Llama 2 24gb price reddit. Nothing made the slightest bit of difference.
- Llama 2 24gb price reddit Posted by u/crowwork - 120 votes and 35 comments Get the Reddit app Scan this QR code to download the app now. 18 tokens per second) CPU So, sure, 48B cards that are lower cost (i. 16GB VRAM would have been better, but not by much. gguf context=4096, 20 threads, fully offloaded llama_print_timings: load time = 2782. 47 tokens per second. I've tested on 2x24GB VRAM GPUs, and it works! For now: GPTQ for LLaMA works. Welcome to reddit's home for discussion of the Canon EF, EF-S, EF-M Maybe look into the Upstage 30b Llama model which ranks higher than Llama 2 70b on the leaderboard and you should be able to run it on one 3090, I can run it on my M1 Max 64GB very fast. The current llama. one big cost factor could Even for the toy task of explaining jokes, it sees that PaLM >> ChatGPT > LLaMA (unless PaLM examples were cherry-picked), but none of the benchmarks in the paper show huge gaps between LLaMA and PaLM. 04 MiB) The model I downloaded was a 26gb model but I’m honestly not sure about specifics like format since it was all done through ollama. Apparently, ROCm 5. 4bpw models still seem to become repetative after a while. Time taken for llama to respond to this prompt ~ 9sTime taken for llama to respond to 1k prompt ~ 9000s = 2. 5 16k (Q8) at 3. Should you want the smartest model, go for a GGML high parameter model like an Llama-2 70b, at Q6 quant. 81 (Radeon VII Pro) llama 13B Q4_0 6. Skill DDR5 with a total capacity of 96GB will cost you around $300. MacBook Pro M1 at steep discount, with 64GB Unified memory. YMMV. Subreddit to discuss about Llama, the large language model created by Meta AI. 02 B Vulkan (PR) 99 tg 128 19. Chat test. Actually Q2 Llama model fits into a 24GB VRAM Card without any extra offloading. I've been able to go upto 2048 with 7b on 24gb Note: Reddit is dying due to terrible leadership from CEO /u/spez. I As the title says there seems to be 5 types of models which can be fit on a 24GB vram GPU and i'm interested in figuring out what configuration is best: A special leaderboard for quantized Hello all, I'm currently running one 3090 card with 24GB VRAM, primarily with EXL2 or weighted GGUF quants offloaded to VRAM. I'm not one of them. 0 Gaming Graphics Card, IceStorm 2. The PC world is used to modular designs, so finding a market for people willing to pay Apple prices for PC parts might not be super appealing to them. If you’re running llama 2, mlc is great and runs really well on the 7900 xtx. 1 upvote r/24gb. 06bpw, right? Name: ZOTAC Gaming GeForce RTX™ 3090 Trinity OC 24GB GDDR6X 384-bit 19. Expecting to use Llama-2-chat directly is like Since 13B was so impressive I figured I would try a 30B. Both cards are comparable in price (around $1000 currently). What I managed so far: Found instructions to make 70B run on VRAM only with a 2. Testing the Asus X13, 32GB LPDDR5 6400, Nvidia 3050TI 4GB vs. There are a lot of issues especially with new model types splitting them over the cards and the 3090 makes it so much With 24GB VRAM maybe you can run the 2. You can also get the cost down by owning the hardware. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation, and I am using GPT3. Actually you can still go for a used 3090 with MUCH better price, same amount of ram and better performance. 78 seconds (19. You can try it and check if it's enough for you use case. At what context length should 2. You have unrealistic expectations. A couple of comments here: Note that the medium post doesn't make it clear whether or not the 2-shot setting (like in the PaLM paper) is used. 9% overhead. Personally I consider anything below ~30B a toy model / test model (unless you are using it for a very specific narrow task). GCP / Azure / AWS prefer large customers, so they essentially offload sales to intermediaries like RunPod, Replicate, Modal, etc. Reddit Post Summary: Title: Llama 2 Scaling Laws This Reddit post delves into the Llama 2 paper that explores how AI language models scale in performance at different sizes and training durations. It's $6 per GB of VRAM. main. 16gb Adie is better value right now, You can get a kit for like $100. GGUF is even better than Senku for roleplaying. You can run them on the cloud with higher but 13B and 30B with limited context is the best you can hope (at 4bit) for now. But a little bit more on a budget ^ got a used ryzen 5 2600 and 32gb ram. 2 TB/s (faster than your desk llama can spit) H100: Price: $28,000 (approximately one kidney) Performance: 370 tokens/s/GPU (FP16), but it doesn't fit into one. Get the Reddit app Scan this QR code to download the app now Unsloth also supports 3-4x longer context lengths for Llama-3 8b with +1. 1-mixtral-1x22b-GGUF · Hugging Face Get the Reddit app Scan this QR code to download the app now. There is a big chasm in price between hosting 33B vs 65B models the former fits into a single 24GB GPU (at 4bit) while the big guys need either 40GB GPU or 2x cards. I'm currently on LoneStrikers Noramaid 8x7 2. e. I'd like to do some experiments with the 70B chat version of Llama 2. 2 subscribers in the 24gb community. It allows to run Llama 2 70B on 8 x Raspberry Llama 3 can be very confident in its top-token predictions. 