Nvidia p40 llm reddit Reply reply B. Or check it out in the app stores Inference using 3x nvidia P40? Resources As they are from an old gen, I scored the top Open LLM Leaderboard models with my own benchmark A 4060Ti will run 8-13B models much faster than the P40, though both are usable for user interaction. The obvious budget pick is the Nvidia Tesla P40, which has 24gb of vram (but around a third of the CUDA cores of a 3090). I haven’t gotten around to trying it yet but once Triton Inference Server 2023. Watercooling with custom loops, air cooling, The primary reason I use the LLM is because I use it for therapy. The P40 is better on that platform. also no other backend can merged loras to quanted models. I bought an extra 850 power supply unit. Cost on ebay is about $170 per card, add shipping, add tax, add cooling, add GPU cpu power cable, 16x riser cables. There are ways of making them useful, but they're rather difficult and nowhere near as efficient as nvidia cards. Full machine. How practical is it to add 2 more 3090 to my machine to get quad 3090? Please help me to make the decision if the 16 core 5950x vs 8+8E i9-12900K is going to make the difference with a rtx 3090 onboard for inference or fine tuning etc down the road. 72 seconds (2. Sell them and buy Nvidia. Each loaded with an nVidia M10 GPU. I added a P40 to my gtx1080, it's been a long time without using ram and ollama split the model between the two card. 25 votes, 24 comments. Groq has some nice custom LLM inferencing chips / servers. offloaded 29/33 layers to GPU Hi folks, I’m planing to fine tune OPT-175B on 5000$ budget, dedicated for GPU. Thermal management should not be an issue as there is 24/7 HVAC and very good air flow. Noro-Hermes-3x7B. Reply reply More replies More replies More replies. Kinda sorta. Adding a P40 to my system? Same as everybody else, I'm running into VRAM issue. 7 tokens per second resulting in one response taking several minutes. true. I would like to note that you should be careful with the P100 as their NVIDIA compute is 6. p40 5 Also this post might be interesting. Expand user menu Open settings menu. That means you get double the usage out of their VR and then you will with any of the Nvidia cards pre v100/P100 (NOT P40) So that 16 gig card is a 32 gig card if you can run 16 Is there discussion anywhere specifically on the P40? I can't figure out why I can't get better performance out of mine. Looking for suggestion on hardware if my goal is to do inferences of 30b models and larger. C). It doesn’t matter what style. Running a local LLM linux server 14b or 30b with 6k to 8k context using one or two Nvidia P40s. I'm planning to build a server focused on machine learning, inferencing, and LLM chatbot experiments. Hi there I am trying to use Gryphe/MythoMax-L2-13b (found here) as I have heard it is pretty good in creative writing for a smaller model. Okay try going here on the GPU2: Nvidia Tesla P40 24GB GPU3: Nvidia Tesla P40 24GB 3rd GPU also mounted with EZDIY-FAB Vertical Graphics Card Holder Bracket and a PCIE 3. It's really important for me to run LLM locally in windows having without any serious problems that i can't solve it. «Start new chat» feels like murder. 6-mixtral-8x7b. Internet Culture (Viral) Amazing; Animals & Pets P40 will be slightly faster but doesn't have video out. Enough for 33B models. We initially plugged in the P40 on her system (couldn't pull the 2080 because the CPU didn't have integrated graphics and still needed a video out). Here's another way to think of it. **EDIT** I should have specified this: Model: bartowski/Meta-Llama-3-70B-Instruct Don't remember all of the ins and outs of Nvidia's enterprise line-up, but I do remember that some of their GPUs had 24GB of memory, but only half of it could be used per-process (e. 1. Or check it out in the app stores And it seems to indeed be a decent idea for single user LLM inference. Azuras33 • Ollama handle all memory automaticaly! llm_load_print_meta: model type = 8x22B llm_load_print_meta: model ftype = Q2_K - Medium llm_load_print_meta: model params = 140. I put in one P40 for now as the most cost effective option to be able to play with LLM's. Top 2% /r/StableDiffusion is back open after the protest of Reddit killing open API access, which Get the Reddit app Scan this QR code to download the app now. So total $725 for 74gb of extra Vram. Sure, the 3060 is a very solid GPU for 1080p gaming and will do just fine with smaller (up to 13b) models. You could put together a machine with 2 at 48GB To be honest, it 1000% feels like a real dev is tricking me and took control of the LLM and is typing the questions. This means only very small models can be run on P40. 5"). RTX 3090 TI + RTX 3060 D. 2x 2tb SSDs Linux Ubuntu TL;DR. I've used the M40, the P100, and a newer rtx a4000 for training. Consider power limiting it, as I saw that power limiting P40 to 130W (out of 250W standard limit) reduces its speed just by ~15-20% and makes it much easier to cool. LLM360 has released K2 65b, a fully reproducible open source LLM matching Llama 2 70b upvotes . essentially 2 GPUs on one card, each with access to half the total VRAM). GTX 1080 Ti as main/first GPU. Electricity cost is also not an issue. Hey Reddit! I'm debating whether to build a rig for large language model (LLM) work. Add your thoughts and get the conversation going. Personally, I would give the Nvidia the middle finger for price gouging, and sit on 1-2 3090s, 7900s or a MI100 until AMD/Intel come out with a sane 32GB+ card. With GPUs you need to load the data into VRAM and that is only going to be available for that GPUs calculations it's not a shared memory pool. However running stable diffusion each card runs it 5-6x faster than each Tesla P40 and only about 1/2 as fast as a 4090 which I thought was impressive. Keep in mind cooling it will be a problem. 