Nvidia p40 llm reddit.

Nvidia p40 llm reddit 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. I bought 4 p40's to try and build a (cheap) llm inference rig but the hardware i had isn't going to work out so I'm looking to buy a new server. Alternatively 4x gtx 1080 ti could be an interesting option due to your motherboards ability to use 4-way SLI. they are registered in the device manager. not much of them can fit into 4090's memory. Works great with ExLlamaV2. , 30B-70B q4 models) without breaking the bank, often found for remarkably low prices on the second-hand market. Subreddit to discuss about Llama, the large language model created by Meta AI. Posted this before, but here are some benchmarks: System specs: Dell R720xd 2x Intel Xeon E5-2667v2 (3. I've used the M40, the P100, and a newer rtx a4000 for training. 0 riser cable P40s each need: - ARCTIC S4028-6K - 40x40x28 mm Server Fan - Adapter to convert the tesla power connector to dual 8 pin PCIE power. May 16, 2023 · Hi folks, I’m planing to fine tune OPT-175B on 5000$ budget, dedicated for GPU. 70 extra per month in power. The difference is the VRAM. 6 月，随着开源大模型(LLM: Large Language Model)越来越多，在本地部署大模型成为触手可及的事情，高性能消费级显卡如 4090、3080Ti 可以满足基本的部署需求，当然还可以直接租赁云厂商提供的 GPU 算力。 We would like to show you a description here but the site won’t allow us. I think the sweet spot for the P40 would be if a 7B model with 16K context is used. Currently the best performance per dollar is the 3090, as you can pick up used ones on ebay for 750-900 usd. A P40 will run at 1/64th the speed of a card that has real FP16 cores. 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. nvidia-smi -q nvidia-smi --ecc-config=0 reboot nvidia-smi -q (confirm its disabled) I got a Razer Core X eGPU and decided to install in a Nvidia Tesla P40 24 GPU and see if it works for SD AI calculations. P40 has more Vram, but sucks at FP16 operations. I run 70b models, type: 2. 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. yarn-mistral-7b-128k. RTX 3090 TI + RTX 3060 D. If you want faster RP with the P40, this model is worth trying. temp. Budget for graphics cards would be around 450$, 500 if i find decent prices on gpu power cables for the server. Is it still worth it to get a 4090? I mainly want to create Lora and train PEFT for some small models. But it's not the best at AI tasks. If you look at Nvidia's own benchmarks compute capability >= 8. But be aware Nvidia crippled the fp16 performance on the p40. Use it. If you’re running llama 2, mlc is great and runs really well on the 7900 xtx. Anything better than 4090 from Nvidia is too expensive. 1 CUDA is all A place for everything NVIDIA, come talk about news, drivers, rumors, GPUs, the industry, show-off your build and more. The way things are going with openai I believe I'm going to want an opensource LLM sooner than later. completely without x-server/xorg. Inference speed is determined by the slowest GPU memory's bandwidth, which is the P40, so a 3090 would have been a big waste of its full potential, while the P6000 memory bandwidth is only ~90gb/s faster than the P40 I believe. Q5_K_M quantisation. 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 A 4060Ti will run 8-13B models much faster than the P40, though both are usable for user interaction. Although the computing power itself may not keep up with the times, it is a NVIDIA Tesla P40 24gb Xilence 800w PSU I installed Ubuntu in UEFI mode. P100 would be logical choice because Nvidia, but it only has 16GBs and that would require like 5 of them to equal my current vram capacity and I don't have enough pcie lanes or space for that. 25/kwh an extra 72 hours (10% addl of 720hrs/mth) of inference costs $2. You could also look into a configuration using multiple AMD GPUs. Jun 3, 2023 · I'm planning to do a lot more work on support for the P40 specifically. Keep in mind cooling it will be a problem. 3GHz, 8 cores / 16 threads each) 128GB DDR3-1600 ECC NVIDIA Tesla P40 24GB Proxmox P40's get you vram. 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. 