GGML - Large Language Models for Everyone: a description of the GGML format provided by the maintainers of the llm Rust crate, which provides Rust bindings for GGML. Specifically, GPTQ can quantize GPT models with 175 billion parameters in approximately four GPU hours, reducing the bitwidth down to 3 or 4 bits. llama. Only the GPTQ models. On my box with Intel 13900K CPU, the 4090 is running at 100%. Model card Files Community. gptq_model-4bit-128g. Did not test GGUF yet, but is pretty much GGML V2. And in my GGML vs GPTQ tests, GGML did 20. In the Model drop-down: choose the model you just downloaded, falcon-7B. License: creativeml-openrail-m. If you are working on a game development project, GGML's specialized features and supportive community may be the best fit. In addition to defining low-level machine learning primitives (like a tensor. Or just manually download it. Interact privately with your documents using the power of GPT, 100% privately, no data leaks (by imartinez) Suggest topics Source Code. As for when - I estimate 5/6 for 13B and 5/12 for 30B. Under Download custom model or LoRA, enter TheBloke/falcon-7B-instruct-GPTQ. I worked with GPT4 to get it to run a local model, but I am not sure if it hallucinated all of that. github. 33B you can only fit on 24GB VRAM, even 16Gb are not enough. en-encoder-openvino. Unique Merging Technique. Running 13B and 30B models on a PC with a 12gb NVIDIA RTX 3060. Reply reply MrTopHatMan90 • Yeah that seems to of worked. I'm stuck with ggml's with my 8GB vram vs 64 GB ram. 22x longer than ExLlamav2 to process a 3200 tokens prompt. GPTQ. Supports transformers, GPTQ, AWQ, EXL2, llama. Specifically, GPTQ can quantize GPT models with 175 billion parameters in approximately four GPU hours, reducing the bitwidth down to 3 or 4. 0-GPTQ. bin: q3_K_L: 3: 3. New comments cannot be posted. My machine has 8 cores and 16 threads so I'll be. Quantize your own LLMs using AutoGPTQ. The GGML format was designed for CPU + GPU inference using llama. For more general-purpose projects that require complex data manipulation, GPTQ's flexibility and extensive capabilities. You'd have the best luck with NVIDIA GPUs, but with AMD GPUs, your mileage may vary. First attempt at full Metal-based LLaMA inference: llama :. My CPU is an "old" Threadripper 1950X. For ref, 13900k is 2x the single core performance vs 1950x. We'll explore the mathematics behind quantization, immersion fea. convert-gptq-ggml. #ggml #gptq PLEASE FOLLOW ME: LinkedIn: number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives. Click the Model tab. However, if your primary concern is efficiency, GPTQ is the optimal choice. So the end. cpp - convert-lora-to-ggml. Download OpenVINO package from release page. This is a Vicuna 1. 16 tokens per second (30b), also requiring autotune. GPTQ is a specific format for GPU only. Do you know of any github projects that I could replace GPT4All with that uses CPU-based GPTQ in Python?TheBloke/guanaco-33B-GGML. While they excel in asynchronous tasks, code completion mandates swift responses from the server. Once it's finished it will say "Done". 8G. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. It is the result of quantising to 4bit using GPTQ-for-LLaMa. GPTQ can lower the weight precision to 4-bit or 3-bit. CPP models (ggml, ggmf, ggjt) All versions of ggml ALPACA models (legacy format from alpaca. When comparing llama. cpp is a project that uses ggml to run LLaMA, a large language model (like GPT) by Meta. 24 # GPU version!pip install ctransformers[gptq] On you computer: We also outperform a recent Triton implementation for GPTQ by 2. I heard that it's slower than GPTQ if GPTQ can run it (meaning it fits into VRAM entirely). It explores their features, benefits,. GGML presents an alternative. 