12x 70B, NVidia H200 achieves nearly 12,000 tokens/sec on Llama2-13B with TensorRT-LLM Nvidia Tesla P40 Pascal architecture, 24GB GDDR5x memory [3] A common mistake would be to try a Tesla K80 with 24GB of memory. 52 GiB Get the Reddit app Scan this QR code to download the app now. Use it! any additional CUDA capable cards will be used and if they are slower than the P40 they will slow the whole thing down Rowsplit is key for speed Get the Reddit app Scan this QR code to download the app now. And for $200, it's looking pretty tasty. NVIDIA P40 24gb on ebay is cheap but you need to diy cooling solution. Anyone try this yet, especially for 65b? I think I heard that the p40 is so old that it slows down the 3090, but it still might be faster from ram/cpu. Just make sure you have enough power and a cooling solution you can rig up, and you're golden. but i cant see them in the task manager The new NVIDIA Tesla P100, powered by the GP100 GPU, can perform FP16 arithmetic at twice the throughput of FP32. 70B will require a second P40. (Does your motherboard have 3 slots for it?) It can be found for around $250, second hand, online. As cliche as it is, these things are basically shovels in a gold rush, everyone will have a use for large amounts of compute in the coming years The community for Old School RuneScape discussion on Buy a used Tesla 24Gb p40, by the way can I use it with the existing 3060 12GB to have a total 36Gb? This won't be useful for LLM's as you will be limited to the 12gb vram when loading a model (or at least this is what I read when also considering a P40). Obviously it has more memory. Large local LLM’s like Falcon 170b, or it’s possible local models will fall very seriously behind and we’ll move back to the cloud. It's a frontend similar to chatgpt, but with the possibility to download several models (some of them are extremely heavy other are more light). L40S. C and max. If the goal is to test a theory, you I'm planning to do a lot more work on support for the P40 specifically. Training is one area where P40 really don't shine. 04 running inside each Windows installation. What is your budget (ballpark is okay)? Hi everyone, I have decided to upgrade from an HPE DL380 G9 server to a Dell R730XD. MI25s are enticingly cheap, but they're also AMD, which is the red headed stepchild of AI right now. RTX 3090 TI + Tesla P40 Note: One important piece of information. very detailed pros and cons, but I would like to ask, anyone try to mix up one P40 for vRAM size and one P100 for HBM2 bandwidth for a dual card ingerence system? What could be the results? 1+1>2 or 1+1<2? :D Thanks in advance. Is there a straightforward tutorial somewhere specifically on the P40? People seem to consider them both as about equal for the price / performance. here is P40 vs 3090 in a 30b int4 P40 Output generated in 33. However, whenever I try to run with MythoMax 13B it generates extremely slowly, I have seen it go as low as 0. With the latter having nearly 4x memory bandwidth, you're never going to see 4060Ti approach the 3090 in anything but most contrived benchmarks involving DLSS3 frame generation. That puts even the largest LLM models in range. And if you go on ebay right now, I'm seeing RTX 3050's for example for like $190 to $340 just at a glance. Would the whole "machine" suffice to run models like MythoMax 13b, Deepseek Coder 33b and CodeLlama 34b (all GGUF) mlc-llm doesn't support multiple cards so that is not an option for me. and if the age and low-end components of the existing PC are likely to introduce a new bottleneck despite having a P40 in the mix. gguf Blue-Orchid-2x7b-Q8_0. Q4_K_M. So nvidia plans to ship 500k H100 GPUs this year (triple that in 2024). 24go of vram and can output 10-15 token/sec. I bench marked the Q4 and Q8 quants on my local rig (3xP40, 1x3090). If anybody has something better on P40, please share. For AMD it’s similar same generation model but could be like As far as i can tell it would be able to run the biggest open source models currently available. But with Nvidia you will want to use the Studio driver that has support for both your Nvidia cards P40/display out. The idea now is to buy a 96GB Ram Kit (2x48) and Frankenstein the whole pc together with an additional Nvidia Quadro P2200 (5GB Vram). "Pascal" was the first series of Nvidia cards to add dedicated FP16 compute units, however despite the P40 being part of the Pascal line, it lacks the same level of FP16 performance as other Pascal-era cards. they're interested in deploying it and using it to assist them in other tasks. 