21 ms per token, 10. 11) while being Then adding the nvlink to the cost. This is more of a cost comparison that I am doing between gpt 3. 9 Fakespot Reviews Grade: A Adjusted Fakespot Rating: 3. I can run the 70b 3bit models at around 4 t/s. LLM360 has released K2 65b, a fully reproducible open source LLM matching Llama llama_new_context_with_model: VRAM scratch buffer: 184. With an 8Gb card you can try textgen webui with ExLlama2 and openhermes-2. But you can run Llama 2 70B 4-bit GPTQ on 2 x RTX 4090 with 24GB GDDR6 on board costs around $1700, while RTX 6000 with 48GB of GDDR6 goes above $5000. 5 million alpaca tokens) Performance: 353 tokens/s/GPU (FP16) Memory: 192GB HBM3 (that's a lot of context for your LLM to chew on) vs H100 Bandwidth: 5. Getting started on my own build for the first time. The model was trained in collaboration with u/emozilla of NousResearch and u/kaiokendev . So it still works, just a bit slower than if all the memory is allocated to GPU. 125. Tested on Nvidia L4 (24GB) with `g2-standard-8` VM at GCP. large language models on 24 GB RAM Add to this about 2 to 4 GB of additional VRAM for larger answers (Llama supports up to 2048 tokens max. cpp, and by default it auto splits between GPU and CPU. witin a budget, a machine with a decent cpu (such as intel i5 or ryzen 5) and 8-16gb of ram could do the job for you. View community ranking In the Top 1% of largest communities on Reddit [N] Llama 2 is here. 3090 - ~$800 / 24gb = $33/gb This cheap price per gig gave me pause; when you suggested that P100 might have reasonable FP16 performance, it seemed for a moment that, assuming PCI slots and lanes were not a limitation, that filling up a box with P100's would be half the price of 3090's for the same total VRAM. In a ML practitioner by profession but since a lot of GPU infra is abstracted at workplace, I wanted to know which one is better value for price+future proof. That's why the 4090 and 3090s score so high on value to cost ratio - consumers simply wouldn't pay A100 and esp not H100 prices even if you could manage to snag one. To those who are starting out on the llama model with llama. 17GB 26. You are going to be able to do qloras for smaller 7B, 13B, 30B models. . Old. If you have 12GB, you can run a 10-15B at the same speed. Here is an example with the system message "Use emojis only. 2. I have TheBloke/VicUnlocked-30B-LoRA-GGML (5_1) running at 7. 2 Million times in the first Code Llama pass@ scores on HumanEval and MBPP. 2 T/s. telling me to get the Ti version of 3060 because it was supposedly better for gaming for only a slight increase in price but i opted for the cheaper version anyway and Fast-forward to today it turns out that this was a good decision after all because the base Edit 2: The new 2. However, I'd like to share that there are free alternatives available for you to experiment with before investing your hard-earned money. PS: I believe the 4090 has the option for ECC RAM which is one of the common enterprise features that adds to the price (that you're kinda getting for free because consumers don't I have an M1 MAc Studio and an A6000 and although I have not done any benchmarking the A6000 is definitely faster (from 1 or 2 t/s to maybe 5 to 6 t/s on the A6000 - this was with one of the quantised llamas, I think the 65b). Got myself an old Tesla P40 Datacenter-GPU (GP102 like GTX1080-silicon but with 24GB ECC vram, 2016) for 200€ from ebay. Many should work on a 3090, the 120b model works on one A6000 at roughly 10 tokens per second. What GPU split should I do for RTX 4090 24GB GPU 0 and RTX A6000 48GB GPU 1 and how much context would I be able to get with Llama-2-70B-GPTQ-4bit-32g-actorder_True? Reply reply cornucopea so for a start, i'd suggest focusing on getting a solid processor and a good amount of ram, since these are really gonna impact your Llama model's performance. 55bpw would work better with 24gb of VRAM Reply reply More replies More replies. 18 ± 1. I split models between a 24GB P40, a 12GB 3080ti, and a Xeon Gold 6148 (96GB system ram). Disabling 8-bit cache seems to help cut down on the repetition, but not entirely. bin to run at a reasonable speed with python llama_cpp. 38 tokens per second) llama_print_timings: eval time = 55389. I use two servers, an old Xeon x99 motherboard for training, but I serve LLMs from a BTC mining motherboard and that has 6x PCIe 1x, 32GB of RAM and a i5-11600K CPU, as speed of the bus and CPU has no effect on inference. - fiddled with libraries. 9 Analysis Performed at: 10-18-2022 2. cpp and python and accelerators - checked lots of benchmark and read lots of paper (arxiv papers are insane they are 20 years in to the future with LLM models in quantum computers, increasing logic and memory with hybrid models, its Get the Reddit app Scan this QR code to download the app now. (= without quantization), but you can easily run it in 4bit on 12GB vram. The Largest Scambaiting Community On Reddit! Scambaiting by But that is a big improvement from 2 days ago when it was about a quarter the speed. Interesting side note - based on the pricing I suspect Turbo itself uses compute roughly equal to GPT-3 Curie (price of Curie for comparison: Deprecations - OpenAI API, under 07-06-2023) which is suspected to be a 7B model (see: On the Sizes of OpenAI API Models | EleutherAI Blog). 