0. c) T4 GPU. 5. Ah, or are you saying GPTQ is GPU focused unlike GGML in GPT4All, therefore GPTQ is faster in MLC Chat? So my iPhone 13 Mini’s GPU drastically outperforms my desktop’s Ryzen 5 3500? Bingo. OpenLLaMA is an openly licensed reproduction of Meta's original LLaMA model. Update 1: added a mention to. One quantized using q4_1, another one was quantized using q5_0, and the last one was quantized using q5_1. Finally, and unrelated to the GGML, I then made GPTQ 4bit quantisations. , only utilizes 4 bits and represents a significant advancement in the field of weight quantization. gpt4-x-vicuna-13B-GGML is not uncensored, but. 2) AutoGPTQ claims it doesn't support LORAs. Although GPTQ does compression well, its focus on GPU can be a disadvantage if you do not have the hardware to run it. The lower bit quantization can reduce the file size and memory bandwidth requirements, but also introduce more errors and noise that can affect the accuracy of the model. I’m keen to try a ggml of it when that becomes possible to see if it’s a bug in my GPTQ files or. Download the 3B, 7B, or 13B model from Hugging Face. This is the repository for the 70B pretrained model, converted for the Hugging Face Transformers format. model files. . cpp (GGUF/GGML)とGPTQの2種類が広く使われている。. GPTQ scores well and used to be better than q4_0 GGML, but recently the llama. 4bit quantization – GPTQ / GGML. < llama-30b-4bit 1st load INFO:Loaded the model in 7. 0更新【6. Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 1-GPTQ-4bit-128g-GGML. When comparing llama. Supports transformers, GPTQ, AWQ, EXL2, llama. Their rate of progress is incredible. conda activate vicuna. Try 4bit 32G and you will more than likely be happy with the result! When comparing GPTQ-for-LLaMa and llama. 首先声明一点，我不是text-generation-webui的制作者，我只是懒人包制作者。懒人包V1. Click Download. You will need auto-gptq>=0. Once it's finished it will say "Done". However, bitsandbytes does not perform an optimization. GPTQ vs. 0. Please note that these MPT GGMLs are not compatbile with llama. Wizard Mega 13B GGML This is GGML format quantised 4bit and 5bit models of OpenAccess AI Collective's Wizard Mega 13B. GPTQ is currently the SOTA one shot quantization method for LLMs. 7 GB, 12. GPU Installation (GPTQ Quantised) First, let’s create a virtual environment: conda create -n vicuna python=3. However, on 8Gb you can only fit 7B models, and those are just dumb in comparison to 33B. and that llama. By reducing the precision of their. Just monitor your cpu usage vs gpu usage. Hi all, looking for a guide/some advice on how to do this. Albeit useful techniques to have in your skillset, it seems rather wasteful to have to apply them every time you load the model. Quantized models are available from TheBloke: GGML - GPTQ (You're the best!) Model details The idea behind this merge is that each layer is composed of several tensors, which are in turn responsible for specific functions. Quantize your own LLMs using AutoGPTQ. cpp, and also all the newer ggml alpacas on huggingface) GPT-J/JT models (legacy f16 formats here as well as 4 bit quantized ones like this and pygmalion see pyg. This format is good for people that does not have a GPU, or they have a really weak one. Download the 3B, 7B, or 13B model from Hugging Face. Click Download. As quoted from this site. In the top left, click the refresh icon next to Model. If you mean running time - then that is still pending with int-3 quant and quant 4 with 128 bin size. Supports transformers, GPTQ, AWQ, EXL2, llama. GPTQ model: anon8231489123/vicuna-13b-GPTQ-4bit-128g on huggingfaceoriginal model: lm-. This user has. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. NF4 vs. This ends up effectively using 2. ) There's no way to use GPTQ on macOS at this time. I was able to load 70B GGML model offloading 42 layers onto the GPU using oobabooga. H2OGPT's OASST1-512 30B GGML These files are GGML format model files for H2OGPT's OASST1-512 30B. Under Download custom model or LoRA, enter TheBloke/falcon-40B-instruct-GPTQ. GGUF, previously GGML, is a quantization method that allows users to use the CPU to run an. The download links might change, but a single-node, “bare metal” setup is similar to below: Ensure you can use the model via python3 and this example. Anyone know how to do this, or - even better - a way to LoRA train GGML directly?gptq_model-4bit-128g. StarCoderPlus is a fine-tuned version of StarCoderBase on 600B tokens from the English web dataset RedefinedWeb combined with StarCoderData from The Stack (v1. The GGML_TYPE_Q5_K is a type-1 5-bit quantization, while the GGML_TYPE_Q2_K is a type-1 2-bit quantization. e. GPTQ is a one-shot weight quantization method based on approximate second-order information, allowing for highly accurate and efficient quantization of GPT models with 175 billion parameters. 