14b like Phi 3 medium is fast on even a P40. GPU: 4090 CPU: 7950X3D RAM: 64GB OS: Linux (Arch BTW) My GPU is not being used by OS for driving any display Idle GPU memory usage : 0. GPUS: Start with 1x and hopefully expand to 6x Nvidia Tesla P40 ($250-300 each) GPU-fanshroud: 3D printed from ebay ($40 for each GPU) GPU-Fan: 2x Noctua NF-A4x20 ($40 for each GPU) GPU-powercable: Chinese "For Nvidia Tesla M40 M60 P40 P100 10CM" ($10 for each GPU) PCI-E riser: Chinese riser ($20 for each GPU) PSU: 2x Corsair RM1000e ($200-250 Disclaimer: I'm just a hobbyist but here's my two cents. Use it. RTX 4090's Training throughput/Watt is Does Nvidia's new "System Memory Fallback for Stable Diffusion" also compatible with LLM in general? Discussion Hi all, today Nvidia released a new driver version that appears to allow the GPU to use system memory instead of crashing when it runs out, seen here: Stupid reddit markdown. I have really found a good sweet spot I think, using 2x7 and 3x7 models. Plus finding a board for 10x cards is gonna suck. The most important of those are the number of codebooks and codebook size. Isn't that almost a five-fold advantage in favour of 4090, at the 4 or 8 bit precisions typical with local LLMs? I made a mega-crude pareto curve for the nvidia p40, with ComfyUI (SDXL), also Llama. So IMO you buy either 2xP40 or 2x3090 and call it a day. Far cheaper than a second 3090 for the additional 24gb. The sweet spot for bargain-basement AI cards is the P40. If I spoke about what was going on, it literally might have got me killed. Dell and PNY ones only have 23GB (23000Mb) but the nvidia ones have the full 24GB (24500Mb). 0 vs the P40 as a 6. 7T), training code and even data cleansing pipeline! Welcome to the official subreddit of the PC Master Race / PCMR! All PC-related content is welcome, including build help, tech support, and any doubt one might have about PC ownership. But, P40 is specific to Deep Learning. Be the first to comment Nobody's responded to this post yet. and non LLM ML models for product decisions, marketing etc. observed so far while inferencing is 55 deg. as quantization improvements have allowed people to finetune smaller models on just 12gb of vram! meaning consumer hardware is now viable if a bit slow. 24GB of GDDR5 and enough tensor cores to actually do something with it for you are correct. I don't think it's going to be a great route to extending the life of old servers. Fully AMD: Ryzen 7600 + Radeon 6800 XT. 24 tokens/s, 257 tokens, context 1701, seed 1433319475) vs 3090 (cuda) I had been thinking of making something similar after seeing the nvidia-pstate tool was released -- a program that can use nvidia-pstate to automatically set the power state for the card based on activity. I have a P40 and a 3090 and the P40 is no slouch. there needs to be consumer hardware capable of running a 70b Dolphin is a very good llm but it's also pretty heavy. I also have a 3090 in another machine that I think I'll test against. Windows 11 23H2 Above 4G and ReBAR enabled in BIOS. al. Or check it out in the app stores TOPICS Yet another state of the art in LLM quantization . More VRAM. A few people actually. NVIDIA's got lots of nice toys like their grace hopper based tech some companies are making workstations / servers out of. 24GB is the most vRAM you'll get on a single consumer GPU, so the P40 matches that, and presumably at a fraction of the cost of a 3090 or 4090, but there are still a number of open source models that won't fit there unless you shrink them considerably. gguf. Isn't that almost a five-fold advantage in favour of 4090, at the 4 or 8 bit precisions typical with local LLMs? Hey Reddit! I'm debating whether to build a rig for large language model (LLM) work. 4 channels isn't going to not work, it is just going to be on the slow side, especially with larger input context. True cost is closer to $225 each. Or check it out in the app stores TOPICS. Its really insane that the most viable hardware we have for LLMs is ancient Nvidia GPUs. P40 as the second GPU mainly for AI applications. We had 6 nodes. BUT there are 2 different P40 midels out there. That will be best bang for the buck in terms of vram. It's a very attractive card for the obvious reasons if it can be made to perform well. Current specs: Core i3-4130 16GB DDR3 1600MHz (13B q5 GGML is possible) It doesn’t matter what style. ) (If you want my opinion if only vram matters and doesn't effect the speed of generating tokens per seconds. 79 tokens/s, 94 tokens, context 1701, seed 1350402937) Output 55 votes, 60 comments. Gaming. Currently the best performance per dollar is the 3090, as you can pick up used ones on ebay for 750-900 usd. 98 Test Prompt: make a list of 100 countries and their currencies in MD table use a column for numbering Interface: text generation webui GPU + CPU Inference You can follow the tutorials for building gaming PC's that are all over. TLDR: At +- 140 watts you get 15% less performance, for saving 45% power (Compared to 250W default mode); #Enable persistence mode. sudo nvidia-smi -pl 140 Was looking for a cost effective way to train voice models, bought a used Nvidia Tesla P40, and a 3d printed cooler on eBay for around 150$ and crossed my fingers. However, now that Nvidia TensorRT-LLM has been released with even more optimizations on Ada (RTX 4xxx) it’s likely to handily surpass even these numbers. I did a quick test with 1 active P40 running dolphin-2. I saw a couple deals on used Nvidia P40's 24gb and was thinking about grabbing one to install in my R730 running proxmox. A100. Since Cinnamon already occupies 1 GB VRAM or more in my case. The difference is the VRAM. 04, and additionally WSL with Ubuntu 22. They said that between the p40 and a 3060, the 3060 is faster for inference by a good amount. 