Llama inference speed a100. 1, evaluated llama-cpp-python versions: 2.

Llama inference speed a100 It is between GGUF Q4_K_M and Q4_K_L. By pushing the batch size to the maximum, A100 can deliver 2. IMHO, A worthy alternative is Ollama but the inference I am running a the 30B parameter model on 4 bit quantization. 0 Clang version: Could not collect CMake version: version 3. We tested them across six different inference engines (vLLM, TGI, TensorRT-LLM, Tritonvllm, Deepspeed-mii, ctranslate) on A100 GPUs hosted on Azure, ensuring a neutral playing field separate from our Inferless I am looking for a GPU with really good inference speed. Mistral 7B: Verified 30 tokens/sec on A100 (FP16) under standard conditions. System requirements for running Llama 3 models, NVIDIA A100 80GB x2: General-purpose inference: 70b-instruct-fp16: 161GB: NVIDIA A100 80GB x2: High-precision fine-tuning and training: 70b-instruct-q2_K: High-speed, mid-precision inference: 70b-instruct-q4_1: 44GB: NVIDIA RTX 4090 x2: Token Generation Speed: Understand how different devices and models affect LLM inference speed. Model Input Dumps here is my co We introduce LLM-Inference-Bench, a comprehensive benchmarking study that evaluates the inference performance of the LLaMA model family, including LLaMA-2-7B, LLaMA-2-70B, LLaMA-3-8B, LLaMA-3-70B, as well as other prominent LLaMA derivatives such as Mistral-7B, Mixtral-8x7B, Qwen-2-7B, and Qwen-2-72B across a variety of AI accelerators, including Nvidia A100, On an A100 SXM 80 GB: 16 ms + 150 tokens * 6 ms/token = 0. ScaleLLM can now host one LLaMA-2-13B-chat inference service on a single NVIDIA RTX 4090 GPU. Based on the performance of theses results we could also calculate the most cost effective GPU to run an inference endpoint for With this in mind, this whitepaper provides step-by-step guidance to deploy Llama 2 for inferencing on an on-premises datacenter and analyze memory utilization, latency, and We discuss how the computation techniques and optimizations discussed here improve inference latency by 6. We’ve been trying to get speed from Llama 3. 5-14B, SOLAR-10. Hugging Face TGI: A Rust, Python and gRPC server for text generation inference. Llama marked a significant step forward for LLMs, demonstrating the power of pre-trained architectures for a wide range of applications. Now auto awq isn’t really recommended at all since it’s pretty slow and the quality is meh since it only supports 4 bit. which directly measures inference speed. However, its base model Llama-2-7b isn't this fast so I'm wondering do we know if there was any tricks etc. 1-70B at an astounding 2,100 tokens per second a model 23x smaller; Equivalent to a new GPU generation’s performance upgrade (H100/A100) SVP of AI and ML at GSK, lyraLLaMA is currently the fastest LLaMA-13b available. 1" tokenizer = In this blog, we discuss how to improve the inference latencies of the Llama 2 family of models using PyTorch native optimizations such as native fast kernels, compile transformations from torch compile, and tensor parallel Cerebras Inference now runs Llama 3. Given the large size of the model, it is recommended to use SSD to speed up the loading times; GCP region is europe-west4; Notes. When it comes to running large language models (LLMs), performance and scalability are key to achieving economically viable speeds. 5 TB/s bandwidth on GPU dedicated entirely to the model on highly optimized backend (rtx 4090 have just under 1TB/s but you can get like 90-100t/s with mistral 4bit GPTQ) LLaMA-4bit inference speed for various context limits on dual RTX 4090 (triton optimized) The inference speed is acceptable, but not great. 24 5800. Uncover key performance insights, speed comparisons, and practical Try classification. 4 tokens/s speed on A100, according to my understanding at least should Twice the Performances and improvment area. 1 with 8 billion parameters and a commonly used 16-bit floating-point precision. Even in FP16 precision, the LLaMA-2 70B model requires 140GB. 04. cpp vs ExLLamaV2, then it is not correct. I would like to know the speed for pure 13900K inference without the help of GPUs, as well as the speed with both GPU and CPU. 65 7504. Same or comparable inference speed on a single A100 vs 2 A100 setup. We introduce LLM-Inference-Bench, a comprehensive benchmarking study that evaluates the inference performance of the LLaMA model family, including LLaMA-2-7B, LLaMA-2-70B, LLaMA-3-8B, LLaMA-3-70B, as well as other prominent LLaMA derivatives such as Mistral-7B, Mixtral-8x7B, Qwen-2-7B, and Qwen-2-72B across a variety of AI accelerators, including Nvidia A100, I am writing to report a performance issue I encountered while running the llama2-70B-chat model locally on an 8*A100 (80G) device. By using TensorRT-LLM and quantizing the model to int8, we can achieve important performance milestones while using only a single A100 GPU. LLM Inference Basics LLM inference consists of two stages: prefill and decode. 