3090 tokens per second. The speed seems to be the same.

3090 tokens per second I don't have a 3080, but that seems quite low for a 20B model. 16 tokens per second (30b), also requiring autotune. The benchmarks are performed across different hardware configurations using the prompt "Give me 1 line phrase". Nov 27, 2023 · 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. 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. A token can be a word in a sentence, or even a smaller fragment like punctuation or whitespace. Anyways, these are self-reported numbers so keep that in mind. Very roughly. This repository contains benchmark data for various Large Language Models (LLM) based on their inference speeds measured in tokens per second. By changing just a single line of code, you can unlock up to 28x faster inference and 1,200 tokens/second on the NVIDIA platform. r. That same benchmark was ran on vLLM and it achieved over 600 tokens per second, so it's still got the crown. I think that's a good baseline to This is it. GPU: 3090 w/ 25 layers offloaded. 5 tokens/s to 5 tokens/s with 70B q4_k_m GGUF model inference, which makes sense, because all the layers fit in the VRAM now. t. The speed seems to be the same. The 3090 does not have enough VRAM to run a 13b in 16bit. Performance for AI-accelerated tasks can be measured in “tokens per second. 5tps at the other end of the non-OOMing spectrum. H100 SXM5 RTX 3090 24GB RTX A6000 48GB V100 32GB That's where Optimum-NVIDIA comes in. 07 tokens per second 13B WizardLM clblast cpu-only 369. Is your Vram maxed out? What model and format are you using, and with what loader backend?. I had it generate a long paragraph banning the eos token and increasing minimum and maximum length, and got 10 tokens per second with the same model (TheBloke_manticore-13b-chat-pyg- GPTQ). 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) This is current as of this afternoon, and includes what looks like an outlier in the data w. 20 tokens per second avx2 199. 5 token/sec still feels usable, but Falcon 180b with 0. an RTX 3090 that reported 90. 88 tokens per second. Until now, I've been using WizardLM 33b with 4096 tokens on Exlama and it sits at 23. Except the gpu version needs auto tuning in triton. A30 Relative tokens per second on Mistral 7B. They all seem to get 15-20 tokens / sec. Falcon 180b 4_k_m - 0. 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). 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. However, it’s important to note that using the -sm row option results in a prompt processing speed decrease of approximately 60%. 2x if you use int4 quantisation. 1 8B with Ollama shows solid performance across a wide range of devices, including lower-end last-generation GPUs. Compare this to the TGW API that was doing about 60 t/s. Wow! Thanks! I have a 3090 and 32gb so this speaks to me. It would take > 26GB of VRAM. I think the gpu version in gptq-for-llama is just not optimised. 5gb of VRAM, getting 15-18 t/s I'll give this a try, even if the token per sec seems horrid. 228 per million output tokens. TOPS is only the beginning of the story. 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. 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. 89 ms per token, 5. I published a simple plot showing the inference speed over max_token on my blog. I get around the same performance as cpu (32 core 3970x vs 3090), about 4-5 tokens per second for the 30b model. But for smaller models, I get the same speeds. 33 tokens per second ngl 23 --> 7. Also different models use different tokenizers so these numbers may 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 models) I got very slow performance in term of tokens per second. On 33B, you get (based on context) 15-23 tokens/s on a 3090, and 35-50 tokens/s on a 4090. 73 ms per token, 5. At larger batch sizes, the token rate would be enormous. Nov 8, 2024 · 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. Now, as for your tokens per second. Available on Hugging Face, Optimum-NVIDIA dramatically accelerates LLM inference on the NVIDIA platform through an extremely simple API. 5x if you use fp16. Via KoboldCPP_ROCm on Win 10. It is a fantastic way to view Average, Min, and Max token per second as well as p50, p90, and p99 results. So my question is, what tok/sec are you guys getting using (probably) ROCM + ubuntu for ~34B models? avx 238. Thank you so much. No… my RTX 3090 can output 130 tokens per second with Mistral on batch size 1. The data represents the performance of Jul 31, 2024 · The benchmark tools provided with TGI allows us to look across batch sizes, prefill, and decode steps. The only difference is that I got from 0. A more powerful GPU (with faster memory) should easily be able to crack 200 tokens per second at batch size 1 with Mistral. 