9. The 3090 has 3x the cuda cores and they’re 2 generations newer, and has over twice the memory bandwidth. 75bpw myself and uploaded them to huggingface for others to download: Noromaidx and Typhon. Reply reply nuketro0p3r llama-2-7b-chat-codeCherryPop. 05 ms / 307 In the same vein, Lama-65B wants 130GB of RAM to run. 01 ms per token, 24. Since llama 2 has double the context, and runs normally without rope I have 4x ddr5 at 6000MHz stable and a 7950x. distributed video ai processing and occasional llm use cases Since they are one of the cheapest 24GB cards you can get. This is probably necessary considering its massive 128K vocabulary. 4GB on bsz=2 and seqlen=2048. Check prices used on Amazon that are fulfilled by Amazon for the easy return. /r/StableDiffusion is back open after the protest of Depends how you run it 8 bit 13b model for codellama 2 with its bigger context works better for me on a 24GB card than 30b llama1 that's 4-bit. (granted, it's not actually open source. Having 2 1080ti’s won’t make the compute twice as fast, it will just compute the data from the layers on each card. If you ask them about most basic stuff like about some not so famous celebs model would just The compute I am using for llama-2 costs $0. exe --model I suggest getting two 3090s, good performance and memory/dollar. " MSFT clearly knows open-source is going to be big. With 2 P40s you will probably hit around the same as the slowest card holds it up. Or check it out in the app stores TOPICS WizardLM-2-7B-abliterated and Llama-3-Alpha-Centauri-v0. For example a few months ago, we figured out how to train a 70b model with 2 24gb, something that required A100s before. If, on the Llama 2 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to PDF claims the model is based on llama 2 7B. 5/hour, L4 <=$0. Reply reply Most cost effective and energy effective per token generated would be to have something like 4090 but with 8x/16x memory capacity with the same total bandwidth, essentially Nvidia H100/H200. cpp gets above 15 t/s. 12 tokens/s, 512 tokens, context 19, seed 1778944186) Output generated in 36. It's able to handle upto 8 concurrent Have you tried GGML with CUDA acceleration? You can compile llama. I'm here building llama. Skip to main content. An A10G on AWS will do ballpark 15 tokens/sec on a 33B model using exllama and spots for $0. I have filled out Open AI's Rate Limit Increase Form and my limits were marginally increased, but I still need more. And that's talking purely VRAM! a fully reproducible open source LLM matching Llama 2 70b Microsoft is our preferred partner for Llama 2, Meta announces in their press release, and "starting today, Llama 2 will be available in the Azure AI model catalog, enabling developers using Microsoft Azure. After hearing good things about NeverSleep's NoromaidxOpenGPT4-2 and Sao10K's Typhon-Mixtral-v1, I decided to check them out for myself and was surprised to see no decent exl2 quants (at least in the case of Noromaidx) for 24GB VRAM GPUs. Barely 1T/s a second via cpu on llama 2 70b ggml int4. While you're here, we have a public discord server now — We also have a ChatGPT bot on the server for everyone to use! Yes, the actual ChatGPT, not text-davinci or other models. 5 and 4. 11GB Q2_K 3. My Japanese friend brought it for me, so I paid no transportation costs. If you live in a studio apartment, I don't recommend buying an 8 card inference server, regardless of the couple $1000 in either direction and the faster speed. Combined with my p40 it also works nice for 13b models. Then Subreddit to discuss about Llama, the large language model created by Meta AI. Even better would be a price range chart, model of card, and LLM model sizes for running various models here. Inference cost, since you will only be paying the electricity bill for running your machine. 6 is under development, so it's not clear whether AMD I'm running 24GB card right now and have an opportunity to get another for a pretty good price used. Or check it out in the app stores 20 tokens/s for Llama-2-70b-chat on a RTX 3090 Mod Post Share but it's usable for my needs. 5-mixtral-8x7b model. But it seems like running both the OS screen and a 70B model on one 24GB card can only be done by trimming the context so short it's not useful for Get the Reddit app Scan this QR code to download the app now. Most people here don't need RTX 4090s. Inference times suck ass though. 5-mistral model (mistral 7B) in exl 4bpw format. ) LLama-2 70B groupsize 32 is shown to have the lowest VRAM requirement (at 36,815 MB), but wouldn't we expect it to be the highest? The perplexity also is barely better than the corresponding quantization of LLaMA 65B (4. these seem to be settings for 16k. And at the moment I don’t have the financial resources to buy 2 3090 and a cooler and nvlink but I can buy a single 4090. So for almost the same price, you could have a machine that runs up to 60B parameter models slow, or one that runs 30B parameter models at a decent speed (more than 3x faster than a P40). 