3090 tokens per second Private chat with local GPT with document, images, video, etc. If you want to learn more about how to conduct benchmarks via TGI, reach out we would be happy to help. Q4_K_M. 5 Tokens per Second) Discussion Share Add a Comment. 0. 8 tokens per second. S> Thanks to Sergey Zinchenko added the 4th config Benchmarking Llama 3. 1 13B, users can achieve impressive performance, with speeds up to 50 tokens per second. I was using oobabooga on Windows, had 4090+3090 running exl 70b airoboros at 13. If you're doing data processing, that's another matter entirely. This level of performance brings near real-time interactions within reach for home users. Compare this to the TGW API that was doing about 60 t/s. 75 and rope base 17000, I get about 1-2 tokens per second (thats actually sending 6000 tokens context). 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. For example, a system with DDR5-5600 offering around 90 GBps could be enough. 1 8B benchmark on differet GPUs, the RTX 3090 delivered the best cost-performance - 1 Million output tokens for just $0. 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 In a test the RTX 3090 was able to serve a user 12. 31 ms per token, 2. The creators of vLLM from UC Berkeley identified memory as the primary bottleneck impeding LLM performance. 38 tokens per second) Reply reply The benchmark provided from TGI allows to look across batch sizes, prefill, and decode steps. Running in three 3090s I get about 40 tokens a second at 6bit. Hardware Details. Lower latency means faster responses, which is especially critical for real-time 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. 96 tokens per second) total time = 101663. 77 tokens per second with llama. H100 SXM5 RTX 3090 24GB RTX A6000 48GB V100 32GB Relative iterations per second training a Resnet-50 CNN on the CIFAR-10 dataset. Supposedly this supports Ampere architecture, which I believe is what the rtx3090 is. 02 had a higher throughput and K,V cache usage than 450. Didn’t try to get some code. Hey there! As you know, large language models (LLMs) have become a hot topic in the AI world lately, and rightfully so. 12 ms / 255 runs ( 106. A token can be a word in a sentence, or even a smaller fragment like punctuation or whitespace. In oobabooga, gguf-parser allows estimating a gguf model file's memory usage and maximum tokens per second (according to device metric) rtx 3090 has 935. Runs great. h2o. That's faster than i can read. 72 is an anomaly that was achieved with token merging = 0. The tokenizer is a small model With tricks to reduce reading time to a few seconds, and writing at 2. 399 4060 ti: 1. The DDR5-6400 RAM can provide up to 100 GB/s. Dear all, While comparing TheBloke/Wizard-Vicuna-13B-GPTQ with TheBloke/Wizard-Vicuna-13B-GGML, I get about the same generation times for GPTQ 4bit, 128 group size, no act order; and GGML, q4_K_M. 102. Tried using the HF docker container and I cannot get it to run at all. 52 ms per token, 15. 8 gb/s rtx 4090 has 1008 gb/s wikipedia. I’m using I think the gpu version in gptq-for-llama is just not optimised. 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. The author's claimed speed for 3090 Ti + 4090 is 20 tokens/s. 8 (latest master) with the latest CUDA 11. 037 seconds per token Intel(R) Xeon(R) Platinum 8358 CPU @ 2. 9 . 04. My Ryzen 5 3600: LLaMA 13b: 1 token per second My RTX 3060: LLaMA 13b 4bit: 18 tokens per second So far with the 3060's 12GB I can train a LoRA for the 7b 4-bit only. 60GHz :: 0. 86 tokens/sec with 20 input tokens and 100 output tokens. Not insanely slow, but we're talking a q4 running at 14 tokens per second in AutoGPTQ vs 40 tokens per second in ExLlama. Overview. Reply reply More replies More replies. You could but the speed would be 5 tokens per second at most depending of the model. I test vicuna-7b(whose performace should similar to llama-7b) on 3090 24G, and got 2. 0-cudnn8-devel-ubuntu20. 75 T/S ngl 0 --> 4. 