📌 Note: Actual inference speeds depend heavily on hardware configuration, batch size, and retrieval latency That is incredibly low speed for an a100. 2. 11, 2. 10. 0. Notifications You must be signed in to change notification settings; Fork 9. 04) 11. Technical Blog. We Let’s Compare A100 and H100 from Alternative Diagonals. 5 while using fewer parameters and enabling faster inference. PowerInfer: 11x Speed up LLaMA II Inference On a Local GPU. Simple classification is a much more widely studied problem, and there are many fast, robust solutions. 24xlarge powered by NVIDIA A100 40GB GPU. I'm still learning how to make it run inference faster on batch_size = 1 Currently when loading the model from_pretrained(), I only pass device_map = "auto" Double or triple throughput at same-or-better latencies by switching to H100 GPUs from A100s for model inference with TensorRT/TensorRT-LLM. For the dual GPU setup, we utilized both -sm row and -sm layer options in llama. 4 LTS (x86_64) GCC version: (Ubuntu 11. Llama 3 also runs on NVIDIA Jetson Orin for robotics and edge computing devices, creating interactive agents like those in the Jetson AI Lab. 1 8B Instruct on Nvidia H100 SXM and A100 chips measure the 3 valuable outcomes of vLLM: High Throughput: vLLM cranks out tokens fast, even when you're handling multiple requests in lyraLLaMA is currently the fastest LLaMA-13b available. 5 for completion tokens. 2 Libc version: glibc-2. Llama 2 comes and p4d. I fonud that the speed of nf4 has been greatly improved thah Qlora. 2: 98: April 12 NVIDIA A100 Llama 3. a comparison of Llama 2 70B inference across various hardware and software settings. I’m using a100 pcie 80g. Is this normal? comments sorted by Best Top New Controversial Q&A Add a Comment. 5 times better 25 votes, 50 comments. What is Llama 2? Llama 2 is a family of LLMs from Meta, trained on 2 trillion tokens. Quantization: We compared performance with and without quantization. It outperforms all current open-source inference engines, especially when compared to the renowned llama. The only place I would consider it is for 120b or 180b and people's experimenting hasn't really proved it to be worth the extra vram What is the raw performance gain from switching our GPUs from NVIDIA A100 to NVIDIA H100, as it can process double the batch at a faster speed. Real-World Testing: Testing of popular models (Llama 3. This thread objective is to gather llama. 3 Performance Characteristics. 1 405B, you’re looking at a staggering 232GB of VRAM, which requires 10 RTX 3090s or powerful data center GPUs like A100s or H100s. cpp (build: 8504d2d0, 2097). 0-1ubuntu1~22. Loading the model requires multiple GPUs for inference, even with a powerful NVIDIA A100 80GB GPU. Note that all memory and speed optimizations that we will apply going forward, are equally applicable to models that require model or tensor parallelism. Memory speed. 1 70B GGUF Q4 model on an A100 80G GPU using vLLM. The text was updated successfully, PyTorch version: 2. For optimal performance, data center-grade GPUs like the NVIDIA H100 or A100 would be recommended. The task force examined several potential candidates for inclusion: GPT-175B, Falcon-40B, Falcon-180B, BLOOMZ, and Llama 2 70B. 5tps at The following are the parameters passed to the text-generation-inference image for different model configurations: ‍ PARAMETERS: LLAMA-2-13B ON A100: LLAMA-2-13B ON A10G: Max Batch Prefill Tokens 10100 Data center inference results. Built on the GGML library released the previous year, llama. We speculate competitive Hello, this is my first time trying out Huggingface with a model this big. 12 (main, Jul 29 2024, 16:56:48) [GCC 11. This blog post aims to help answer these questions and guide your inference deployment planning. cpp, with ~2. Llama 2 70B, A100 compared to H100 with and