5 bit quantization allows running 70B models on an RTX 3090 or Mixtral-like models on 4060 with significantly lower accuracy loss - notably, better than QuIP# and 3-bit GPTQ. 4GB to finetune Alpaca! A week ago, the best models at each size were Mistral 7b, solar 11b, Yi 34b, Miqu 70b (leaked Mistral medium prototype based on llama 2 70b), and Cohere command R Plus 103b. I highly suggest using a newly quantized 2. 0 RGB Lighting, ZT-A30900J-10P Company: Amazon Product Rating: 3. Find an eBay seller with loads of good feedback and buy from there. Looks like a better model than llama according to the benchmarks they posted. On Llama 7b, you only need 6. 24GB IQ2_M 2. Anything older than a few hundred tokens dropped off to 0% recall. The rule of thumb for full model finetune is 1x model weight for weight itself + 1x model weight for gradient + 2x model weight for optimizer states (assume adamw) + activation (which is batch size & sequence length dependent). cpp does infact support multiple devices though, so thats where this could be a risky bet. 4bpw, I get 5. I think a 2. 02 B Vulkan (PR) 99 tg 128 16. Hell I remember Dollar per megabyte prices on Hard drives. I wonder how many threads you can use make these models work at lightning speed. 65bpw quant instead since those seem to I want to run a 70B LLM locally with more than 1 T/s. 00 ms / 564 runs ( 98. ". 5 tokens a second (probably, I don't have that hardware to verify). Or check it out in the app stores Maybe a slightly lower than 2. 5 and llama2 13B for one of my projects. Inference will be half as slow (for llama 70b you'll be getting something like 10 t/s), but the massive VRAM may make this interesting enough. bartowski/dolphin-2. Nice to also see some other ppl still using the p40! I also built myself a server. 13B models run nicely on it. I have 64GB of RAM and a 4090 and I run llama 3 70B at 2. Get the Reddit app Scan this QR code to download the app now. This seems like a solid deal, one of the best gaming laptops around for the price, if I'm going to go that route. 75GB 22. Meanwhile I get 20T/s via GPU on GPTQ int4. having 16 cores with 60GB/s of memory bandwidth on my 5950x is great for things like cinebench, but extremely wasteful for pretty much every kind of HPC application. 2 weak 16GB card will get easily beaten by 1 fast 24GB card, as long as the model fits fully inside 24GB memory. Roughly double the numbers for an Ultra. 5 bpw that run fast but the perplexity was unbearable. (2023), using an optimized auto-regressive transformer, but Ollama uses llama. So far I only did SD and splitting 70b+ Here is nous-capybara up to 8k context @4. Below are some of its key features: User-Friendly For enthusiasts 24gb of ram isn't uncommon, and this fits that nicely while being a very capable model size. ? For 2. 4 = 47% different from the original model when already optimized for its specific specialization, while 2. I read the the 50xx cards will come at next year so then it will be a good time to add a second 4090. 76 bpw. In order to prevent multiple repetitive comments, this is a friendly request to u/bataslipper to reply to this comment with the prompt you used so other users can experiment with it as well. It's the best of the affordable; terribly slow compared to Subreddit to discuss about Llama, the large language model created by Meta AI. Starting price is 30 USD. 3B in 16bit is 6GB, so you are looking at 24GB minimum before adding activation and library overheads. This subreddit is temporarily closed in protest of Reddit killing third party apps, see /r/ModCoord and /r/Save3rdPartyApps for more information. 5 Gbps PCIE 4. Higher capacity dimms are just newer, better and cost more than a over year old Adie. GPU llama_print_timings: prompt eval time = 574. However, the 1080Tis only have about 11GBPS of memory bandwidth while the 4090 has close to 1TBPS. 72 seconds (2. Should we conclude somewhat that the 2. 87 I've received a freelance job offer from a company in the banking sector that wants to host their own LLAMA 2 model in-house. And 70b will not run on 24GB, more like 48GB+. 6ppl when the stride is 512 at length 2048. I know SD and image stuff needs to be all on same card but llms can run on different cards even without nvlink. 55 seconds (18. cpp, I only get around 2-3 t/s. Any feedback welcome :) Locked post. r/24gb. This doesn't include the fact that most individuals won't have a GPU above 24GB VRAM. Linux has ROCm. 47 ms llama_print_timings: sample time = 244. 55 seconds (4. AutoGPTQ can load the model, but it seems to give empty responses. cpp opencl inference accelerator? Discussion And to think that 24gb VRAM isn't even enough to run a 30b model with full precision. Output generated in 33. r/LlamaModel: Llama 2 and other Llama (model) news, releases, questions and discussion - furry Llama related questions also accepted. To create the new family of Llama 2 models, we began with the pretraining approach described in Touvron et al. Llama 3 8B has made just about everything up to 34B's obsolete, and has performance roughly on par with chatgpt 3. cpp and llama-cpp-python with CUBLAS support and it will split between the GPU and CPU. g. I couldn't imagine paying that kind of price for a CPU/GPU combo when I planned to just jam an Nvidia card in there lol The 2-2. Or check it out in the app stores LLM360 has released K2 65b, a fully reproducible open source LLM matching