5. `A look at the current state of running large language models at home. AI's original model in float32 HF for GPU inference. The 8bit models are higher quality than 4 bit, but again more memory etc. GPTQ clearly outperforms here. But Vicuna 13B 1. They take only a few minutes to create, vs more than 10x longer for GPTQ, AWQ, or EXL2, so I did not expect them to appear in any Pareto frontier. However, if your primary concern is efficiency, GPTQ is the optimal choice. Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Click the Model tab. Further, we show that our model can also provide robust results in the extreme quantization regime,WizardLM-7B-uncensored-GGML is the uncensored version of a 7B model with 13B-like quality, according to benchmarks and my own findings. I appear to be stuck. 0-GPTQ. in the download section. jsons and . Sol_Ido. How is ggml speed for you vs gptq if you don’t mind me asking? I have a 5800x3d and a 4090 so not too different, but have never tried ggml. 4bit quantised GPTQ models for GPU inference - TheBloke/stable-vicuna-13B-GPTQ. Under Download custom model or LoRA, enter TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ. KoboldCpp, a powerful GGML web UI with GPU acceleration on all platforms (CUDA and OpenCL). GGML vs GPTQ — Source:1littlecoder 2. Recent advancements in weight quantization allow us to run massive large language models on consumer hardware, like a LLaMA-30B model on an RTX 3090 GPU. Damp %: A GPTQ parameter that affects how samples are processed for quantisation. GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Model Developers Meta. Looking forward, our next article will explore the GPTQ weight quantization technique in depth. cpp users to enjoy the GPTQ quantized models. 增加exllama，一种比AutoGPTQ速度更快(生成速度上)的GPTQ量化模型加载方式。Damp %: A GPTQ parameter that affects how samples are processed for quantisation. cpp) can. cpp (GGUF), Llama models. This repo is the result of quantising to 4bit and 5bit GGML for CPU inference using llama. --Best--GGML Wizard Vicuna 13B 5_1 GGML Wizard Vicuna 13B 5_0 GPTQ Wizard Vicuna 13B 4bit GGML Wizard Vicuna. For GPTQ tests, I used models with groupsize 128 and no desc_act, which are the ones that are widely used. I've just finished a thorough evaluation (multiple hour-long chats with 274 messages total over both TheBloke/Nous-Hermes-Llama2-GGML (q5_K_M) and TheBloke/Redmond-Puffin-13B-GGML (q5_K_M)) so I'd like to give my feedback. Eventually, this gave birth to the GGML format. You can now start fine-tuning the model with the following command: accelerate launch scripts/finetune. bin. py oasst-sft-7-llama-30b/ oasst-sft-7-llama-30b-xor/ llama30b_hf/. Benchmark Execution: Running benchmarks on identical tasks using both SYCL and CUDA forms the foundation of performance comparison. ggml for llama. the latest version should be 0x67676d66, the old version which needs migration should be: 0x67676d6c. Repositories availableTim Dettmers' Guanaco 65B GGML These files are GGML format model files for Tim Dettmers' Guanaco 65B. In the table above, the author also reports on VRAM usage. 01 is default, but 0. 0. pt. Half precision floating point, and quantization optimizations are now available for your favorite LLMs downloaded from Huggingface. float16 HF format model for GPU inference. GGML: 3 quantized versions. GPTQ vs. GGUF / GGML versions run on most computers, mostly thanks to quantization. ggml is a library that provides operations for running machine learning models. 01 is default, but 0. Moreover, GPTQ compresses the largest models in approximately 4 GPU hours, and can execute on a single GPU. Updated to the latest fine-tune by Open Assistant oasst-sft-7-llama-30b-xor. 4375 bpw. Combining Wizard and Vicuna seems to have strengthened the censoring/moralizing stuff each inherited from fine-tuning with Open ClosedAI's ChatGPT even more. Another day, another great model is released! OpenAccess AI Collective's Wizard Mega 13B. The default templates are a bit special, though. SuperHOT is a new system that employs RoPE to expand context beyond what was originally possible for a model. GPTQ supports amazingly low 3-bit and 4-bit weight quantization. Llama 2 Airoboros 7/13/70B GPTQ/GGML Released! Find them on