3060 12gb isn't half bad if you want a more modern architecture. 4 already installed. 5 inference and small finetuning, or maybe an LLM for for an exhibition, do you know how's the performance with those? i don't need extreme performance but it would be way easier and cheaper running it local instead of renting a server Hi reader, I have been learning how to run a LLM(Mistral 7B) with small GPU but unfortunately failing to run one! i have tesla P-40 with me connected to VM, couldn't able to find perfect source to know how and getting stuck at middle, would appreciate your help, thanks in advance That should help with just about any type of display out setup. What is that i found this thread randomly while searching for tha same-ish thing so i'll ask as well, i'm planning to buy a couple of those as well to run SD1. The 250W per card is pretty overkill for what you get You can limit the cards used for inference with CUDA_VISIBLE_DEVICES=x,x. Hi I have a dual 3090 machine with 5950x and 128gb ram 1500w PSU built before I got interested in running LLM. 3x Nvidia P40 on eBay: $450 Cooling solution for the P40s: $30 (you'll need to buy a fan+shroud kit for cooling, or just buy the fans and 3D print the shrouds) Power cables for the P40s: $50 Open air PC case/bitcoin mining frame: $40 Cheap 1000W PSU: $60 What CPU you have? Because you will probably be offloading layers to the CPU. Nvidia Tesla P40 24 694 250 200 Nvidia 2 x RTX 4090 The 3090 is about 1. RTX 4090's Training throughput and Training throughput/$ are significantly higher than RTX 3090 across the deep learning models we tested, including use cases in vision, language, speech, and recommendation system. It is Turing (basically a 2080 TI), so its not going to be as optimized/turnkey as anything Ampere (like the a6000). My rig has a 3090ti and 12900k plus 64 GB of system RAM. So, the fun part of these mi 25s is that they support 16 bit operations. I know 4090 doesn't have any more vram over 3090, but in terms of tensor compute according to the specs 3090 has 142 tflops at fp16 while 4090 has 660 tflops at fp8. Get the Reddit app Scan this QR code to download the app now. That's already double the P40's iterations per second. Alltogether, you can build a machine that will run a lot of the recent models up to 30B parameter size for under $800 USD, and it will run the smaller ones relativily easily. The P40 offers slightly more VRAM (24gb vs 16gb), but is GDDR5 vs HBM2 in the P100, meaning it has far lower bandwidth, which I believe is important for inferencing. in that case your 48gb rig can at most support llama 70b running at Q5 I got a little intoxicated and ordered an Nvidia Quadro P6000 and have been loving it. It works nice with up to 30B models (4 bit) with 5-7 tokens/s (depending on context size). e. Dell 7810 Xeon 2660 v4 192 gigs of ram 1x 3060 12 gig. 238k cuda. ran into some issues early on with bits and bytes. Automatic1111’s interface gives you a lot of flexibility to do everything from making a photo stylized (like what you could do with talent and photoshop) to generating thousands of custom avatars for a forum in just a few clicks. Please use our Discord server instead of supporting a company that acts against its Hello all! The TLDR is that I’m trying to set up a personal rig with a Tesla P40 I was able to buy cheaply, for hobby AI projects (I was recently a grad student doing this research, but I chose to leave and downgrade my AI involvement to a hobby). I was wondering if anyone had any experience adding a P40 (or similar high memory GPU) to an existing dual GPU setup for just the memory? resources: reservations: devices: - driver: nvidia device_ids: ['0,1,3,4'] capabilities: [gpu] If you look at the device IDs there you can see that I'm skipping device 2, which is the host GPU (a 1030 with 2gb vram) so that it doesn't get used in Get the Reddit app Scan this QR code to download the app now. Had a spare machine sitting around (Ryzen 5 1600, 16GB RAM) so I threw a fresh install of Ubuntu server 20. On the first 3060 12gb I'm running a 7b 4bit model (TheBloke's Vicuna 1. cpp logs to decide when to switch power states. Benchmark videocards performance analysis: PassMark - G3D Mark, PassMark - G2D Mark, Geekbench - OpenCL, NVIDIA RTX 5090 new rumored specs: 28GB GDDR7 and 448-bit bus - VideoCardz. The problem is, I have P40 has lots of memory (24GiB), 100 has HBM which is important if you're memory bandwidth limited. At a rate of 25-30t/s vs 15-20t/s running Q8 GGUF models. Which is not ideal setup, but in current distorted market it can still be a viable low-end option. As I've been looking into it, I've come across some articles about Nvidia locking drivers behind vGPU licensing. 