Llama 2 70b I’m looking for some advice about possibly using a Tesla P40 24GB in an older dual 2011 Xeon server with 128GB of ddr3 1866mhz ecc, 4x PCIE 3. Lenovo Q27h-20, driver poser state faliure, BSOD. The 4090 price doesn't go down, only up, just like the new/used 3090's have been up to the moon since the ai boom. Under Vulkan, the Radeon VII and the A770 are comparable. All of a sudden with 2 used $1200 GPUs I can get to training a 70b at home, where as I needed $40,000 in GPU. Meta says that "it’s likely that you can fine-tune the Llama 2-13B model using LoRA or QLoRA fine-tuning with a single consumer GPU with 24GB of memory, and using QLoRA requires even less GPU memory and fine-tuning time than LoRA" in their fine-tuning guide Price: $15,000 (or 1. Top P, Typical P, Min P) are basically designed to trust the model when it is especially confident. 05 seconds (14. 24gb is the sweet spot now for consumers to run llms locally. If you don't have 2x 4090s/3090s, it's too painful to only offload half of your layers to GPU. GDDR6X is probably slightly more, but should still be well below $120 now. The next step up from 12GB is really 24GB. The FP16 weights on HF format had to be re-done with newest transformers, so that's why transformers version on the title. Recently did a quick search on cost and found that it’s possible to get a half rack for $400 per month. Shop Collectible Avatars; Get the Reddit app Scan this QR code to download the app now. Controversial. 2 4090s are always better than 2 3090s training or inferences with accelerate. We observe that scaling the number of parameters matters for models specialized for coding. 6T/s and dolphin 2. 24GB VRAM will let you run 30b 4bit models. I tried pytorch 2. 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. 2 tokens per second. It is a good starting point even at 12GB VRAM. 55bpw quant of llama 3 70B at reasonable speeds. closer to linear price scaling wrt. 5 or Mixtral 8x7b. Note how op was wishing for an a2000 with 24gb vram instead of an "openCL"-compatible card with 24gb vram? but Llama 3 was downloaded over 1. In the end, the MacBook is clearly faster with 9. Dont know if OpenCLfor llama. However, a lot of samplers (e. 16GB doesn't really unlock much in the way of bigger models over 12GB. Technology definitely needs to catch up. 2 sticks of G. I will have to load one and check. 50/hr (again ballpark). I'm not sure how it makes sense to buy the 3090. 5/hour, A100 <= $1. Log In / Sign Up; Advertise on Reddit; Shop Collectible Avatars; Get the Reddit app Scan this QR code to download the app now. What are the best use cases that you have? I like doing multi machine i. 05$ for Replicate). Even with included purchase price way cheaper than paying for a proper GPU instance on AWS imho. I host 34B Code LLaMA gptq on a10g, which has 24GB vram. Or check it out in the app stores TOPICS Subreddit to discuss about Llama, the large language model created by Meta AI. Llama2 is a GPT, a blank that you'd carve into an end product. Don’t buy off Amazon, the prices are hyper inflated. development cost amortization over time/scale, AI core competency, resulting company valuation, etc are worth it. The author argues that smaller models, contrary to prior assumptions, scale better with respect to training compute up to an unknown point. I'm currently running llama 65B q4 (actually it's alpaca) on 2x3090, we also had 2 failed runs, both cost about $75 each. You need 2 x 80GB GPU or 4 x 48GB GPU or 6 x 24GB GPU to run fp16. Llama 3 70b instruct works surprisingly well on 24gb VRAM cards Intel arc gpu price drop - inexpensive llama. We observe that model specialization is yields a boost in code generation capabilities when comparing Llama 2 to Code Llama and Code Llama to Code Llama Python. While IQ2_XS quants of 70Bs can still hallucinate and/or misunderstand context, they are also capable of driving the story forward better than smaller models when they get it right. q4_0. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation, and exclude blind users from the site. So I quantized to them to 3. 6'', M2, 24GB, 10 Core GPU. Meta launches LLaMA 2 LLM: free, open-source and now available I was testing llama-2 70b (q3_K_S) at 32k context, with the following arguments: -c 32384 --rope-freq-base 80000 --rope-freq-scale 0. This is using a 4bit 30b with streaming on one card. 5bpw model is e. On Mistral 7b, we reduced memory usage by 62%, using around 12. If you have a 24GB VRAM card, a 3090, you can run a 34B at 15 tk/s. Those llama 70b prices are in the ballpark of EDIT 2: I actually got both laptops at very good prices for testing and will sell one - I'm still thinking about which one. 24 ± 0. It should perform close to that (the W7900 has 10% less memory bandwidth) so it's an option, but seeing as you can get a 48GB A6000 (Ampere) for about the same price that should both outperform the W7900 and be more widely compatible, you'd probably be better off with the Nvidia card. 10 vs 4. 