TheBloke's huggingface page! Hopefully, the L2-70b GGML is an 16k edition, with an Airoboros 2. Click Download. In the Model dropdown, choose the model you just downloaded: WizardCoder-15B-1. GPU/GPTQ Usage. Under Download custom model or LoRA, enter TheBloke/Wizard-Vicuna-7B-Uncensored-GPTQ. Click Download. GPTQ is TERRIBLE with RAM swap, because CPU doesn't compute anything there. There are already bleeding edge 4-bit quantization efforts such as GPTQ for LLaMA. This is an example to launch koboldcpp in streaming mode, load a 8k SuperHOT variant of a 4 bit quantized ggml model and split it between the GPU and CPU. It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. It can load GGML models and run them on a CPU. Python 27. Env: Mac M1 2020, 16GB RAM Performance: 4 ~ 5 tokens/s Reason: best with my limited RAM, portable. cpp and libraries and UIs which support this format, such as: KoboldCpp, a powerful GGML web UI with full GPU acceleration out of the box. This is possible thanks to novel 4-bit quantization techniques with minimal performance degradation, like GPTQ, GGML, and NF4. 4bit and 5bit GGML models for GPU inference. Damp %: A GPTQ parameter that affects how samples are processed for quantisation. Can ' t determine model type from model name. Click the Refresh icon next to Model in the top left. This is possible thanks to novel 4-bit quantization techniques with minimal performance degradation, like GPTQ, GGML, and NF4. bin file is to use this script and this script is keeping the GPTQ quantization, it's not converting it into a q4_1 quantization. GGML vs. This is normal. Repositories available 4-bit GPTQ models for GPU inferencellama. gpt4-x-alpaca’s HuggingFace page states that it is based on the Alpaca 13B model, fine. That's what I understand. GGML is a C library for machine learning (ML) — the “GG” refers to the initials of its originator (Georgi Gerganov). Under Download custom model or LoRA, enter TheBloke/Nous-Hermes-13B-GPTQ. py generated the latest version of model. Wait until it says it's finished downloading. yaml. And I've seen a lot of people claiming much faster GPTQ performance than I get, too. GPTQ-for-LLaMa vs llama. but when i run ggml it just seems so much slower than GPTQ versions. pt file into a ggml. 24 seconds. Updated the ggml quantizations to be compatible with the latest version of llamacpp (again). You should expect to see one warning message during execution: Exception when processing 'added_tokens. At a higher level, the process involves. One of the most popular is GPTQ – introduced in March 2023 which uses 4 bits (16 distinct values!) to represent a floating point. Vicuna v1. 0-GPTQ. Yup, an extension would be cool. Open Llama 3B has tensor sizes that are not a multiple of 256. 256 70 2,931 contributions in the last year Contribution Graph; Day of Week: November Nov: December Dec: January Jan: February Feb: March Mar: April Apr: May May: June Jun:. This video explains difference between GGML and GPTQ in AI models in very easy terms. GGML is designed for CPU and Apple M series but can also offload some layers on the GPU. Prompt processing speed. cpp) rather than having the script match the existing one: - The tok_embeddings and output. cpp (GGUF), Llama models. cpp team have done a ton of work on 4bit quantisation and their new methods q4_2 and q4_3 now beat 4bit GPTQ in this. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". LLM: quantisation, fine tuning. Different UI for running local LLM models Customizing model. So here it is, after exllama, GPTQ and SuperHOT stole GGML the show for a while, finally there's a new koboldcpp version with: full support for GPU acceleration using CUDA and OpenCL. NF4. When you run this program you should see output from the trained llama. I am on the razer edge, but I was able to have an 8 hour RP with that of around 868K Tokens sent total for the entire session. Currently these files will also not work with code that. There are 2 main formats for quantized models: GGML (now called GGUF) and GPTQ. cpp team on August 21, 2023, replaces the unsupported GGML format. Using MythoLogic-L2's robust understanding as its input and Huginn's extensive writing capability as its output seems to. 35 2,669 9. Output Models generate text only. Are we just kidding ourselves and it's more the randomness as to what you get. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Next, we will install the web interface that will allow us. Currently, quantizing models are used for two main purposes: So far, two integration efforts have been made and are natively supported in transformers : bitsandbytes and auto-gptq . cpp and libraries and UIs which support this format, such as: text-generation-webui; KoboldCpp; ParisNeo/GPT4All-UI; llama-cpp-python; ctransformers; Repositories available 4-bit. 1. Reply reply. 3 Python text-generation-webui VS llama Inference code for LLaMA modelsIt still works with Pygmalion 7B GPTQ, but it doesn't seem to work with Wizard Vicuna 13B GGML, although I can load and use the latter in Ooba. We performed some speed, throughput and latency benchmarks using optimum-benchmark library. GGUF boasts extensibility and future-proofing through enhanced metadata storage. I have suffered a lot with out of memory errors and trying to stuff torch. py does work on the QLORA, but when trying to apply it to a GGML model it refuses and claims it's lacking a dtype. GGCC is a new format created in a new fork of llama. sponsored. First I will show the results of my personal tests, which are based on the following setup: A . I think the gpu version in gptq-for-llama is just not optimised. A discussion thread on GitHub that compares the performance of GGML, a generative model for text generation, with and without GPU acceleration and three different GPTQ. q3_K_L. The change is not actually specific to Alpaca, but the alpaca-native-GPTQ weights published online were apparently produced with a later version of GPTQ-for-LLaMa. in-context. Damp %: A GPTQ parameter that affects how samples are processed for quantisation. cpp library, also created by Georgi Gerganov. Block scales and mins are quantized with 4 bits. Supports transformers, GPTQ, AWQ, EXL2, llama. 4bit means how it's quantized/compressed. GPTQ is a one-shot weight quantization method based on approximate second-order information, allowing for highly accurate and efficient quantization of GPT models with 175 billion parameters. INFO:Loaded the model in 104. GPTQ dataset: The dataset used for quantisation. Pre-Quantization (GPTQ vs. To use with your GPU using GPTQ pick one of the . 4-bit quantization tends to come at a cost of output quality losses. 0-Uncensored-GGML or if you have a GPU with 8 GB of VRAM use the GPTQ version instead of the GGML version. Downloaded Robin 33B GPTQ and noticed the new model interface, switched over to EXllama and read I needed to put in a split for the cards. 4375 bpw. 0. Detailed Method. , 2023) was first applied to models ready to deploy. (2) Es ist schwer zu sagen wann man lieber auf ein GPTQ quantisierten oder einen. 01 is default, but 0. cpp. q6_K version of the model (llama. It allowed models to be shared in a single file, making it convenient for users. 01 is default, but 0. Results. 44 tokens/sClick the Model tab. cpp) rather than having the script match the existing one: - The tok_embeddings and output weights (i. 9. Now click the Refresh icon next to Model in the. During GPTQ I saw it using as much as 160GB of RAM. Just anecdotally, switching from a Q4 GPTQ model to Q6_K GGML for MythoMax-L2-13B produced palpable improvements. I have not tested this though. Before you can download the model weights and tokenizer you have to read and agree to the License Agreement and submit your request by giving your email address. 9 min read. Gptq-triton runs faster. 2x. Download: GGML (Free) Download: GPTQ (Free) Now that you know what iteration of Llama 2 you need,. Links to other models can be found in the index at the bottom. For reference, I'm used to 13B models generating at 2T/s, and 7B models at 4 T/s. But for me, using Oobabooga branch of GPTQ-for-LLaMA AutoGPTQ versus llama-cpp-python 0. 