24GB, and faster than a P40, and has a blower fan. This can be really confusing. System is just one of my old PCs with a B250 Gaming K4 motherboard, nothing fancy Works just fine on windows 10, and training on Mangio-RVC- Fork at fantastic speeds. Llama. Reply reply Top 1% Rank by size I'm trying to run Ollama in a VM in Proxmox. Would start with one P40 but would like the option to add another later. completely without x-server/xorg. Father's day gift idea for the man that has everything: nvidia 8x h200 server for a measly $300K A open source LLM that includes the pre-training data (4. An added observation and related question: Looking at Nvidia-smi while inferencing I noticed that although it reaches 100 pct utilization intermittently, the card never goes above 102 watts in power consumption (despite the P40 being capable of 220 Watts) and temps never go very high (idle is around 41 deg. There's a lot of stuff that happened a long time ago that I had to keep silent about. My budget for Refurbished Nvidia Tesla P40 accelerator Hardware setup. Most people here don't need RTX 4090s. If you're willing to tinker, I recommend getting the Nvidia Tesla P40, to add on to the 1080Ti. 55 seconds (4. The pure tflops that both the 3090 and 4090 can deliver are incredible, and if multi card inference is properly Monitor power usage and temperatures of the cards with the nvidia-smi command. I’ve found that Hey Folks, Still trying to dive into the local LLM space with more RAM, but sadly my T420+P40 cooling situation has been a battle. Some have run it at reasonably usable speeds using three or four p40 and server hardware for less than We would like to show you a description here but the site won’t allow us. Intel i7-10700K on a Z490 motherboard. Was looking for a cost effective way to train voice models, bought a used Nvidia Tesla P40, and a 3d printed cooler on eBay for around 150$ and crossed my fingers. i should've been more specific about it being the only local LLM platform that uses tensor cores right now with models fine-tuned for consumer GPUs. I was wondering if adding a used tesla p40 and splitting the model across the vram using ooba booga would be faster than using ggml cpu plus gpu offloading. $100. Welcome to the official subreddit of the PC Master Race / PCMR! All PC-related content is welcome, including build help, tech support, and any doubt one might have about PC ownership. Or check it out in the app stores 2 x Tesla P40's, 24GB RAM each = 48GB ($200ea = $400) 2 x PCI Riser cards This is the point of the nvlink with nvidia. Has 24GB capacity and 700GB/s memory speed. com) 27 votes, 56 comments. This Subreddit is community run and does not represent NVIDIA in any capacity unless specified. It sounds like a good solution. The difference in performance for GPUs running on x8 vs x16 is fairly small even with the latest cards. Do note you won't be able to see it in the task manager unless you pull some regedit shenanigans. But you can do a hell of a lot more LLM-wise with a P40. View community ranking In the Top 5% of largest communities on Reddit. No NVIDIA Stock Discussion. Valheim; Genshin Impact; literally no other backend besides possibly HF transformers can mix nvidia compute levels and still pull good speeds, not crash, etc. I've been running a P40 in a 4x slot and while it's probably slower I can't say it's noticeably slower. most people who are interested in open source LLMs aren't interested in training it from scratch. Given some of the The Tesla P40 is much faster at GGUF than the P100 at GGUF. Share Add a Comment. I started with 3x Nvidia Tesla P40s since they have 24GB of VRAM which is really what's needed for these machine learning tasks, but then came across a good deal on these awesome Titan RTX cards. I don't have anything against AMD, but Nvidia pretty much owns the AI market so everyone builds and tests their products to run on them. Like 30b/65b vicuña or Alpaca. I built a small local llm server with 2 rtx 3060 12gb. I kind of think at that level you might just be better off putting in low bids on 10XX/20XX 8GB nvidia cards until you snag one of those up for your budget range. 79 tokens/s, 94 tokens, context 1701, seed 1350402937) Output generated in 60. doing their own things. I am also open to other model suggestions if anyone has a good one. But it depends what LLM you use, if you use near 48GB VRAM, it doesn't matter much if you put all you could into the 4090 VRAM, the P40 is full as well. Or check fine-tuning, etc. Ideally, I'd like to run 70b models at good speeds. While the P40 has more CUDA cores and a faster clock speed, the total throughput in GB/sec goes to the P100, with 732 vs 480 for the P40. g. I have the henk717 fork of koboldai set up on Ubuntu server with ~60 GiB of RAM and my Nvidia P40. Resources Is Nvidia p40 supported by this quants? Would a buying a p40 make bigger models run noticbly faster? If it does is there anything I should know about buying p40's? Like do they take normal connectors or anything like 🐺🐦⬛ LLM Comparison/Test: 2x 34B Yi (Dolphin, Nous Capybara) vs. Valheim; Genshin Impact; Minecraft; It’s possible that it will, but it’s also possible we’ll see more v. 6B to 120B (StableLM, DiscoLM German 7B, Mixtral 2x7B, Beyonder, Laserxtral, MegaDolphin) upvotes · comments r/LocalLLaMA I'd like to get a M40 (24gb) or a P40 for Oobabooga and StableDiffusion WebUI, among other things (mainly HD texture generation for Dolphin texture If anyone is contemplating the use of a p40 and they would like me to test something for them let me know. Internet Culture (Viral) Amazing; Animals & Pets In the latest