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: The cost per token for both compute and memory bounds is determined using the formula: We selected ‘tokens per second’ (tokens/s) as this metric. I've been reading a post in the LocalLLaMA sub and I saw that someone is getting 15-20 tokens per second using a 4bit 13b model with an nvidia 3060 graphics card. 19500 MHz), but has lower bandwidth (695. 61 ms / 200 runs ( 0. 74 X faster and the 3090 is ~5 X faster (than the 1080 ti) Reply With mistral 7b FP16 and 100/200 concurrent requests I got 2500 llama_print_timings: sample time = 45. Surprisingly the 3050 doesn’t slow things down. m2 ultra has 800 gb/s m2 max has 400 gb/s 65B model fairly comfortably on a 4090+CPU situation, but too much ends up on CPU side, and it is only worth about 3-4 tokens per I'm able to consistently get about 1. However, there are many used/second-hand RTX 3090 options that can bring the total system price down quite a bit. 69 tokens per second) Epyc 7402 8x16GB ECC 2133MHz: 288 runs ( 436. I also have a 3060, but my output speed seems capped just under 5 tokens/second running Using an RTX 3090 in conjunction with optimized software solutions like ExLlamaV2 and a 8-bit quantized version of Llama 3. 15 votes, 35 comments. 61 tokens per second, which is nearly the same at 128 concurrent requests. For basic LoRA and QLoRA training the 7900XTX is not too far off from a 3090, although the 3090 still trains 25% faster, and uses a few percent less memory with the same settings. I've heard others using this repo achieving around 12t/s which is considerably faster. After some tinkering, I finally got a version of LLaMA-65B-4bit working on two RTX 4090's with (3090,4090 and added a 3050 with 8gb more VRAM). However, it’s important to note that using GPU: 3090 w/ 25 layers offloaded. To compare performance of GPUs at common image processing tasks we used the Ultralytics YOLOv8 models in medium, large and extra large sizes. 3 seconds per iteration depending on prompt. 66 request/s 1269. 41: 112: NVIDIA RTX A6000: 3. Or even skim through the text. A 4090 should cough Hi. This setup, while slower than a fully GPU-loaded model, still manages a token generation rate of 5 to 6 tokens per second. While that's faster than the average person can read, generally said to be In this Llama 3. 0 fine, but even after enabling various optimizations, my GUI still produces 512x512 images at less than 10 iterations per second. 76 ms / 11 tokens ( 64. Comparing the RTX 4070 Ti and RTX 4070 Ti SUPER Moving to the RTX 4070 Ti, the performance in running LLMs is remarkably similar to the RTX 4070, largely due to their identical memory bandwidth of 504 GB/s. 2x if you use int4 quantisation. It shows how many tokens (or words) a model can process in one second. It's mostly for gaming, this is just a side amusement. 27 ms per token, 3690. Latency: This metric indicates the delay between input and output. It is a fantastic way to view Average, Min, and Max token per second as well as p50, p90, and p99 results. 7. cpp, and more. 02 and 450. For 1080p games, upgrading from GTX 1070 to RTX 3090 is not worth it if Just got a second 3090, what models are you guys running with all this VRAM? Discussion I am curious to know what models people with 48GB of VRAM are running. A high end GPU in contrast, let's say, the RTX 3090 could give you 30 to 40 tokens per second on 13b models. Once you take Unsloth into account though, the difference starts to get quite large. I only get around 3t/s with my RTX 3090 (GPU is being detected) on a 13b Vicuna model. 04 docker image, logs shown that 530. 0. 63 ms per token, 2. But I didn’t have excellent results following instruction, I’m waiting for a finetuned version. When comparing performance across two different LLMs, you need to adjust TPS based on the models’ tokenizers. 100% private, Apache 2. gguf: codellama-34b. 