without TensorRT-LLM The first speed is for a 1920-token prompt, A100 (SXM4) and H100 (PCIe) with WizardLM-30B-Uncensored-GPTQ Here are my results (avg on 10 runs) with 14 tokens prompt, 110 tokens generated on average and 2048 max seq. 1 405B while achieving 1. We implemented a custom script to measure Tokens Per Second Hugging Face TGI provides a consistent mechanism to benchmark across multiple GPU types. . A100 not looking very impressive on that. Additionally, I am curious if the E-cores of the 13900K have a negative impact on performance and if you turn them off. 1 family is Meta-Llama-3–8B. I Dive into our comprehensive speed benchmark analysis of the latest Large Language Models (LLMs) including LLama, Mistral, and Gemma. 1 To address challenges associated with the inference of large-scale transformer models, DeepSpeed Inference uses 4th generation Intel Xeon Scalable processors to speed up the inferences of GPT-J-6B and Llama-2-13B. GPU inference stats when all two GPUs are available to the inference process 2x A100 GPU server, cuda 12. The top performers in the new generative AI categories was an Nvidia H200 system that combined eight of the GPUs with two Intel Xeon CPUs. *RAM needed to load the model initially. The average reading speed is estimated to be between 200-300 words per minute, with exceptional readers reaching Very good work, but I have a question about the inference speed of different machines, I got 43. 04, CUDA 12. 48 Each model brings unique strengths, from Qwen2's rapid token generation to Llama's impressive efficiency under various token loads. 19 with cuBLAS backend. What’s more, NVIDIA RTX and GeForce RTX GPUs for workstations and PCs speed inference on Llama 3. Efficient management of attention key and value memory with Llama 3 will likely demand substantial GPU resources, possibly exceeding those of Llama 2. With -sm row, the dual RTX 3090 demonstrated a higher inference speed of 3 tokens per second (t/s), whereas the dual RTX 4090 performed better with -sm layer, achieving 5 t/s I can't imagine why. 3 70B Inference Throughput 3x with NVIDIA TensorRT-LLM Speculative Decoding. 40 on A100-80G. 4. it does not increase the inference speed. If your system doesn't have quite enough RAM to fully load the model at startup, you can create a swap file to help with the loading. 1 ROCM used to build PyTorch: N/A OS: Ubuntu 22. 25x higher throughput per node over baseline (Fig. len: avg perf So far your implementation is the fastest inference I've tried for quantised llama models. For example, Llama 2 70B significantly outperforms Llama 2 7B in downstream tasks, but its inference speed is approximately 10 times slower. If the inference backend supports native quantization, we used the inference backend-provided quantization method. 2: 521. 1 405B Performance up to 1. Let's try to fill the gap 🚀. 44x with NVIDIA TensorRT Model Optimizer on NVIDIA H200 GPUs Hello everyone,I'm currently running Llama-2 70b on an A6000 GPU using Exllama, and I'm achieving an average inference speed of 10t/s, with peaks up to 13t/s. 2 inference software with NVIDIA DGX H100 system, Llama 2 70B query with an input sequence length of 2,048 and output sequence length of 128. Are there ways to speed up Llama-2 for classification inference? This is a good idea - but I'd go a step farther, and use BERT instead of Llama-2. 6). Reply reply By using device_map="auto" the attention layers would be equally distributed over all available GPUs. For the 70B model, we performed 4-bit quantization so that it could run on a single A100–80G GPU. The TensorRT compiler is efficient at fusing layers and increasing execution speed, however, Boost Llama 3. Using vLLM v. Some neurons are HOT! Some are cold! LLMs (including OPT-175B) on a single NVIDIA RTX 4090 GPU, only 18% lower than that achieved by a top-tier server-grade A100 GPU. In a landscape where AI innovation is accelerating at an unprecedented pace, Meta’s Llama family of open sourced large language models (LLMs) stands out as a notable breakthrough. The inference speed of lyraLLaMA has achieved 3000+ tokens/s on A100, up to 6x acceleration upon the torch version. nim, llama. 2 and 2-2. However, the speed of nf4 is still slower than fp16. However, the H100 significantly boosts performance with FP8 support, Transformer Engine, and higher memory bandwidth, enabling faster multi-modal processing. Multiple NVIDIA GPUs or Apple Silicon for Large Language Model Inference? 