Nvidia driver, the video card will be able to take the missing video memory from the RAM. With rapid advancements in AI, I'm Finally joined P40 Gang. Original Post on github (for Tesla P40): JingShing/How-to-use-tesla-p40: A manual for helping using tesla p40 gpu (github. If your application supports spreading load over multiple cards, then running a few 100’s in parallel could be an option (at least, that’s an option im exploring) 🐺🐦⬛ LLM Comparison/Test: 6 new models from 1. Sort by: Best Xeon E5-2673, 4 core (8 processors) but with Nvidia Quadro K620 AND Nvidia Tesla p40 excelerator Get the Reddit app Scan this QR code to download the app now. Get app Get the Reddit app Log In Log in to Reddit. and my outputs always end up spewing out garbage after the second Hello, I am just getting into LLM and AI stuff so please go easy on me. It does not work with larger models like GPT-J-6B because K80 is not Do you have any LLM resources you watch or follow? I’ve downloaded a few models to try and help me code, help write some descriptions of places for a WIP Choose Your Own Adventure book, etc but I’ve tried Oobabooga, KoboldAI, etc and I just haven’t wrapped my head around Instruction Mode, etc. temp. (i mean like solve it with drivers update and etc. 1 so some libraries might be a bit wonky. 2 nVidia P40s at 24GB each. So, as you probably all know, geforce now's server machines use a Tesla P40, a very powerful card that sadly is not optimazed for gaming, in the best case games use around 50% of its power, leaving us with quite low framerates compared to even a gtx 1060. Which brings to the P40. If this is going to be a "LLM machine", then the P40 is the only answer. They did this weird thing with Pascal where the GP100 (P100) and the GP10B (Pascal Tegra SOC) both support both FP16 and FP32 in a way that has FP16 (what they call Half Precision, or HP) run at double the speed. Be aware that Tesla P40 draws 350W of power, requires a PCIe 3 x16 socket, and requires “Above 4G decoding” in BIOS. 4060_ti: 353 AI TOPS 3090: 285 AI TOPS These kinds of comparisons on Nvidia's site make me lol. But yeah the RTX 8000 actually seems reasonable for the VRAM. Possibly because it supports int8 and that is somehow used on it using its higher CUDA 6. This would tend to support the theory that the memory bandwidth on the 4060 ti is A place for everything NVIDIA, come talk about news, drivers, rumors, GPUs, the industry, show-off your build and more. If Everyone, i saw a lot of comparisons and discussions on P40 and P100. Some observations: the 3090 is a beast! 28 If you're solely looking to build a computer to run LLMs, you'd likely do better on a server board with a TON of 12GB RTX 3060's running at PCIE X4, or better yet, four Nvidia P40's or five I would like to upgrade it with a GPU to run LLMs locally. I recently got my hands on an Nvidia Tesla M40 GPU with 24GB of VRAM. V100 (experimental) Get the Reddit app Scan this QR code to download the app now. After I connected the video card and decided to test it on LLM via Koboldcpp I noticed that the generation speed from ~20 tokens/s dropped to ~10 tokens/s. So the p40 is Nvidia 3090(24GB): $900-1k-ish. My use case is not gaming, or mining, but rather finetuning and playing around with local LLM models, these typically require lots of vram and cuda cores. Looks like this: X-axis power (watts), y-axis it/s. 5x as fast as a P40. GPT4 was trained on 25k A100s in ~3months. Both are dual slot though. Internet Culture (Viral) Amazing Just buy a Nvidia P40. Nvidia griped because of the difference Relvant update: P40 build specs and benchmark data for anyone using or interested in inference with these cards : r/LocalLLaMA (reddit. The unofficial but A place for everything NVIDIA, come talk about news, drivers, rumors, GPUs, the industry, show-off your build and more. If the m40 works then the p40 probably should as well. The point is, if 1500$ is Being able to then access StableDiffusion from your phone or tablet. A place for everything NVIDIA, come talk about news, drivers, rumors, GPUs, the industry, show-off your build and more. People often pick up a nvidia P40 for increasing the VRAM amount. 1 4bit) and on the second 3060 12gb I'm running Stable Diffusion. I ran all tests in pure shell mode, i. reading time: 47 minutes. Don't know if you got this answered. Valheim; Genshin Impact; NVIDIA RTX 3090 = 936 GB/s NVIDIA P40 = 694 GB/s Dual channel DDR5 5200 MHz RAM on CPU only = 83 GB/s If I can find a deal for a 128GB Mac, I will upgrade. they are registered in the device manager. Flame my choices, recommend me a different way, and any ideas on benchmarking 2x P40 vs 2x P100 As long as your cards are connected with at least PCIe v3 x8 then you are fine for LLM usage (nvidia-smi will tell you how the cards are Quadro cards seem to have kinda bad value, most people on this sub will recommend multiple 3090s, I myself have, due to rather limited budget, opted for dual a Tesla P40 setup (basically 24gb 1080; they have not yet arrived, and the information given on this sub on how useful they are kinda contradicts itself sometimes, apparently these cards can't run 4-bit models but 8-bit I also have one and use it for inferencing. Watercooling with custom loops, air Hello, I am just getting into LLM and AI stuff so please go