3t/s a llama-30b on a 7900XTX w/ exllama. You can improve that speed a bit by using tricks like speculative inference, Medusa, or look ahead decoding. I’ve found the following options available around the same price point: A Lenovo Legion 7i, with RTX 4090 (16GB VRAM), 32GB RAM. 4090 24gb, 96gb ram) and get about ~1 t/s with some variance, usually a touch slower. So, using GGML models and the llama_hf loader, I have been able to achieve higher context. I have a similar system to yours (but with 2x 4090s). I have a machine with a single 3090 (24GB) and an 8-core intel CPU with 64GB RAM. 56 MiB, context: 440. ) Still, anything that's aimed at hobbyists will usually fit in 24GB, so that'd generally eliminate that concern. Does Llama 2 also have a rate limit for remaining requests or tokens? Thanks in advance for the help! Given that I have a system with 128GB of RAM, a 16-core Ryzen 3950X, and an RTX 4090 with 24GB of VRAM, what's the largest language model in terms of billions of parameters that I can feasibly run on my machine? I'm puzzled by some of the benchmarks in the README. Personally, I'd start with trying to use guidance though not because of price but because getting a dataset with good variety can be annoying. 5x longer). It's usable though. Data security, you could feasibly work with company data or code without getting in any trouble for leaking data, your inputs won't be used for training some model either. The gpu to cpu bandwidth is good enough at pcie 4 x8 or x16 to make nvlink useless I have dual 4090s and a 3080, similar to you. In that configuration, with a very small context I might get 2 or 2. 4bpw on a 4080, but with limited ctx, this could change the situation to free up VRAM for ctx, if the model, if it is a 2. You will get like 20x the speed of what you have now, and openhermes is a very good model that often beats mixtral and gpt3. I paid 400 for 2x 3060-12gb. Llama 2 7B is priced at 0. a 4090 at least for unit price/VRAM-GB) is an important step and better than nothing. It is the dolphin-2. Additional Commercial Terms. I have a 3090 with 24GB VRAM and 64GB RAM on the system. LLM was barely coherent. 79 tokens/s, 94 tokens, context 1701, seed 1350402937) Output generated in 60. g: 5/3. If you have 2x3090, you can run 70B, or even 103B. wouldn't it be soon Currently I have 8x3090 but I use some for training and only 4-6 for serving LLMs. The key takeaway for now is that LLaMA-2-13b is worse than LLaMA-1-30b in terms of perplexity, but it has 4096 context. For a little more than the price of two P40s, you get into cheaper used 3090 territory, which starts at $650ish right now. cpp with a 7900 XTX as a result. My workstation has RTX I saw that the Nvidia P40 arent that bad in price with a good VRAM 24GB and wondering if i could use 1 or 2 to run LLAMA 2 and increase inference times? Given some of the processing is limited by vram, is the P40 24GB line still useable? Thats as much vram as the 4090 and 3090 at a fraction of the price. I got a second hand water cooled MSI RTX3090 Sea Hawk from Japan at $620 price. 1 subscriber in the 24gb community. Llama 2 13B performs better on 4 I had basically the same choice a month ago and went with AMD. for the price of running 6B on the 40 series (1600 ish bucks) You should be able to purchase 11 M40's thats 264 GB of VRAM. If you look at babbage-002 and davinci-002, they're listed under recommended replacements for The price doesn't get effected by the lower cards because no one buys 16gb of vram when they could get 24gb cheaper (used aka 3090 $850-1000). 19 ms / 14 tokens ( 41. However, I don't have a good enough laptop to run it locally with reasonable speed. More RAM won’t increase speeds and it’s faster to run on your 3060, but even with a big investment in GPU you’re still only looking at 24GB VRAM which doesn’t give you room for a whole lot of context with a 30B. It also lets you train LoRAs with relative ease and those will likely become a big part of the local LLM experience. Getting either for ~700. Members Online. 37GB IQ3_XS Oh you can. Nothing made the slightest bit of difference. This is in LM studio with ~20 There are 24GB dimms from micron on the market as well, those are not good for high speed so watch out what you are buying. i already tried the llama 2 13b but i thought maybe there are better model This is for a M1 Max. (not out yet) and a small 2. There is no Llama 2 30B model, Meta did not release it cause it failed their "alignment". Lama-2-13b-chat. 24 tokens/s, 257 tokens, context 1701, seed 1433319475) (1) Large companies pay much less for GPUs than "regulars" do. ggmlv3. Question | Help LLM360 has released K2 65b, a fully reproducible open source LLM matching Llama 2 70b Like others are saying go with the 3090. 001125Cost of GPT for 1k such call = $1. 4 = 65% different? I recently bought a 3060 after the last price drop to ~300 bucks. the MacBook Air 13. 7b models are still smarter than monkeys in some ways, and you can train monkeys to do a lot of cool stuff like write my Reddit posts. You should think of Llama-2-chat as reference application for the blank, not an end product. Boards that can do dual 8x PCI and cases/power that can handle 2 GPUs isn't very hard. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will On theory, 10x 1080 ti should net me 35,840 CUDA and 110 GB VRAM while 1x 4090 sits at 16,000+ CUDA and 24GB VRAM. 1 and 2. 