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 Thanks to patch provided by emvw7yf below, the model now runs at almost 10 tokens per second for 1500 context length. This ruled out the RTX 3090. A more powerful GPU (with faster memory) should easily be able to crack 200 tokens per second at In a benchmark simulating 100 concurrent users, Backprop found the card was able to serve the model to each user at 12. 1 8B (fp16) on our 1x RTX 3090 instance suggests that it can support apps with thousands of users by achieving reasonable tokens per second at 100+ concurrent requests. 05 *[TG]- tokens generated; [PP] - prompt processing; Can you tell me which LLM-app you're using and how much tokens per second you get? Cause I am using OpenBLAS, but I didn't see an option for AVX1 or 2 or 512 in LM Studio or KoboldCPP. 8 tokens/s), so we don't benchmark it. Hi there, I just got my new RTX 3090 and spent the whole weekend on compiling PyTorch 1. I set the swappiness to 1, but still, I only get 0,30 tokens per second :( It just hit me that while an average persons types 30~40 words per minute, RTX 4060 at 38 tokens/second (roughly 30 words per second) achieves 1800 WPM. that's Tokens per Second (T/s): This is perhaps the most critical metric. Gptq-triton runs faster. You can also train models on the 4090s. 042 seconds per token With default cuBLAS GPU acceleration, the 7B model clocked in at approximately 9. 138 So, the 4060 ti is about 2. 66: 148. 3 tokens per second. (20 tokens/second on a Mac is for the smallest model, ~5x smaller than 30B and 10x smaller than 65B) On my 3090, I get 50 t/s and can fit 10k with the kV cache in vram. Supports oLLaMa, Mixtral, llama. 228. I can tell you that, when using Oobabooga, I haven't seen a q8 of a GPTQ that could load in ExLlama or ExLlamav2. I recently completed a build with an RTX 3090 GPU, it runs A1111 Stable Diffusion 1. Performance for AI-accelerated tasks can be measured in “tokens per second. Constants. 1 Instruct 8B and comparison to other AI models across key metrics including quality, price, performance (tokens per second & time to first token), context window & more. I think two 4090s can easily output 25-30 tokens/s 8. Reply reply jacek2023 • ( 167. 28% in tokens per second compared to vLLM. Downsides are higher cost ($4500+ for you’ll need two of these cards for a total GPU cost of $3000. Well, number of tokens per second from an LLM would be an indicator, or the time it To achieve a higher inference speed, say 16 tokens per second, you would need more bandwidth. I am not sure what I am doing wrong but I get around 2 tokens/per sec The A100 chip is optimal at 64 concurrent requests with a maximum throughput of 2. Tokens per second (TPS): The average number of tokens per second received during the entire response. I used TheBloke's LLama2-7B quants for benchmarking (Q4_0 GGUF, GS128 No Act Order GPTQ with 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 inference I am running inference on a 30B model and wanting 35 tokens per second from benchmarks but am only seeing about 20 tokens / second. Let me know what models I should try out. 23 ms per token, 4385. Combined with token streaming, it's acceptable speed for me. I tested a GGUF Q4 70b model and I was getting between 1. Suffice to say, if you're deciding between a 7900XTX for $900 or a used RTX 3090 for $700-800, the latter I think is Mixtral 8x22B on M3 Max, 128GB RAM at 4-bit quantization (4. 1 70B 6bpw EXL2 - 24. 30. 25 tokens per second) llama_print_timings: eval time = 27193. They’ve been achieving impressive results in various tasks, from text *heavily quantized, and with a few tokens per second, and will push your ram to its limits if you use Chrome at the same time, and will eat your disk space because of course you have to try all the quantization methods and bits per weight, and will make you doubt your sanity because output quality heavily depends on luck, and then you will To achieve a higher inference speed, say 16 tokens per second, you would need more bandwidth. cpp compiled with -DLLAMA_METAL=1 A 13b should be pretty zippy on a 3090. The situation is compensated by the larger number of CUDA cores This is a very simple script, only generating details about tokens per second. Analysis of Meta's Llama 3. I get near instantaneous responses at 12 tokens per second. 