easy on me. AMD, Intel, et. 341/23. This means you cannot use GPTQ on P40. I recently bought 2x P40 for LLM Get the Reddit app Scan this QR code to download the app now. com) Seems you need to make some registry setting changes: After installing the driver, you may notice that the Tesla P4 graphics card is not detected in the Task Manager. A few details about the P40: you'll have to figure out cooling. RTX 4090 vs RTX 3090 Deep Learning Benchmarks. The Tesla P40 and P100 are both within my prince range. Currently exllama is the only option I have found that does. Are you asking what is literally being done to process 16K tokens into an LLM model? From cuda sdk you shouldn’t be able to use two different Nvidia cards has to be the same model since like two of the same card, 3090 cuda11 and 12 don’t support the p40. Inference That is approximate to running a training job on 1 nvidia 3090 for one month or 2 nvidia 3090 for 2 weeks. I have it attached to an ancient motherboard, but when I attach a 3060 12GB to the same motherboard, performance doesn't seem to take much of a hit. At $180 you are paying P40 prices for inferior cards too. There was an Nvidia engineer in here the other day going through the math behind it. This is my setup: - Dell R720 - 2x Xeon E5-2650 V2 - Nvidia Tesla M40 24GB - 64GB DDR3 I haven't made the VM super powerfull (2 cores, 2GB RAM, and the Tesla M40, running Ubuntu 22. Fine tuning too if possible. If you have $1600 to blow on LLM GPUs, then do what everybody else is doing and pick up two used 3090s. And the P40 GPU was scoring roughly around the same level of an RX 6700 10GB. Reply reply I just recently got 3 P40's, only 2 are currently hooked up. Therefore, you need to modify the registry. nvidia-smi -ac 3003,1531 unlocks the core clock of the P4 to 1531mhz This will make the P40 show up in nvidia-smi and device manager. I want to use 4 existing X99 server, each have 6 free PCIe slots to hold the GPUs (with the remaining 2 slots for NIC/NVME drives). Performance is very up there with Nvidia from the benchmarks I have seen for a considerably lower price. Note for the K80, that's 2 GPUs in it, but for SD that doesn't combine well in the software. i swaped them with the 4060ti i had. My plan is just to run ubuntu, possibly vm but may not. Tesla P40 C. NVIDIA Tesla P40 24GB Proxmox Ubuntu 22. The GP102 (Tesla P40 and NVIDIA Titan X), GP104 (Tesla P4), and GP106 GPUs all support instructions that can The P40 was designed by Nvidia for data centers to provide inference, and is a different beast than the P100. P40 works better than expected for just messing around when paired with a 3060 12gig. Dell and PNY ones and Nvidia ones. But the p40 outputs about as fast as I can read anyway for 70b. Q8_0. Even You can build a box with a mixture of Pascal cards, 2 x Tesla P40's and a Quadro P4000 fits in a 1x 2x 2x slot configuration and plays nice together for 56Gb VRAM. I would recommend looking at a dell R7910/T9710 workstation or server and the Nvidia P40. Note: Reddit is dying due to terrible leadership from CEO /u/spez. 04), however, when I try to run ollama, all I get is "Illegal instruction". But it should be lightyears ahead of the P40. Two laptops and one workstation here are dualboots of Win11 or Win10 and Ubuntu 22. HOWEVER, the P40 is less likely to run out of vram during training because it has more of it. A P40 will run at 1/64th the speed of a card that has real FP16 cores. Internet Culture (Viral) Optimal Hardware Specs for 24/7 LLM Inference (RAG) with Scaling Requests - CPU, GPU, RAM, MOBO Considerations In terms of GPUs, what are the numbers I should be looking at? Do I need one GPU with high VRAM for LLM inference/generation is very intensive. Log In / Sign Up; Advertise on Reddit; Shop Collectible Avatars; Writing this because although I'm running 3x Tesla P40, it takes the space of 4 PCIe slots on an older server, plus it uses 1/3 of the power. #Set power limit to 140Watts. Reply reply NVIDIA launches GeForce RTX 40 SUPER series: $999 RTX 4080S, $799 RTX 4070 TiS and $599 I am also confused by the persistence of Windows in this domain. 04 on to play around with some ML stuff. 04 VM w/ 28 cores, 100GB allocated memory, PCIe passthrough for P40, dedicated Samsung SM863 SSD This subreddit has gone Restricted and reference-only as part of a mass protest against Reddit's recent API That is a fair point. Granted, the 3090 will be faster by about a factor of 2 or 3. I would probably split it between a couple windows VMs running video encoding and game streaming. 5-32B today. . You need ram and vram. A30. My guess is that it will be better to fill up the server with more P40's before I start upgrading the CPU. NVidia have given consumers some absolutely incredible hardware with amazing capabilities. So load data into the P40, then only the P40 it will only be able to use that for it's calculations. If you are limited to single slot, and have a slightly higher budget avilable, RTX A4000 is quick (roughly double of P40/100 which are surprisingly similar in performance), has 16GiB and fits in a single slot, but is quite long (9. If you want to try some local llm, you can try to host a docker of Serge (you can find it on GitHub). If you choose to forge ahead with those GPUs, expect headaches and little to no community support. Each with a NVIDIA GPU (P40, RTX3070m, GTX970m). You can limit the power with nvidia-smi pl=xxx. The build I made called for 2X P40 GPU's at $175 each, meaning I had a budget of $350 for GPU's. 1x p40. 