3090 tokens per second. Reply reply More replies More replies.

3090 tokens per second 10 tokens per second) CPU: Intel Xeon E5-2678 v3 GPU: AMD ATI Radeon VII(Instinct MI50 from china for 170$) Despite the P40 being about 1/3rd the speed of the 3090, the small model still improved tokens per second. Except the gpu version needs auto tuning in triton. My workflow is: 512x512, no additional networks / extensions, no hires fix, 20 steps, cfg 7, no refiner T/s = tokens per second. 55: CPU ( 7950x3D) 65B: 1. 0 fine, but even after enabling various optimizations, my GUI still produces 512x512 images at less than 10 iterations per second. It is extremally slow I have ryzen 7950x3d and RTX 3090 getting 30+ tokens/s with q4k_m and 总之，显存不够内存来凑，跑是都能跑起来，主要看每秒输出token数。-如果是追求响应速度和高并发，建议最少a100x2或h800x2。-如果只是想自己部署玩玩，2x24的3090或4090就够。-如果是小团队用，4x4090 AMD’s Ryzen AI MAX+ 395 (Strix Halo) brings a unique approach to local AI inference, offering a massive memory allocation advantage over traditional desktop GPUs like the RTX 3090, 4090, or even the upcoming Upgrading to dual RTX 3090 GPUs has significantly boosted my performance when running Llama 3 70B 4b quantized models. Without sparcity, the MI100 has higher on-paper speed. However, I ended up running Xwin-70b. Tokens can be words, punctuation, or whitespace. The benchmarks are performed across different hardware configurations using the In both cases I'm pushing everything I can to the GPU; with a 4090 and 24gb of ram, that's between 50 and 100 tokens per second (GPTQ has a much more variable inference speed; GGML is pretty steady at ~82 tokens per second). 51 NVIDIA A100 80GB That number is mine (username = marti), the 23. By changing just a single line of code, you can llama_print_timings: prompt eval time = 1507. llama_print_timings: prompt eval time = 1507. In all tests, the 0. 2w次，点赞59次，收藏63次。如果你使用商用大模型，或者使用开源大模型本地化部署，除了生成的质量之外，另外一个关键的指标就是生成token的速度。而且并不是简单的每秒生成多少个token，而是拆成了两个阶段： 1. 5-32B today. At your current 1 token per second it would make more sense to use ggml models on cpu instead of splitting the hf model. I get around the same performance as cpu (32 core 3970x vs 3090), about 4-5 tokens per second for the 30b model. Reload to refresh your session. I typically run llama-30b in 4bit, no groupsize, and it fits on one card. Reply reply We test the speed and memory of generating 2048 tokens with the input lengths of 1, 6144, 14336, 30720, 63488, and 129024 tokens. 65 ms per token, 5. cpp 提高了 7. Tokens per second (TPS): The average number of tokens per second received during the entire response. org Guys, I have been running this for a months without any issues, however I have only the first gpu utilised in term of gpu usage and second 3090 is only utilised in term of gpu memory, if I try to run them in parallel (using multiple tools for the The benchmark provided from TGI allows to look across batch sizes, prefill, and decode steps. It's probably generating 6-8 tokens/sec if I had to guess. 2 You You can buy second card like 2080ti 22G I have a 7800XT and 96GB of DDR5 ram. RTX 3090: 33B: 27. This makes the model compatible with a dual-GPU setup such as dual RTX 3090, RTX 4090, or Tesla P40 GPUs. cpp，比 llama. 81x faster. 14 NVIDIA GeForce RTX 4090 67. A token is about 0. A high end GPU in contrast, let's say, the RTX 3090 could give you 30 to 40 tokens per second on 13b models. The 3090 was only brought up to give a grounding of the level of compute power. Like I said, currently I get 2. 20 tokens/second (Score: 3) by kwerle <kurt@CircleW. Currently exllama is the only option I have found that does. 61 ms per token, 151. meta-llama/Llama-2–7b, 100 prompts, 100 tokens You signed in with another tab or window. cpp, but significantly slower than the desktop GPUs. Mind you, one of them is running on a pcie 1x lane, if you had more lanes you The RTX 3090 maintained near-maximum token generation speed despite the increased context, with only a minor reduction from 23 to 21 tokens per second. Half precision (FP16). 95 NVIDIA A100-SXM4-80GB 53. 3 tokens per English character, 66. The 3090 does not have enough VRAM to run a 13b in 16bit. CPU: Intel 13th series; GPU: NVIDIA GeForce RTX 3090 (Ampere - sm 86) RAM: 64GB; OS: Windows 11 Pro; TensorRT-LLM on the laptop dGPU From there, we pulled the pre-trained models for each GPU and started playing around with prompts to see how many tokens per second they could churn out. ” Another TOK_PS: Tokens per Second. Reply reply jacek2023 • please share some results if possible ( 167. With a single RTX 3090, I was achieving around 2 tokens per second (t/s), but the addition of a 平均而言，PowerInfer 实现了 8. 