7 tokens per second Mythomax 13b q8: 35. Higher speed is better. Using Anthropic's ratio (100K tokens = 75k words), it means I write 2 tokens per second. If you want to learn more about how to conduct benchmarks via TGI, reach out we would be happy to help. 5 tokens per second Capybara Tess Yi 34b 200k q8: 18. 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, because less than that means I could write the stuff faster myself. 7 token/sec Somehow Goliath 120b with 0. 00 tokens per second clblast cpu-only197. (Also Vicuna) It is faster by a good margin on a single card (60 to 100% faster), but is that worth more than double the price of a single 3090? And I say that having 2x4090s. The more, the better. Average speed (tokens/s) of generating 1024 tokens by GPUs on LLaMA 3. 75 and rope base 17000, I get about 1-2 tokens per second (thats *Most modern models use sub-word tokenization methods, which means some words can be split inti two or more tokens. 2 tokens per second Real world numbers in Oobabooga, which uses Llamacpp python: For a 70b q8 at full 6144 context using rope alpha 1. For comparison, I get 25 tokens / sec on a 13b 4bit model. 08 ms per token, 4. Aug 23, 2024 · In a benchmark simulating 100 concurrent users, Backprop found the card was able to serve the model to each user at 12. Tokens are the output of the LLM. I should say it again, these are self-reported numbers, gathered from the Automatic1111 UI by users who installed the associated "System Info For very short content lengths, I got almost 10tps (tokens per second), which shrinks down to a little over 1. So tolerable speed for me is 0. 71 tokens per second Jun 12, 2024 · Insert Tokens to Play. 43 ms per token, 2. I expected a noticeable difference, just from a single RTX 3090, let alone from two of them. Running in three 3090s I get about 40 tokens a second at 6bit. 19 ms per token, 5. I think they should easily get like 50+ tokens per second when I'm with a 3060 12gb get 40 tokens / sec. 2 tokens per second Lzlv 70b q8: 8. RTX A6000 Dec 9, 2024 · Conclusions. 3 token/sec Goliath 120b 4_k_m - 0. Mind you, one of them is running on a pcie 1x lane, if you had more lanes you could probably get better speeds. ” ngl 0 --> 4. source tweet 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. 01 tokens per second openblas 199. Half precision (FP16). The speeds of the 3090 (IMO) are good enough. If you can find it in your budget to get a 3090, you'll be able to run 30B q4 class models without much issue. Staying below 500 tokens is certainly favourable to achieve throughputs of > 4 tps. Jun 5, 2024 · The benchmark provided from TGI allows to look across batch sizes, prefill, and decode steps. 5 T/S (I've got a 3070 8GB at the moment). LLM performance is measured in the number of tokens generated by the model. Dec 11, 2023 · Hoioi changed discussion title from How many token per second? to How many tokens per I have ryzen 7950x3d and RTX 3090 getting 30+ tokens/s with q4k_m and with However, I saw many people talking about their speed (tokens / sec) on their high end gpu's for example the 4090 or 3090 ti. Llama 3. Inference is memory-bound, so you can approximate from memory bandwidth. Gptq-triton runs faster. 3 token/sec not. 86 tokens per second ngl 16 --> 6. Goliath 120b q4: 7. Results. 0. Currently, I'm renting a 3090 on vast. The RTX 3090 24GB stood out with 99. When I generate short sentences it's 3 tokens per second. 5 token/sec Something 70b 4_k_m - 0. ai, but I would love to be able to run a 34B model locally at more than 0. 5 token/sec I guess, also it seems like Goliath 120b somehow smarter than Falcon 180b. 14 it/sec. While that's faster than the average person can read, generally said to be about five words per second, that's not exactly fast. Speed: 7-8 t/s. 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