🧐 See more Our “go-to” hardware for inference for a model of this size is the g5. Performance Impact of Scaling a 70B Model Across Multiple A100 GPUs and Further Speed Optimization #7648. The A10 is a cost-effective NVIDIA A100 Llama 3. 92s. 4x on 65B parameter LLaMA models powered by Google These benchmarks of Llama 3. This is why popular inference engines like vLLM and TensorRT are vital to production scale deployments . cpp code base was originally released in 2023 as a lightweight but efficient framework for performing inference on Meta Llama models. Discover how these models perform on Azure's A100 GPU, providing essential insights for AI engineers and developers It supports single-node inference of Llama 3. 88 times lower than that of a single service using vLLM on a single A100 GPU. When running Llama-2 AI models, you gotta pay attention to how RAM bandwidth and mdodel size impact inference speed. Current Behavior. We conducted extensive benchmarks of Llama 3. to get the best response times. On Llama 2—a popular language model released recently by Meta and used widely by organizations looking to incorporate generative AI—TensorRT-LLM can accelerate inference performance by 4. Speedup is normalized to the GPU count. We raised a $75m series C to build the future of Time to first token also depends on factors like network speed, The H100 offers 2x to 3x better performance than the A100 for model inference, To calculate an example, let's take the popular LLM Llama 3. cpp performance 📈 and improvement ideas💡against other popular LLM inference frameworks, especially on the CUDA backend. So I have 2-3 old GPUs (V100) that I can use to serve a Llama-3 8B model. cpp quickly became attractive to many users and developers (particularly for use on personal workstations) due to its focus on C/C++ without Mixtral 8x7B is an LLM with a mixture of experts architecture that produces results that compare favorably with Llama 2 70B and GPT-3. 5 on mistral 7b q8 and 2. Maximum context length support. When tested I get a slightly lower inference speed on 3090 compared to A100. true. You can estimate Time-To-First-Token (TTFT), Time-Per-Output-Token (TPOT), and the VRAM (Video Random Access Memory) needed for Large Language Model (LLM) inference in a few lines of calculation. Your current environment I am running the llama 3. x across NVIDIA A100 GPUs. 0] (64 The following are the parameters passed to the text-generation-inference image for different model configurations: ‍ PARAMETERS: LLAMA-2-7B ON A100: LLAMA-2-7B ON A10G: Max Batch Prefill Tokens 6100 Explore our detailed analysis of leading LLMs including Qwen1. cpp. Gain efficiency insights from Llama-2-70B benchmarking. Not required for inference. To get 100t/s on q8 you would need to have 1. cpp that referenced this issue Aug 2, 2023 It can run a 8-bit quantized LLaMA2-7B model on a cpu with 56 cores in speed of ~25 tokens / s. Factoring in GPU prices, we can look at an approximate tradeoff between speed and cost for inference. vLLM is a distributed inference and serving library, which provides: State-of-the-art serving throughput. 8 on llama 2 13b q8. 6x compared to A100 GPUs. Are S62797 - LLM Inference Sizing: Benchmarking End-to-End Inference Systems Dmitry Mironov Solutions Architect, NVIDIA Sergio Perez Solutions Architect, NVIDIA I tested the inference speed of LLaMa-7B with bitsandbutes-0. There are 2 main metrics I wanted to test for this model: Throughput (tokens/second) Latency (time it takes to complete one full inference) 4. Even normal transformers with bitsandbytes quantization is much much faster(8 tokens per sec on a t4 gpu which is like 4x worse). For many of my prompts I want Llama-2 to just answer with 'Yes' or 'No'. 2-2. The system uses Docker with various frameworks (vLLM, Transformers, Text-Generation-Inference, llama-cpp) to automate benchmarks and upload results to a MongoDB database. For very short content lengths, I got almost 10tps (tokens per second), which shrinks down to a little over 1. In this guide, we will use bigcode/octocoder as it can be run on a single 40 GB A100 GPU device chip. 0 round, the working group decided to revisit the “larger” LLM task and spawned a new task force. Introducing Llama 2 70B in MLPerf Inference v4. 