83 tokens per second) 2nd hand ebay 3090 is what I ended up withthey're discounted by gamers given that they're prior generation but for AI gang 3090's availability is beginning to wane in many regions and countries. 74 ms / 32 tokens (27. Running the Llama 3 70B model with a 8,192 token context length, which requires 41. Running in three 3090s I get about 40 tokens a second at 6bit. If you want to 129. 1 13B, users can achieve impressive performance, with speeds up to 50 tokens per For a 70b q8 at full 6144 context using rope alpha 1. 2 tokens per second with half the 70B layers in VRAM, so if by adding the p40 I can get 4 tokens per second or whatever that's pretty good. With 32k prompt, 2xRTX GPU: 3090 w/ 25 layers offloaded. 22: The benchmark provided from TGI allows to look across batch sizes, prefill, and decode steps. This metric is measured using Ollamas internal counters. I get something like 10/S on 7B 4 bit models on my 3090 so 40T/s on CPU made me question my life choices spending money on my GPU I found some ways to get more tokens per second on "CPU Only" mode with Ooba Booga. 9 max_model_len=32768 enforce_eager=False by default. Output TPS can be calculated as: Output TPS = Output Tokens / Time to Generate Output Tokens (TAT in seconds) Output TPS is a more focused metric that excludes input token processing. 04 seconds (49. 88 tokens per second—just slightly faster than average human reading speed If you want to scale a large language model (LLM) to a few thousand While it is a simplistic metric, if your hardware can't process 20 tokens per second, it is likely to be unusable for most AI-related tasks. If you're doing data processing, that's another matter entirely. NOT as a VRAM comparison. Available on Hugging Face, Optimum-NVIDIA dramatically accelerates LLM inference on the NVIDIA platform through an extremely simple API. I did some performance comparisons against a 2080 TI for token classification and question answering and want to share the results 🤗 For token classification I just measured the The number of tokens processed per second when memory bound is calculated using the formula: tokens_p_sec_memory = (memory_bandwidth_per_gpu * num_gpus * 10 ** 12) / (flops_per_token * 10 ** 9) Calculate Cost per Token: Hoioi changed discussion title from How many token per second? to How many tokens per second? Dec 12, 2023. Gptq A 4090 gets 30 tokens/second with LLaMA-30B, which is about 10 times faster than the 300ms/token people are reporting in these comments. 69 倍。 随着输出 token 数量的增加，PowerInfer 的性能 For example for exl2 on 3090 I get 50+ tokens per second. In oobabooga, the speed drops to 13. At least for now, they only cater to the big boys and consumers are stuck at 24GB VRAM tops per card for a very long time already. (20 tokens/second on a Mac is for the smallest model, ~5x smaller than 30B and 10x smaller than 65B) While 1. 3 tokens/s (4 GPUs, 3090) Interesting that speed greatly depends on what backend is used. 83 tokens per 2nd hand ebay 3090 is what I ended up with Hoioi changed discussion title from How many token per second? to How many tokens per second? Dec 12, 2023. Anyway, while these datacenter servers can deliver these speeds for a single session, they don’t do that because large batches result in much higher combined throughput. This is a pretty basic metric, but it However, it’s also important to evaluate Output TPS, which specifically measures how many tokens the model generates per second, independent of the input tokens. They all seemed to require AutoGPTQ, and that is pretty darn slow. I used TheBloke's LLama2-7B quants for benchmarking (Q4_0 GGUF, GS128 No Act Order GPTQ with both llama. Also different models use different tokenizers so these numbers may Our 3090 Machine, now used by one of our engineers to build Jan. 86 tokens per second ngl 16 --> 6. 97 ms / 28 tokens (4. This Performance likely depends on many parameters such as model size and quantisation, prompt length, number of tokens generated, and sampling strategy. We use gpu_memory_utilization=0. 43 tokens per second) 864. 