14 NVIDIA GeForce RTX 4090 67. 65 ms per token, 5. The RTX 3090 24GB stood out with 99. Sort by: Best. And thanks for the tip with the swappiness. The more, 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. 9% faster in tokens per second throughput than llama. 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 Tokens per second ; NVIDIA RTX A5000: 3. Baseten benchmarks at a 130-millisecond time to first token with 170 tokens per second and a total response time of 700 milliseconds for Mistral 7B, solidly in the most attractive quadrant for TPS: Tokens Per Second. 04 in nvidia/cuda:11. Performance likely depends on many parameters such as model size and quantisation, prompt length, number of tokens generated, and sampling strategy. Streaming is important because I can interrupt generation and regenerate or change the prompt as soon as I notice the conversation derailing. 50 tokens per second) llama_print_timings: eval time = 19700. For comparison, high-end GPUs like the Nvidia RTX 3090 boast nearly 930 GBps of bandwidth for their VRAM. Like I said, currently I get 2. 86 tokens per second ngl 16 --> 6. is there some trick you guys are using to get them to It seems the M1, M2 and M3 all have similar tokens per second as there is not a significant improvement 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 Max iterations per second NVIDIA GeForce RTX 3090 90. 48 votes, 84 comments. ai , but I would love to be able to run a 34B model locally at more than 0. bitsandbytes is very slow (int8 6. QPS: Queries Per Second. TOPS is only the beginning of the story. 983% of requests successful, and generating over 1700 tokens per second across the cluster with 35 concurrent users, which comes out to a cost of just $0. A30 Insert Tokens to Play. My dual 3090 setup will run a 4. Unless you're doing something like data processing with the AI, most people read between 4 and 8 tokens per second equivalent. 64 ms per token, 9. Demo: https://gpt. 3 tokens/s (4 GPUs, 3090) Interesting that speed greatly depends on what backend is used. Running the model purely on a CPU is also an option, RTX 3090 24 GB: 22/33: 512 tokens: 14. Reply reply PacmanIncarnate • and you should left with ~500MB free VRAM and speeds at 11tk/s (don't think 3090 vs 4090 differ that much here). 9 max_model_len=32768 enforce_eager=False by default. 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. It’s important to note that a token ≠ a word. The 3090 does not have enough VRAM to run a 13b in 16bit. 00 tokens per second) llama_print I get about 4 token/s on q3_K_S 70b models @ 52/83 layers on GPU with a 7950X + 3090. 58: 107: NVIDIA A40: 3. gguf: Those 3090 numbers look really bad, like really really bad. 8 GB/s versus 936. AI Model. The more, the better. The 13B version, using default cuBLAS GPU acceleration, returned approximately 5. Applying these metrics, a single NVIDIA H200 Tensor Core GPU generated about 3,000 tokens/second — enough to serve about 300 simultaneous users — in an initial test using the version of Llama 3 with 70 I'm using koboldcpp and putting 12-14 layers on GPU accelerates it enough. However, GTX 1070 is still capable of producing more than 60 frames per second. 094 3090: 2. 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. meta-llama/Llama-2–7b, 100 prompts, 100 tokens Relative tokens per second on Mistral 7B. 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 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. 2 tokens per second, the quality of 70b model is a leap ahead. 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 'train_samples_per_second': 1080 ti: 0. ai 1 iteration per second, dropping to about 1. 95 NVIDIA A100-SXM4-80GB 53. 5B (Transformer) I'm curious about how to calculate the token generation rate per second of a Large Language Model (LLM) Say, for 3090 and llama2-7b you get: 936GB/s bandwidth; 7B INT8 parameters ~ 7Gb vram; ~= 132 tokens/second This is 132 generated tokens for greedy search. For a 34b q8 sending in 6000 context my 4090+3090 combo running 70B LzLv 4KM at 6k context GGUF gives me about 8. 