10 is released deployment should be relatively straightforward (yet still much more complex than With interest I've been playing around with a bit of LLM generative text and Stable Diffusion. People seem to consider them both as about equal for the price / performance. Some RTX 4090 Highlights: 24 GB memory, priced at $1599. You seem to be monitoring the llama. AQLM method has a number of hyperparameters. 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. You'll have 24 + 11 = 35GB VRAM total. 2x TESLA P40s would cost $375, and if you want faster inference, then get 2x RTX 3090s for around $1199. nvidia-smi -pm ENABLED. I've only used Nvidia cards as a passthrough so I can't help much with other types. The 3090 is about 1. I really want to run the larger models. This card can be found on ebay for less than $250. Here is one game I've played on the P40 and plays quite nicely DooM Eternal is Get the Reddit app Scan this QR code to download the app now. gguf Wizard-Kun-Lake_3x7B-MoE_Q5_K_M Get the Reddit app Scan this QR code to download the app now. You can split up people into multiple vehicles. Smaller codebook sizes allow for faster inference at the cost of slightly worse performance. I personally run voice recognition and voice generation on P40. com Discussion That's not large upgrade a paradigm shift is coming. I've tried Super excited for the release of qwen-2. Will there be: a, Ryzen/Nvidia issue I need to beware? Cheapest way is to get refurb hardware. 62 B llm_load_print_meta: model size = 48. Average it/s for Mixtral models is 20. when TensorRT-LLM came out, Nvidia only advertised it for their server GPUs TensorRT-LLM is rigorously tested on the following GPUs: H100. Est. Note the P40, which is also Pascal, has really bad FP16 performance, for some reason I don’t understand. I bought a DELL Optiplex 7020 Minitower, installed Ubuntu on it, and was able to see the card using lspci ; however, no matter Hoping will be even more valuable if/when nvidias tensorRT-LLM framework delivers the promise of efficient multi gpu scaling ( which doesn’t do well past 2 cards right now ). I am looking for a strong creative writing ability and decent output speed. 0 riser cable P40s each need: - ARCTIC S4028-6K - 40x40x28 mm Server Fan You could do dual p40s or dual 3090s. Here, I provide an in-depth analysis of GPUs for deep learning/machine learning and explain what is the best GPU for your use-case and budget. Now, here's the kicker. While doing some research it seems like I need lots of VRAM and the cheapest way would be with Nvidia P40 GPUs. Everything else is on 4090 under Exllama. i have windows11 and i had nvidia-toolkit v12. But for the price of 1x 3090, one could get 2 or 3 P40 for inference plus 2 or 3 P100 for training, and swap around as needed. So I work as a sysadmin and we stopped using Nutanix a couple months back. Update Nvidia drivers to the very latest ones. Actually, I have a P40, a 6700XT, and a pair of ARC770 that I am testing with also, trying to find the best low cost solution that can also be Get the Reddit app Scan this QR code to download the app now. Comparative analysis of NVIDIA Tesla P40 and NVIDIA Tesla M10 videocards for all known characteristics in the following categories: Essentials, Technical info, Video outputs and ports, Compatibility, dimensions and requirements, API support, Memory. Initially we were trying to resell them to the company we got them from, but after months of them being on the shelf, boss said if you want the hardware minus the disks, be my guest. Btw, you can get by one 1 P40 or 3090 with a 70b model. Most important of all, do not use both cards together in tandem, for they are too different, the 2060 will only impede the performance of my P40 and it will be the same with the 3060. In nvtop and nvidia-smi the video card jumps from 70w to 150w (max) out of 250w. Get a refurb workstation and a used P40 plus cooling solution. Internet Culture (Viral) Tesla P40's give you a solid 24gb of vram per ~$200; Pascal will be supported for some time longer IIUC. Internet Culture (Viral) Amazing; Animals & Pets A place for everything NVIDIA, come talk about news, drivers, rumors, GPUs, the industry, show-off your build and more. • Do you know if the same applies for text2img? I'm playing with the idea of hosting both a text2img model and an llm and I'm trying to figure out what the When I install the P40 drivers it detects the P40, but once I install the 1080 Ti drivers, it refuses to detect the P40 anymore. A open source LLM that includes the pre-training data Got a couple of P40 24gb in my possession and wanting to set them Dual Tesla P40 local LLM Rig i just also got two of them on a consumer pc. On the other hand, 2x P40 can load a 70B q4 model with borderline bearable speed, while a 4060Ti + partial offload would be very slow. Funny Share Add a Comment. cpp and koboldcpp recently made changes to add the flash attention and KV quantization abilities to the P40. Most models are designed around running on Nvidia video cards. ifkd brntj zhcvwxt mufy vuqo quelwar zekpof sbmealc cwjraoy svjile