29. OpenLLaMA is an openly licensed reproduction of Meta's original LLaMA model. Tim Dettmers' Guanaco 33B GGML These files are GGML format model files for Tim Dettmers' Guanaco 33B. TheBloke/wizardLM-7B-GPTQ. Click Download. Wait until it says it's finished downloading. TheBloke/MythoMax-L2-13B-GPTQ differs from other language models in several key ways: 1. cpp and libraries and UIs which support this format, such as: text-generation-webui; KoboldCpp; ParisNeo/GPT4All-UI; llama-cpp-python; ctransformers; Repositories available 4-bit. In combination with Mirostat sampling, the improvements genuinely felt as good as moving. In GPTQ, we apply post-quantization for once, and this results in both memory savings and inference speedup (unlike 4/8-bit quantization which we will go through later). GPTQ & GGML allow PostgresML to fit larger models in less RAM. 2t/s. I don't usually use ggml as it's slower than gptq models by a factor of 2x using GPU. GGML is the only option on Mac. GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. cpp, and adds a versatile Kobold API endpoint, additional format support, backward compatibility, as well as a fancy UI with persistent stories, editing tools, save formats, memory, world info,. One quantized using q4_1, another one was quantized using q5_0, and the last one was quantized using q5_1. cpp is the slowest, taking 2. Once the quantization is completed, the weights can be stored and reused. cpp (GGUF/GGML)とGPTQの2種類が広く使われている。. safetensors along with all of the . Env: Mac M1 2020, 16GB RAM. . GPTQ, AWQ, and GGUF are all methods for weight quantization in large language models (LLMs). For illustration, GPTQ can quantize the largest publicly-available mod-els, OPT-175B and BLOOM-176B, in approximately four GPU hours, with minimal increase in perplexity, known to be a very stringent accuracy metric. jsons and . model files. I'll be posting those this weekend. ローカルLLMの量子化フォーマットとしては、llama. Wait until it says it's finished downloading. GGML13B Metharme GGML: CPU: Q4_1, Q5_1, Q8: 13B Pygmalion: GPU: Q4 CUDA 128g: 13B Metharme: GPU: Q4 CUDA 128g: VicUnLocked 30B (05/18/2023) A full context LoRA fine-tuned to 1 epoch on the ShareGPT Vicuna Unfiltered dataset, with filtering mostly removed. You can find many examples on the Hugging Face Hub, especially from TheBloke . 9 GB: True: AutoGPTQ: Most compatible. 0. Oobabooga: If you require further instruction, see here and here Baku. Click Download. cpp, text-generation-webui or KoboldCpp. GGML vs. 4. We built Llama-2-7B-32K-Instruct with less than 200 lines of Python script using Together API, and we also make the recipe fully available . This is the pattern that we should follow and try to apply to LLM inference. If you’re looking for an approach that is more CPU-friendly, GGML is currently your best option. cpp/GGML CPU inference, which enables lower cost hosting vs the standard pytorch/transformers-based GPU hosting. GPTQ dataset: The dataset used for quantisation. Wait until it says it's finished downloading. An exchange should look something like (see their code):Complete guide for KoboldAI and Oobabooga 4 bit gptq on linux AMD GPU Tutorial | Guide Fedora rocm/hip installation. So far, I've run GPTQ and bitsandbytes NF4 on a T4 GPU and found: fLlama-7B (2GB shards) nf4 bitsandbytes quantisation: - PPL: 8. w2 tensors, else GGML_TYPE_Q3_K: llama-2. Super fast (12tokens/s) on single GPU. We've fine-tuned Phind-CodeLlama-34B-v1 on an additional 1. As this is a GPTQ model, fill in the GPTQ parameters on the right: Bits = 4, Groupsize = 128, model_type = Llama. Llama 2. To download from a specific branch, enter for example TheBloke/Wizard-Vicuna-7B. 其实有一个感想是目前. We will try to get in discussions to get the model included in the GPT4All. However, we made it in a continuous conversation format instead of the instruction format. 45/hour. I didn't end up using the second GPU, but I did need most of the 250GB RAM on that system. I am in the middle of some comprehensive GPTQ perplexity analysis - using a method that is 100% comparable to the perplexity scores of llama. TheBloke/guanaco-65B-GPTQ. Agreed on the transformers dynamic cache allocations being a mess. jsons and . And I dont think there is literally any faster GPU out there for inference (VRAM Limits excluded) except H100. In the top left, click the refresh icon next to Model. model files. The huge thing about it is that it can offload a selectable number of layers to the GPU, so you can use whatever VRAM you have, no matter the model size. By using the GPTQ-quantized version, we can reduce the VRAM requirement from 28 GB to about 10 GB, which allows us to run the Vicuna-13B model on a single consumer GPU.