42 ms Output generated in 102. Compared to the RTX 3090, this memory is slightly faster (20000 MHz vs. The following factors would influence the key metrics, so we kept them consistent across different trials of the experiment. 87 tokens/s, faster than your A100 40G. 1 (that should support the new 30 series properly). 33 tokens per second ngl 23 --> 7. LLM performance is measured in the number of tokens generated by the model. This represents a slight improvement of approximately 3. vLLM Improves Memory Utilisation with PagedAttention. A q4 meta-llama/Llama-2–7b, 100 prompts, 100 tokens generated per prompt, batch size 16, 1–5x NVIDIA GeForce RTX 3090 (power cap 290 W) Summary This repository contains benchmark data for various Large Language Models (LLM) based on their inference speeds measured in tokens per second. 72 ms per token, 48. FTL: First Token Latency, measured in milliseconds. However, when comparing to Llama 2 and Mistral, regardless of the libraries used, Gemma I only use partial offload on the 3090 so I don't care if it's technically being slowed down. 52 Such a service needs to deliver tokens — the rough equivalent of words to an LLM — at about twice a user’s reading speed which is about 10 tokens/second. 8. It is meant for reuse and to serve as a base for extension. So it takes about 50 seconds per image on defaults for everything. We test the speed and memory of generating 2048 tokens with the input lengths of 1, 6144, 14336, 30720, 63488, and 129024 tokens. CPU: Intel 13th series; GPU: NVIDIA GeForce RTX 3090 (Ampere - sm 86) RAM: 64GB; OS: Windows 11 Pro; TensorRT-LLM on the laptop dGPU was 29. 88 tokens/s, resulting in a total tokens/s of 1300+! We’re talking 2x higher tokens per second easily. 12 tokens/s, 423 tokens, context 3146, 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. TGI GPTQ 8bit load failed: Llama 2 7bn Gemma 7Bn, using Text Generation Inference, showed impressive performance of approximately 65. I think that's a good baseline to For a 70b q8 at full 6144 context using rope alpha 1. 16 tokens per second (30b), also requiring autotune. However, with full offloading of all 35 layers, this figure jumped to 33. Speed: 7-8 t/s. Applicable only in stream mode. My workflow is: 512x512, no additional networks / extensions, no hires fix, 20 steps, cfg 7, no refiner How to pass that 5 tokens per second though? Also, don't you notice differences with 13B models and maybe Mixtral 8x7B Within the next week or two, my second 3090 will be coming in, and I already have a 3090 NVlink, so I'll be able to post some hard numbers for single card, Epyc 7402 8x16GB ECC 2933MHz: 64 runs ( 371. An 8 channel EPYC or Threadripper with DDR5 ram as well as 3090 GPUs costs the same price as the apple and will be more efficient in token/s per watt since its throughput is that much faster. this is my current code to load llama 3. Tokens are the output of the LLM. 99 tokens per second) llama_print_timings: prompt eval time = 709. Total response time : The total time taken to generate 100 tokens. The chart below shows that for 100 concurrent requests, each request gets worst case (p99) 12. 5x if you use fp16. 5 T/S ( 20. codellama-34b. 58 seconds (4. Not sure But I wonder, if I get an RTX 3090 with 24 gb vram (iiirc) there might be a lot of potential for being able to do exactly zero more than with 12 gb. something has changed We've tested all the modern graphics cards in Stable Diffusion, using the latest updates and optimizations, to show which GPUs are the fastest at AI and machine learning inference. The benchmarks are performed across different hardware configurations using the 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. That same benchmark was ran on vLLM and it achieved 1,200 tokens per second for Llama 2 7B on H100! Discussion I have a dual 3090 box here on ubuntu 22. 