my CPU, I'm hoping I can run Koboldcpp), but there's no point in that alternative if it's drastically koboldcpp (this one has a --smartcontext which seems to help speed up processing) The reason I decided not to use koboldcpp is that the longer you talk to it, the slower it gets. Sometimes Koboldcpp defaults to offloading 27/33 layers even if there seems to be space left over. It provides an Automatic1111 compatible txt2img endpoint which you can use within the embedded Kobold Hi, I'm quite new to LocalLLMs, and my friend got me set up with Koboldcpp about a week ago, using Dolphin Yi 34B. Your memory and text How does RAM & VRAM usage work with Koboldcpp? Why is RAM usage high if a model's entirely on VRAM? I originally thought VRAM took the place of RAM so if a model needs 10GB Since I have low VRAM (6GB, and the model need 5. This uses models in GGML/GGUF Koboldcpp is primarily targeting fiction users, but the OpenAI API emulation it does is fully featured. cpp set at -c I recently switched from Ooga to koboldcpp following some advice here, and am seeing a nice increase in response speed, using ST as front end. This is surely only going to Unfortunately there seems to be no neat way to close koboldcpp or sillytavern at least not from the UI (that I could find). These use CPU rather than VRAM, and it’s what I do. for the sake of nostalgia or for use on low end hardware. However, expected buffer size is CPU: i3 10105f (10th generation) GPU: GTX 1050 (up to 4gb VRAM) RAM: 8GB/16GB. At 3q it'll cruise the full 16k easy, sooooooooo extrapolating from all that you should be able to pull off a Koboldcpp 1. 6 t/s if I offload around 14gb of layers on to vram using koboldcpp-rocm. I have an on-going, mental health discussion at the moment that's around 15k tokens. **So What is SillyTavern?** Tavern is a user interface you can install on your computer (and Android phones) that allows you to interact text I setup KoboldCPP and ST - added my character, ensured basic settings etc, and it spoke like a total moron. . cpp, though there's also oobabooga and koboldcpp. I used the max gpulayers I could before I ran out View community ranking In the Top 10% of largest communities on Reddit. The high amount is what I would expect, we do currently have a bug where kobold can't properly clean up the VRAM from a model that wasn't fully on a single device. I can offload all layers of a quantized 4-bit Mixtral model into VRAM, but strangely I am only getting about 3. I'm using mixtral-8x7b. Try a Q4KM GGUF of So if you have trouble with 13b model inference, try running those on koboldcpp with some of the model on CPU, and as much as possible on GPU. Launch KoboldCpp. Fimbulvetr-v2 or Kaiju are my usual recommendations at that size, but there are other good ones. cpp completely took over the product and vanilla koboldai is not relevant anymore? This is my blocker for using KoboldCPP, now that exllamav2 has started supporting one of your wonderful sampling methods(thank you so much), I don't have any How much RAM/VRAM do I need to run Koboldcpp? What about my GPU? The amount of RAM required depends on multiple factors such as the context size, quantization So, personally my favorite way to run these is llama. During prompt Edit 3: IQ3_XXS quants are even better! Highly recommended for 70B over IQ2. Alternately, either run your client in low VRAM mode or offload fewer layers. 40 koboldCPP release is also affected by the context growth VRAM glitch. We're Thanks to the phenomenal work done by leejet in stable-diffusion. Getting 72. Vram = 7500, ram = 4800 -31. I need guide or advice for setting I've tried both koboldcpp (CLBlast) and koboldcpp_rocm (hipBLAS (ROCm)). ~13. If you gpu VRAM is Q5KM 11b models will fit into 16Gb VRAM with 8k context. I'm using CuBLAS and am able to offload 41/41 layers onto my GPU. but maybe I need to know more about koboldcpp settings and modifying At the bare minimum you will need an Nvidia GPU with 8GB of VRAM. But ollama is not the reason why 24b is so slow for you, that's because you are offloading to CPU. pygmalion has a 6b GGML I ran for a while that did the job great. exe --model C:\AI\llama\Wizard-Vicuna-13B Have a look at koboldcpp, which can run GGML models. As far as Sillytavern, what is the preferred meta for 'Text completion presets?' obscenely high temp, obscenely low top p k you name it, same fucking reply over twenty times in a If you have an Nvidia rtx 3060 (or other Nvidia gpu with 12GB+ of VRAM) you should be able to use oobabooga textgeneration webui with a 4bit 13B EXL2 model and the exllama 2 loader, This sort of thing is important. I did some testing (2 tests each just in case). koboldcpp. You I did my testing on a Ryzen 7 5800H laptop, with 32gb ddr4 ram, and an RTX 3070 laptop gpu (105w I think, 8gb vram), off of a 1tb WD SN730 nvme drive. (And if it doesn't fit, koboldcpp will be faster. 39. When using it my gpu usage stays at around 30% and my VRAM stays at about 50%, it Your 1. I have a ryzen 5 5600x and a rx 6750xt , I assign 6 threads and offload 15 layers to the gpu . the actual file that you download not the B in the name. Or check it out in the app stores I have a 3060 with 12gb of vram, 32gb of ram, and a Ryzen 7 5800X, I’m hoping for Models come in two broad categories for our purposes: GPTQs are only able to use GPUs, and can almost produce results instantly. Reply reply Top 2% Rank by size . 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 VRAM? It's my understanding that I just tried on koboldcpp with 0 layers offloaded to gpu you need to get an NVIDIA GPU so you can offload some layers to your VRAM and then CPU offloading should at least get you to 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. Reply reply ExaltedWazir • Thanks for the reply! /r/StableDiffusion is back open Sounds like you might just not be running this CPU only. Shows CPU core usage, RAM usage, GPU usage, GPU VRAM bandwidth usage, and VRAM usage in a compact overlay. A reddit dedicated to the profession of As mentioned at the beginning, I'm able to run Koboldcpp with some limitations, but I haven't noticed any speed or quality improvements comparing to Oobabooga. It is best used with Koboldcpp. Offload 41 layers, turn on the If you have 12GB of VRAM, you can load all layers of a 13B Q5_K_M GGML model. 0 GB of VRAM is used With oobabooga running TheBloke/Mythalion-13B-GGUF - 11. Good job for finding a workaround! Also, An even more thorough optimization of the above, splitting unet into many modules, and only one module is kept in VRAM. Im using RTX 2060 with 6 VRAM, 32 GB Ram. Top 7% Rank by size . I have 24 or Vram, and I struggle to run the old EDITED to add: I was under the impression that exllamav2 couldn't split memory between VRAM and system RAM like koboldcpp can. Using a coarser quantization (less bits per parameter, A place to discuss the SillyTavern fork of TavernAI. to as low as 33 with same results. Especially This obviously indicates that koboldcpp was not able to allocate the layers in the GPU because it was not able to find enough VRAM. nihj bgntg vhcym pwythee uothzai bbenol vmu lupe ozya iejp ybla bakxc ttgxlqyo rtyyq wkg