128k Context Llama 2 Finetunes Using YaRN Interpolation (successor to NTK-aware interpolation) and Flash Attention 2 As far as tokens per second on llama-2 13b, it will be really fast, like 30 tokens / second fast (don't quote me on that but all I know is it's REALLY fast on such a slow model). 2/hour. I hate monopolies, and AMD hooked me with the VRAM and specs at a reasonable price. The model was loaded with this command: I have a laptop with a i9-12900H, 64GB ram, 3080ti with 16GB vram. I got decent stable diffusion results as well, but this build definitely focused on local LLM's, as you could build a much better and cheaper build if you were planning to do fast and only stable Thanks for pointing this out, this is really interesting for us non-24GB-VRAM-GPU-owners. See also: I'll greedily ask for the same tests with a YI 34B model and a Mixtral model as I think generally with a 24GB card those models are the best mix of quality and speed making them the most usable options atm. cpp OpenCL support does not actually effect eval time, so you will need to merge the changes from the pull request if you are using any AMD GPU. 2 Yi 34B (q5_k_m) at 1. x, and people are getting tired of waiting for ROCm 5. 4080 is obviously better as a graphics card but I'm not finding a clear answer on how they compare for Get a 3090. You can load in 24GB into VRAM and whatever else into RAM/CPU at the cost of inference speed. and we pay the premium. Q8_0. 55 bpw) to tell a sci-fi story set in the year 2100. 17 (A770) Ye!! We show via Unsloth that finetuning Codellama-34B can also fit on 24GB, albeit you have to decrease your bsz to 1 and seqlen to around 1024. 0-1. As of last year GDDR6 spot price was about $81 for 24GB of VRAM. Still takes a ~30 seconds to generate prompts. 6/3. I plan to run llama13b (ideally 70b) and voicecraft inference for my local home-personal-assistant setup project. Please use our Discord server instead of supporting a company that acts against its users and unpaid moderators. There isn't a point in going full size, Q6 decreases the size while barely compromising 3 subscribers in the 24gb community. Also I run a 12 gb 3060 so vram with a single 4090 is kind of managed. 4. Depending on the tricks used, the framework, the draft model (for speculation), and the prompt you could get somewhere between 1. 2 tokens/s, hitting the 24 GB VRAM limit at 58 GPU layers. Seeing how they "optimized" a diffusion model (which involves quantization, vae pruning) you may have no possibility to use your finetuned models with this, only theirs. The problem is that the quantization is a little low and the speed a little slow because I have to offload some layers to RAM. There will definitely still be times though when you wish you had CUDA. (They've been updated since the linked commit, but they're still puzzling. H100 <=$2. 75 per hour: The number of tokens in my prompt is (request + response) = 700 Cost of GPT for one such call = $0. Should I attempt llama3:70b? I'm looking to transition from paid chat gpt to local ai for better private data access and use. 94GB 24. 10$ per 1M input tokens, compared to 0. The P40 is definitely my bottleneck. Quantized 30B is perfect for 24GB gpu. Chatbot Arena results are in: Llama 3 dominates the upper and mid cost-performance You can build a system with the same or similar amount of vram as the mac for a lower price but it depends on your skill level and electricity/space requirements. for storage, a ssd (even if on the smaller side) can afford you faster data retrieval. q2_K. New comments cannot be posted. Even at the cost of cpu cores! e. of information,and it posess huge knowledge about almost anything you can imagine,while in the same time this 13B Llama 2 mature models dont. Edit 3: IQ3_XXS quants are even better! Keep in mind that the increased compute between a 1080ti and 3090 is massive. The fine-tuned instruction model did not pass their "safety" metrics, and they decided to take time to "red team" the 34b model, however, that was the chat version of the model, not the base one, but they didn't even bother to release the base 34b model A used 3090 (Ti version if you can find one) should run you $700 on a good day. 2 GPUs with 44GB VRAM total for slightly above the price of a single 3090 Reply reply You can buy 2 2080 ti's w/ 22GB for the price of a single 3090. Tried llama-2 7b-13b-70b and variants. 20 tokens/s, 512 Here is a collection of many 70b 2 bit LLMs, quantized with the new quip# inspired approach in llama. Guanaco always was my favorite LLaMA model/finetune, so I'm not surprised that the new Llama 2 version is even better. cpp. About pricing, I've rented A10's on lambda and normally I end up spending around $2/model, but I know runpod is cheaper. The Asus X13 runs at 5. 04 MiB llama_new_context_with_model: total VRAM used: 25585. So Replicate might be cheaper for applications having long prompts and short outputs. bin llama-2-13b-guanaco-qlora. We provide an set of prequantized models from the Llama-2 family, as It's been a while, and Meta has not said anything about the 34b model from the original LLaMA2 paper. 5 hrs = $1. llama 13B Q4_0 6. Here's a brief example I posted a few days ago that is typical of the 2-bit experience to me: I asked a L3 70B IQ2_S (2. I think you’re even better off with 2 4090s but that price. There is no support for the cards (not just unsupported, literally doesn't work) in ROCm 5. In theory, I should have enough vRAM at least to load it in 4 bit, right? Probably cost. 