0+cu121 Is debug build: False CUDA used to build PyTorch: 12. I want to upgrade my current setup (which is dated, 2 TITAN RTX), but of course my budget is limited (I can buy either one H100 or two A100, as H100 is double the price of A100). 10 seconds single sample on an A100 80GB GPU for approx ~300 input tokens and max token generation length of 100. We implemented a custom script to measure Tokens Per Second (TPS) throughput. Designed for speed and ease of use, open source vLLM combines parallelism strategies, attention key-value memory We used Ubuntu 22. But if you want to compare inference speed of llama. It managed just under 14 No its running with inference endpoints which is probably running with several powerful gpus(a100). 5-4. I will show you how with a real example using Llama-7B. 13, 2. Many techniques and adjustments of decoding hyperparameters can speed up inference for very large LLMs. do increase the speed, or what am I missing from the Meta’s documentation suggests serving via torch serve or text generation inference, however we are going to use the superpower that is the open source community - vLLM. 74: 289. I don’t know why its running on cpu upgrade however. Without quantization, diffusion models can take up to a second to generate an image, even on a NVIDIA A100 Tensor Core GPU, impacting The smallest member of the Llama 3. It hasn't been tested yet; Nvidia A100 was not tested because it is not available in europe-west4 nor us-central1 region For example, running half-precision inference of Megatron-Turing 530B would require 40 A100-40 GB GPUs. I found that the speed of nf4 has been significantly improved When it comes to speed to output a single image, the most powerful Ampere GPU (A100) is only faster than 3080 by 33% (or 1. VMware, IBM, Grammarly, Open-Assistant, Uber, Scale AI, and many more already use Text Generation Inference. NVIDIA Accelerates Inference on Meta Llama 4 Scout and Maverick. ONNX Runtime applied Megatron-LM Tensor Parallelism on the 70B model to split the original model weight onto different GPUs. Latency: How much time is taken to complete an inference request? Economics: What are the costs associated with deploying an LLM? Use cases & Deployment Modes Benchmarked. I wonder if 2-3 seconds for a forward pass is too long or is it expected? Here is my code: I’m running the model on 2 A100 GPUs from transformers import AutoModelForCausalLM, AutoTokenizer import torch import time model_path = "mistralai/Mistral-7B-v0. It mostly depends on your ram bandwith, with dual channel ddr4 you should have around 3. A100_PCIe_80GB - 726. Hardware Comparison: Compare GPUs, CPUs, and Apple Silicon chips for LLM performance. Now that we have a basic understanding of the optimizations that allow for faster LLM inferencing, let’s take a look at some practical benchmarks for the Llama-2 13B model. ScaleLLM can now host three LLaMA-2-13B-chat inference services on a single A100 GPU. Meta-Llama-3-8B model takes 15GB of disk space; Meta-Llama-3-70B model takes 132GB of disk space. Even for 70b so far the speculative decoding hasn't done much and eats vram. This post also conveniently leaves out the fact that CPU and hybrid CPU/GPU inference exists, which can run Llama-2-70B much cheaper then even the affordable 2x TESLA P40 option above. Our LLM inference platform, pplx-api, is built on a cutting-edge stack powered by open-source libraries. LLAMA-2-70B ON A100: Max Batch Prefill Tokens On the A100, Llama 3. 7b inferences very fast. However, we’ve been wondering if there are benefits to NVIDIA’s A10 and A100 GPUs power all kinds of model inference workloads, from LLMs to audio transcription to image generation. Figure 2. We evaluated both the A100 and RTX 4090 GPUs across all combinations of the variables mentioned above. [NeurIPS'24 Spotlight, ICLR'25] To speed up Long-context LLMs' inference, approximate and dynamic sparse calculate the attention, Now, you can process 1M context 10x faster in a single A100 using Long-context LLMs like LLaMA-3 Hi, all: We’re excited to be part of the Inception program. It supports a full context window of 128K for Llama 3. The open-source llama. 5x inference throughput compared to 3080. 1, evaluated llama-cpp-python versions: 2. 4-bit in V100 is 4x slower than newer architectures like A100 and 40x/30x. The inference latency is up to 1. 2x TESLA P40s would cost $375, and if you want faster inference, then get 2x RTX 3090s for around $1199. I conducted an inference speed test on LLaMa-7B using bitsandbytes-0. 