5 and 2 tokens per second tops. Performance of 13B Version. Ollama token bench is designed to benchmark models' tokens/s in Ollama, by generating responses based on prompts, logging the results, and computing time series statistics. Our Observations: For the smallest models, the GeForce RTX and Ada cards with 24 GB of VRAM are the most cost effective. Tokens per second (TPS) is one of the most important metrics we track for LLM performance. But it seems like running both the OS screen and a 70B model on one 24GB card can only be 3090 is a good cost effective option, if you want to fine tune or train models yourself (not big LLMs of course) then a 4090 will make a difference. 9 tokens per second. It would take > 26GB of VRAM. 5 tokens / second by splitting the model up across them. Compare graphics card gaming performance in 81 games and in 1080p, 1440p The RTX 3090 is faster by 174% for 1440p gaming. Half precision (FP16). Which, I'd love faster, but it's usable for my needs. 43 T/s dual 3090 running exl 70b guanaco at 8-10 T/s My heavily power limited 3090 (220w) + 4090(250w) runs over 15 token/s on exllama. Results. RTX A6000 Relative tokens per second on Mistral 7B. cpp, Throughput: The number of output tokens, per second, per GPU, that the inference server can generate across all users and requests. On my 3090+4090 system, a 70B Q4_K_M GGUF inferences at about 15. And I do some test on driver version 530. Llama 3. On an Instinct MI60 w/ llama. 67: 106: NVIDIA V100: 3. 2 GB/s). 5 t/s, so about 2X faster than the M3 Max, but the bigger deal is that prefill speed is 126 t/s, Observe the console for tokens per second figures. We use gpu_memory_utilization=0. I have a 13th I9, 64gig ddr-5 ram and an idle RTX 3090 Fresh installed with anaconda Running llama 7B in 8 bit mode gives me 4-7 tokens per second, the GPU stays below 1% average utilization in task manager. (This is running on Linux, if I use Windows and diffusers etc then it’s much slower, about 2m30 per image) GTX 1070 versus RTX 3090 performance benchmarks comparison. Q5_K_M. P. 228 per million output tokens. Reply reply 20 tokens per second, I get proper sentences, not garbage. cpp 1591e2e, I get around ~10T/s. For example a dual I compared the 7900 XT and 7900 XTX inferencing performance vs my RTX 3090 and RTX 4090. eetq-8bit doesn't require specific model. For vLLM, the memory usage is not reported because it pre-allocates all GPU memory. I get around the same performance as cpu (32 core 3970x vs 3090), about 4-5 tokens per second for the 30b model. They all seemed to require AutoGPTQ, and that is pretty darn slow. 88 tokens per second. It does vary quite a bit depending on the CPU. Conclusions. Follow us on Twitter or LinkedIn to stay up to date with future analysis For example for exl2 on 3090 I get 50+ tokens per second. The benchmark tools provided with TGI allows us to look across batch sizes, prefill, and decode steps. 77: 102: AI image processing . With the RTX 4090 priced over **$2199 CAD**, my next best option for more than 20Gb of VRAM was to get ( 0. ” Currently, I'm renting a 3090 on vast. Except the gpu version needs auto tuning in triton. Mind you, one of them is running on a pcie 1x lane, if you had more lanes you No my RTX 3090 can output 130 tokens per second with Mistral on batch size 1. 51 NVIDIA A100 80GB That number is mine (username = marti), the 23. AMD EPYC 7513 32-Core Processor :: 0. 5 bit EXL2 70B model at a good 8 tokens per second with no problem. Go with the 3090. P. 1 8B with Ollama shows solid performance across a wide range of devices, including lower-end last-generation GPUs. I have a 3090 and seems like I can run 30b models but not 33 or 34 b. 29 tokens per second) Xeon W-2135 8x32GB ECC 2133MHz: 42 runs Now that I have achieved that it's time to Our 3090 Machine, now used by one of our engineers to build Jan. Performance can vary widely from one model to another. . lrfzcnaduomfghozipfskhavoketyedxzzwfadhwvogpwojsnguf
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