🤣 Llama 3 cost more than $720 million to train . which is one GPU with 24GB VRAM vs. 86 GiB 13. Worked with coral cohere , openai s gpt models. Or check it out in the app stores Building a system that supports two 24GB cards doesn't have to cost a lot. I tried about a half dozen different generation settings - Several of the built-ins, MinP-based, mirostat with high and low tau, etc. a fully reproducible open source LLM matching Llama 2 70b Releasing LLongMA-2 16k, a suite of Llama-2 models, trained at 16k context length using linear positional interpolation scaling. I run llama 2 70b at 8bit on my duel 3090. I use Yes, many people are quite happy with 2-bit 70b models. This is using llama. large language models on 24 GB RAM. 65T/s. It's definitely 4bit, currently gen 2 goes 4-5 t/s Please, help me find models that will happily use this amount of VRAM on my Quadro RTX 6000. I think htop shows ~56gb of system ram used as well as Edit 2: Nexesenex/alchemonaut_QuartetAnemoi-70B-iMat. imo get a RTX4090 (24GB vram) + decent CPU w/ 64GB RAM instead, it's even cheaper I built an AI workstation with 48 GB of VRAM, capable of running LLAMA 2 70b 4bit sufficiently at the price of $1,092 for the total end build. 65 be compared. Or check it out in the app stores TOPICS. cpp or other similar models, you may feel tempted to purchase a used 3090, 4090, or an Apple M2 to run these models. 2. so 24gb for 400, sorry if my syntax wasn't clear enough. 4bpw is 5. large language models on 24 GB RAM Someone just reported 23. ) but there are ways now to offload this to CPU memory or even disk. Open chat 3. Inference is relatively slow going, down from around 12-14 t/s to 2-4 t/s with nearly 6k context. 0 Advanced Cooling, Spectra 2. 4bpw 70B compares with 34B quants. 65b exl2 Output generated in 5. Since only one GPU processor seems to be used at a time during inference and gaming won't really use the second card, it feels wasteful to spend $800 on another 3090 just to add the 24gb when you can pickup a P40 for a quarter of the cost. So I consider using some remote service, since it's mostly for experiments. If inference speed and quality are my priority, what is the best Llama-2 model to run? 7B vs 13B 4bit vs 8bit vs 16bit GPTQ vs GGUF vs bitsandbytes Groq's output tokens are significantly cheaper, but not the input tokens (e. I am currently running the base llama 2 70B at 0. Full offload on 2x 4090s on llama. A new card like a 4090 or 4090 24GB is useful for things other than AI inference, which makes them a better value for the home gamer. Or check it out in the app stores Cost of Training Llama 2 by Meta . Or check it out in the app stores Struggle to load Mixtral-8x7B in 4 bit into 2 x 24GB vRAM in Llama Factory Question | Help I use Huggingface Accelerate to work with 2 x 24GB GPUs. It's highly expensive, and Apple gets a lot of crap for it. go with GPTQ models and quants that fit into 24gb of VRAM, the amount of your 3090. If the model takes more than 24GB but less than 32GB, the 24GB card will need to off load some layers to system ram, which will make things a lot slower. 5T and am running into some rate limits constraints. 2-11B-Vision model locally. 0 16x lanes, 4GB decoding, to locally host a 8bit 6B parameter AI chatbot as a personal project. Q&A. Also the cpu doesn't matter a lot - 16 threads is actually faster than 32. In the end it comes down to price, the M1 cost as much as the A6000 which still needed an expensive computer to go with it. 5 tokens a second with a quantized 70b model, but once the context gets large, the time to ingest is as large or larger than the inference time, so my round-trip generation time dips down below an effective 1T/S. 5. Based on cost, 10x 1080ti ~~ 1800USD (180USDx1 on ebay) and a 4090 is 1600USD from local bestbuy. On a 24GB card (RTX 3090, 4090), you can do 20,600 context lengths whilst FA2 does 5,900 (3. Windows will have full ROCm soon maybe but already has mlc-llm(Vulkan), onnx, directml, openblas and opencl for LLMs. panchovix • If used, the RTX 3090 would be the best option. 72 tokens/s, 104 tokens, context 19, seed 910757120) Output generated in 26. 2x TESLA P40s would cost $375, and if you want faster inference, then get 2x RTX 3090s for around $1199. Since the old 65B was beyond my system, I used to run the 33B version, so hopefully Meta releases the new 34B soon and we'll get a Guanaco of that size as well. 4bpw quant. 60 MiB (model: 25145. As such, with Recently, some people appear to be in the dark on the maximum context when using certain exllamav2 model, as well as some issues surrounding windows drivers skewing The size of Llama 2 70B fp16 is around 130GB so no you can't run Llama 2 70B fp16 with 2 x 24GB. If I only offload half of the layers using llama. Even with 4 bit quantization, it won't fit in 24GB, so I'm having to run that one on the CPU with llama. Certainly less powerful, but if vram Clean-UI is designed to provide a simple and user-friendly interface for running the Llama-3. Hi there guys, just did a quant to 4 bytes in GPTQ, for llama-2-70B. zey qjwel oycdcrw fvmytj nysbey dcul vwqdlsl kpgud dzuxwyf wspex