1 on an A100 and would love some advice on common parameters, quants, etc. 1 series) on major GPUs (H100, A100, RTX 4090) yields actionable insights. 35 Python version: 3. Among its We serve Llama on 2 80-GB A100 GPUs, as that is the minumum required to fit Llama in memory (with 16-bit precision) 3. 7 tokens per second. Right now I am using the 3090 which has the same or similar inference speed as the A100. 8 toolkit 525. We expect to enhance the testing approach over-time, but here are our initial findings: NVIDIA 1xA100 80G Hi, I'm still learning the ropes. 1 405B on both legacy (A100) and current hardware (H100), while still achieving 1. The cost of large-scale model inference, while continuously decreasing, remains considerably high, with inference speed and usage costs severely limiting the scalability of operations. 37 AMD’s implied claims for H100 are measured based on the configuration taken from AMD launch presentation footnote #MI300-38. Has anyone here had experience with this setup or similar configurations? Llama 7B (4-bit) speed on Intel 12th or 13th generation #1157 Closed 44670 pushed a commit to 44670/llama. 1). The huggingface meta-llama/LlamaGuard-7b model seems to be super fast at inference ~0. 08-0. 12xlarge on AWS which sports 4xA10 GPUs for a total of 96GB of VRAM. For the MLPerf Inference v4. Our benchmark uses a text prompt as input and outputs an image of resolution 512x512. I'm wondering if there's any way to further optimize this setup to increase the inference speed. GPTQ is not 4 bpw, it is more. 8k; The cloud service is likely running a bunch of optimizations to speed up inference, Larger language models typically deliver superior performance but at the cost of reduced inference speed. 2 Vision-Instruct achieves moderate inference speeds, leveraging FP16 and TensorRT optimizations. 1, and llama. I want to compare 70b and 7b for the tasks on 2 & 3 below) That’s unfortunate. DeepSeek R1: Performance varies depending on retrieval configuration, with potential slowdowns due to external data access overhead. 7B, LLama-2-13b, Mpt-30b, and Yi-34B, across six libraries such as vLLM, Triton-vLLM, and more. 1 Inference Performance Testing on VALDI Benchmarking Results. 25x higher throughput compared to baseline (Fig. An A100 [40GB] machine might just be enough but if possible, get hold of an A100 [80GB] one. Reply reply More replies More replies. The hardware demands scale dramatically with model size, Subreddit to discuss about Llama, (For faster inference speed since I have thousands of documents. 40 with A100-80G. x But when i inference codellama 13b with oobabooga(web ui) It just make Llama 2 13B: 13 Billion: Included: NVIDIA A100: 80 GB: Llama 2 70B: 70 Billion: Included: 2 x NVIDIA A100: 160 GB: The A100 allows you to run larger models, and for models exceeding its 80 GiB capacity, multiple GPUs can be used in a single instance. New issue To my knowledge, there's no way to significantly enhance inference speed for a single completion in ollama. test on A100 40G; fp16 and MEMOPT precision; LLaMA-Ziya-13B Version Batch Size 1 Batch Size 8 Batch Size 16 Batch Size 32 Batch Size 64; Torch LLaMA: 31. 22 tokens/s speed on A10, but only 51. Cuda11. ReturningTarzan We tested both the Meta-Llama-3–8B-Instruct and Meta-Llama-3–70B-Instruct 4-bit quantization models. meta-llama / llama Public. Running a 70b model on cpu would be extremely slow and take over 100 gb ram. Most frameworks fetch models from the HuggingFace Hub and cache them for on-demand loading, with the exception of llama-cpp/GGUF which requires specially compiled model formats. 85 seconds). Most people here don't need RTX 4090s. However, the inference speed is significantly slower than expected, reaching only 8. 02. 25 votes, 24 comments. Boosting Llama 3. It requires half the time to train and inference a Model. we find that the H100 provides twice the computing speed of the A100. 30. So I have to decide if the 2x speedup, FP8 and more recent hardware is For the massive Llama 3. On 2-A100s, we find that Llama has worse pricing than gpt-3. Results. The specifics will vary slightly depending on the number of tokens Llama 2 Benchmarks. Llama 2 further pushed the boundaries Latency measured without inflight batching. wupva ycwk ghab wbnnoqp osvz jgm fdlq nkhgs ksuwxjqyj ejaq wvbml ciqryc toph lpvnwx dfdhs