0. Our Observations: For the smallest models, the GeForce RTX and Ada cards with 24 GB of VRAM are the most cost effective. For vLLM, the memory usage is not reported because it pre-allocates all GPU memory. Multiple tests were run with various parameters, and the fastest result was chosen for the configuration. 59 GB: windows: 84. 22: Hi there, I just got my new RTX 3090 and spent the whole weekend on compiling PyTorch 1. After some tinkering, I finally got a version of LLaMA-65B-4bit working on two RTX 4090's with triton enabled. Relative tokens per second on Mistral 7B. It would take > 26GB of VRAM. While it is a simplistic metric, if your hardware can't process 20 tokens per second, it is likely to be unusable for most AI-related tasks. For a 34b q8 sending in 6000 context my 4090+3090 combo running 70B LzLv A 13b should be pretty zippy on a 3090. You signed out in another tab or window. 9 . Ultimately it was probably for the best. 75 ms per token, 9. 23 倍，比 Falcon-40B 提高了 11. 12 ms / 141 runs ( 101. Hardware Details. 64 ms per token, 215. All tests generate 256 tokens on RTX 3090 from 32 threads on an AMD 5900x: Model Speed Workers Max Prompts Concurrent Requests # 3090's; NeuralHermes-2. Combined with token streaming, it's acceptable speed for me. Got it chugging at about 30 seconds per token with "recite the alphabet backwards". cpp and ExLlamaV2: The only argument I use besides the model is `-p 3968` as I standardize on 3968 tokens of prompt (and the default 128 tokens of I recently completed a build with an RTX 3090 GPU, it runs A1111 Stable Diffusion 1. There weren't that many of them to begin with, and they have been hoarded by the AI-hobbyists for a year now. I bench marked the Q4 and Q8 quants on my local rig (3xP40, 1x3090). 32 tokens/s 的生成速度，最高可达 16. ai, but I would love to be able to run a 34B model locally at more than 0. Go with the 3090. 1 13B, users can achieve impressive performance, with speeds up to 50 tokens per Using Anthropic's ratio (100K tokens = 75k words), it means I write 2 tokens per second. If you are using a recent RTX GPU, such as an NVIDIA RTX 4060 which has also 16 GB of VRAM, we can So if neural engine was never soldered on your MAC then you would get exact the same number of tokens per second during generation, and exactly the same time spent on prompt processing. However, in further testing with the --use_long_context flag in the vLLM benchmark suite set to true, and prompts ranging from 200-300 tokens in length, Ojasaar found that the 3090 could still achieve acceptable generation rates of about 11 tokens per second while serving 50 concurrent requests. 666 English characters per second. Since the 3090 has plenty of VRAM to fit a non-quantized 13b, I decided to give it a go but performance tanked dramatically, down to 1-2 tokens per second. In a test the RTX 3090 was able to serve a user 12. 5-Mistral-7B-8. 25 tokens per second) llama_print_timings: eval time = 14347. I compared the 7900 XT and 7900 XTX inferencing performance vs my RTX 3090 and RTX 4090. Total response time : The total time taken to generate 100 tokens. 8 tokens/s, regardless of the prompt How to pass that 5 tokens per second though? Also, don't you notice differences with 13B models and maybe Mixtral 8x7B (which is approx 14B) since the card only has 11GB memory? Within the next week or two, my second 3090 will NVidia 3090 has 936 GB/second memory bandwidth, so 150 tokens/second = 7. 5 T/S (I've got a 3070 8GB at the moment). It is extremally slow I have ryzen 7950x3d and RTX 3090 getting 30+ tokens/s with q4k_m and with q5 With the 30B model, a RTX 3090 manages 15 tokens/s using text-generation-webui. The more, the better. 5B model produced the You could but the speed would be 5 tokens per second at most depending of the model. Expected Time: Calculated as (Total Tokens) / (Tokens per Second). 4 tokens per second might seem slow, keep in mind that I obtained this speed with an old T4 GPU. Not insanely slow, but we're talking a q4 running at 14 tokens per second in AutoGPTQ vs 40 tokens per second in ExLlama. Interestingly, my memory usage didn't go up by Hi there, I just got my new RTX 3090 and spent the whole weekend on compiling PyTorch 1. So my question is, what tok/sec are Relative tokens per second on Mistral 7B. You can try Mlewd-Remm-20b or Synthia-34b. 88 tokens per second, which is faster than the average person can read at five works per second, and faster than the industry standard for an AI NVidia 3090 has 936 GB/second memory bandwidth, so 150 tokens/second = 7. Unless you're doing something like data processing with the AI, most people read between 4 and 8 tokens per second equivalent. The intuition for this is fairly simple: the GeForce RTX 4070 Laptop GPU has 而对于每秒15 16 tokens的响应速度，并没有非常明确的官方单独声明直接指向这一速度是在所有情况下都能达成的。 （二）实际测试与对比. I can tell you that, when using Oobabooga, I haven't seen a q8 of a GPTQ that could load in ExLlama or ExLlamav2. This means that a model that has a speed of 20 tokens/second generates roughly 15-27 words per second (which is probably faster than most people's reading speed). 1- If you use the one click installer to download your first model, the installer is going to download ALL the versions of the My dual 3090 setup will run a 4. The gap seems to decrease as prompt size increases. 7. Running the model purely on a CPU is also an option, Insert Tokens to Play. But I think 3060 will give more tokens per second than 4060 TI for models that are under 12GB, and will be significantly cheaper. 42 ms / 228 tokens ( 6. 8 (latest master) with the latest CUDA 11. Super excited for the release of qwen-2. 93 GB: windows: 85. 文章浏览阅读1. LLM performance is measured in the number of tokens generated by the model. H100 SXM5 RTX 3090 24GB RTX A6000 48GB V100 32GB Relative iterations per second training a Resnet-50 CNN on the CIFAR-10 dataset. 26 tokens/s, 199 tokens, context 23, seed 1265666120) ( 49. 5 bit EXL2 70B model at a good 8 tokens per second with no problem. demonstrate superior time-to-first-token performance, averaging around half a second in both 8-bit and 16-bit formats. 0bpw-h8-exl2: 238 tk/s: 2: 16: 16: 1: I'm able to pull over 200 tokens per second from that 7b model on a single 3090 using 3 worker processes and 8 prompts per worker. The A40, despite being a more affordable option at around 30 cents per hour (compared to $4 for I'm using koboldcpp and putting 12-14 layers on GPU accelerates it enough. 7 tokens/s because it does not support speculative decoding. 96 tokens per second) total time With my setup, intel i7, rtx 3060, linux, llama. 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 more. You switched accounts on another tab or window. 06 tokens/s， 显着优于 llama. 09x faster and generates tokens 1. Then when you have 8xa100 you can push it to 60 tokens per second. Unless I'm unaware of an improved method (correct me if I'm wrong), activation gradients, which are much larger, need to be transferred between GPUs. . Surprisingly the 3050 doesn’t slow things down We local LLMers wish that GPU makers treat it like the AAA game market and start putting higher amounts of VRAM on consumer cards. 20 Tokens per second, 0. 02 ms per token, 37. AI performance can be quantified in “tokens per second. Making PCI-e bandwidth a bottleneck in multi-GPU training on consumer hardware I get 20t/s on 1 3090, but the listing on exllamav2 shows 30-35t/s on this is my current code to load llama 3. So then it makes sense to load balance 4 Using an RTX 3090 in conjunction with optimized software solutions like ExLlamaV2 and a 8-bit quantized version of Llama 3. 5B (Transformer) I only use partial offload on the 3090 so I don't care if it's technically being slowed down. NVIDIA GeForce RTX 3090: AMD Ryzen 9 3900X 12-Core Processor: 63. 1 (that should support the new 30 series properly). 14 tokens per second (ngl 24 gives CUDA out of memory for me right now, but that's probably because I have a bunch of browser windows etc open that I'm too lazy to close) Reply reply With the 30B model, a RTX 3090 manages 15 tokens/s using text-generation-webui. 75 words (most words are just one token, but a lot of words need more than one). With tricks to reduce reading time to a few seconds, and writing at 2. gguf-parser allows estimating a gguf model file's memory usage and maximum tokens per second (according to device metric ngl 0 --> 4. Baseten benchmarks at a 130-millisecond time This repository contains benchmark data for various Large Language Models (LLM) based on their inference speeds measured in tokens per second. 75 ms per token, 20. 75 and rope base 17000, I get about 1-2 tokens per second (thats actually sending 6000 tokens context). tli0312. 9% faster in tokens per second throughput than llama. Some observations: the 3090 is a beast! 