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. RTX 3090 TI + Tesla P40 Note: One important piece of information. You can limit the power with nvidia-smi pl=xxx. As far as i can tell it would be able to run the biggest open source models currently available. A few details about the P40: you'll have to figure out cooling. The P40 and M40 are probably the cheapest 24GB cards you could buy. I see that the P40 seems to have a slot thing on pictures where the nvlink/sli connector would be. It's important to note this referenced benchmark is from an independent third party - which is a crucial caveat. And one more Is there discussion anywhere specifically on the P40? I can't figure out why I can't get better performance out of mine. Mar 2, 2024 · Hi, I’m going to create an inference/training workstation. Does it make sense to create a workstation with two variants of video cards, or will only P40 or P100 be enough? And why? (I’m going to build a workstation with 4 graphics cards in a year). 07. I heard somewhere that Tesla P100 will be better than Tesla P40 for training, but the situation is the opposite for output. - 3D print this fan housing. Tesla GPU’s do not support Nvidia SLI. So total $725 for 74gb of extra Vram. My goal has been to run a 20b model; so perhaps 3x Tesla P40's would do it, assuming all the other wrinkles can be ironed out. GPU 1: Tesla P40, compute capability 6. It's good for "everything" (including AI). Dell and PNY ones and Nvidia ones. 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. $100. P100 = 2070 sometimes in games (i'd say around + ~10% difference sometimes) Some have run it at reasonably usable speeds using three or four p40 and server hardware for less than two grand worth of parts, but that's a hacked together solution on old and rapidly out dated hardware and not for everyone (support for those older cards is spotty). The x399 supports AMD 4-Way CrossFireX as well. HOWEVER, the P40 is less likely to run out of vram during training because it has more of it. Most models are designed around running on Nvidia video cards. I've been on the fence about toying around with a p40 machine myself since the price point is so nice, but never really knew what the numbers on it looked like since people only ever say things like "I get 5 tokens per second!" Oct 16, 2023 · Hello! Has anyone used GPU p40? I'm interested to know how many tokens it generates per second. C and max. I would like to upgrade it with a GPU to run LLMs locally. Llama3 has been released today, and it seems to be amazingly capable for a 8b model. But you can do a hell of a lot more LLM-wise with a P40. M40 is almost completely obsolete. 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. Dell and PNY ones only have 23GB (23000Mb) but the nvidia ones have the full 24GB (24500Mb). However, it is becoming increasingly available on the secondary market, with PCIe V100 (32GB) models currently selling for around $1,300 , while the 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 currently connected) PCIe is forwards and backwards compatible so you can run PCIe v4 cards on a PCIe v3 motherboard without any issues. I recently bought 2x P40 for LLM inference (I wanted 40+ GB VRAM to run Mixtral) and I should receive them in two weeks. The 22C/44T are still $250 (same as a P40) so not really worth it as it does not give extra options it seems. The server already has 2x E5-2680 v4's, 128gb ecc ddr4 ram, ~28tb of storage. Get the Reddit app Scan this QR code to download the app now NVIDIA RTX 3090 = 936 GB/s NVIDIA P40 = 694 GB/s That puts even the largest LLM models in range. But since 12C/24T Broadwells are like $15, why not. xx) at all. There will definitely still be times though when you wish you had CUDA. I bought an extra 850 power supply unit. Will eventually fit a 4th P40 in there, tested it out and it fits in the bottom-most slot no I just recently got 3 P40's, only 2 are currently hooked up. Since Cinnamon already occupies 1 GB VRAM or more in my case. When using them for fp32 they are about the same. Far cheaper than a second 3090 for the additional 24gb. Question: The recent release of LLM are BIG. it took only 9. Was looking for a cost effective way to train voice models, bought a used Nvidia Tesla P40, and a 3d printed cooler on eBay for around 150$ and crossed my fingers. P40 has more vram, and normal pstates you would expect. gguf. 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). 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. I know you can use openvino for stable diffusion, I haven't heard it translate super well to the LLM world. I've seen people use a Tesla p40 with varying success, but most setups are focused on using them in a standard case. S. 