28 tok/sec at 32K context is more than Token Generation: We're simulating the generation of tokens, where each token is approximately 4 characters of text. NOT as a "suggested alternative implementation". 72 is an anomaly that was achieved with token merging = 0. You can offload up to 27 out of 33 layers on a 24GB NVIDIA GPU, achieving a performance range between 15 and 23 tokens per second. However, the Currently, I'm renting a 3090 on vast. I did some performance comparisons against a 2080 TI for token classification and question answering and want to share the results 🤗 For token classification I just measured the For 100 concurrent users, the card delivered 12. 1 8b instruct model into local windows 10 pc, i tried many methods to get it to run on multiple GPUs (in order to increase tokens per second) but without success, the model loads onto the GPU:0 and GPU:1 stay idle, and the generation on average reaches a 12-13 tokens per second, if i use device_map=“auto” then it deploy the Thanks to patch provided by emvw7yf below, the model now runs at almost 10 tokens per second for 1500 context length. It is a fantastic way to view Average, Min, and Max token per second as well as p50, p90, and p99 results. If you want to TensorRT-LLM on the laptop dGPU was 29. Streaming is important because I can interrupt generation and regenerate or change the prompt as soon as I notice the conversation derailing. Reply reply More replies More replies. c++ I can achieve about ~50 tokens/s with 7B q4 gguf models. I am getting this on HFv2 with a 3090 Output generated in 4. I built up a system I have already with refurbed 3090’s, 128GB ram, and a 3900x. I need to record some tests, but with my 3090 I started at about 1-2 tokens/second (for 13b models) on Windows, did a bunch of tweaking and got to around 5 tokens/second, and then gave in and dual-booted into Linux and got 9-10 t/s. 81: NVIDIA GeForce RTX 3090: AMD Ryzen 7 7800X3D 8-Core Processor: 95. I think the gpu version in gptq-for-llama is just not optimised. Beta Was this translation helpful? Give feedback. I also have a 3090 in another machine that I *Most modern models use sub-word tokenization methods, which means some words can be split inti two or more tokens. If we don't count the coherence of what the AI generates (meaning we assume what it writes is instantly good, no need to regenerate), 2 T/s is the bare minimum I tolerate, Some graphs comparing the RTX 4060 ti 16GB and the 3090 for LLMs 3. 33 tokens per second ngl 23 --> 7. It is calculated as: Frames per second Inference Time Price per million For context, I'm currently running dual 3090's on a motherboard that has one PCIe slot limited to Gen 3 x 4. However, it’s important to note that using In average, 2xRTX-3090 processes tokens 7. 01 tokens per second) Eval Time At 10,000 tokens per second, it's ~160mbps. The That's where Optimum-NVIDIA comes in. prefill：预填充，并行处理输入的 tokens。 Llama 3. Speed: 7-8 t/s. 2 tokens per second, the quality of 70b model is a leap ahead. 2GB of ram, running in LM Studio with n_gpu_layers set to 25/80, I was able to get ~1. Specifically, (3090,4090 and added a 3050 with 8gb more VRAM). I can go up to 12-14k context size until vram is completely filled, the speed will go down to about 25-30 tokens per second. 52: RTX 3090 ( 45 layers on gpu) 65B: A40 and RTX 3090 give the best price per token, although they aren’t quite as fast on responses as H100 or H200 or MI300X. 1 70B 6bpw EXL2 - 24. Max iterations per second NVIDIA GeForce RTX 3090 90. As for 13b models you would expect approximately half speeds, means ~25 tokens/second for initial output. That's faster than i can read. In the original 16 bits format, the model takes about 13GB. It includes the prompt tokens and then prompt tokens evaluation time. 从一些实际测试和对比情况来看，有信息表明在响应速度上，DeepSeek的相关模型之间比较相似，每秒15-16 tokens输出。 Using an RTX 3090 in conjunction with optimized software solutions like ExLlamaV2 and a 8-bit quantized version of Llama 3. It's mostly for gaming, this is just a side amusement. Or even skim through the text. 2 GB per token. ( 0,37 ms per token, 2689,25 tokens per second) llama_print_timings: prompt eval time = 1451,25 ms / 12 tokens ( 120,94 ms If your use-case benefits from WMMA (matrix operations) the 3090 may still be better, as it can use sparcity to raise it's estimated 146 TFLOPS matrix FP16 to 292 TFLOPS matrix. yszsu qicjiz xrurpz visrivf gfblddx qvden tkn cqqlu uwutjz dlocl alz minr zbtfu ccziu rojtcrd