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. Hello, I am just getting into LLM and AI stuff so please go easy on me. the thing is i was running this project ealier with the 4060 but now its failing https I'm building an inexpensive starter computer to start learning ML and came across cheap Tesla M40\P40 24Gb RAM graphics cards. Jul 5, 2022 · Nvidia Tesla P40 Pascal architecture, 24GB GDDR5x memory [3] A common mistake would be to try a Tesla K80 with 24GB of memory. Is there a straightforward tutorial somewhere specifically on the P40? We would like to show you a description here but the site won’t allow us. i swaped them with the 4060ti i had. Windows will have full ROCm soon maybe but already has mlc-llm(Vulkan), onnx, directml, openblas and opencl for LLMs. Also some AMD cards in there like VEGA Frontier and some older ones. That's pretty much it. Question: is it worth taking them now or to take something from this to begin with: 2060 12Gb, 2080 8Gb or 40608Gb? If you've got the budget, RTX 3090 without hesitation, the P40 can't display, it can only be used as a computational card (there's a trick to try it out for gaming, but Windows becomes unstable and it gives me a bsod, I don't recommend it, it ruined my PC), RTX 3090 in prompt processing, is 2 times faster and 3 times faster in token generation (347GB/S vs 900GB/S for rtx 3090). Nvidia 3090(24GB): $900-1k-ish. Especially when it comes to running multiple GPU's at the same time. Although they used the P100 16GB or V100 16GB for the DGX, it made more sense to go for the P40 24GB for my use case since they have more VRAM where it matters more for running LLMs. Mar 24, 2025 · The NVIDIA Tesla V100 stands out as the most likely successor to the P40 in terms of affordability and usability, though it is unlikely to reach the ultra-low pricing that made the P40 so popular. the setup is simple and only modified the eGPU fan to ventilate frontally the passive P40 card, despite this the only conflicts I encounter are related to the P40 nvidia drivers that are funneled by nvidia to use the datacenter 474. observed so far Get the Reddit app Scan this QR code to download the app now Tesla P40's give you a solid 24gb of vram per ~$200; Pascal will be supported for some time longer Sweet. 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). The problem is, I have trouble deciding on GPU to buy came across this post because ive got the exact same problem, as per my research of these gpus, its the fact that they are 24gb, im not sure if the bios for these servers can support mapping 24gb on the bar. For AMD it’s similar same generation model but could be like having 7900xt and 7950xt without issue. but i cant see them in the task manager is this bad i dont know. I achieve around 7-8 t/s with ~6k of context. , "Meta-Llama-3-70B-Instruct-Q8_0. 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. P100 has good FP16, but only 16gb of Vram (but it's HBM2). 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. The pure tflops that both the 3090 and 4090 can deliver are incredible, and if multi card inference is properly implemented and available, on that same consumer hardware ( without any bios unlocks or hacks ), the capabilities that are available to many 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 Get the Reddit app Scan this QR code to download the app now nvidia 8x h200 server for a measly $300K upvotes A open source LLM that includes the pre-training Sorry but nope Tensor in TensorRT-LLM doesn't stand for tensor core. Or would adding a P40 to the 4090 allow me to run a Q8 quant (e. Uses around 10GB VRAM while inferencing. Around 1. 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. It's still great, just not the It looks like you're right-on about the P40 not supporting 16-bit native, and that it runs at FP32 speeds (apparently due to a couple of new instructions that were added in Pascal). 4bpw/EXL2 on my single 4090 desktop system and get about 24 tokens/sec - as of the timeframe now as I post this I would look into those specific type models and make sure your Exllama2 is working as a model loaderinstalling the latest text-generation-webui and choosing Nvidia and then to install the 12. A p40 is around $300 USD give or take right now. llama. But now, when I boot the system and decrypt it, I'm getting greeted with a long waiting time (like 2 minutes or so). At $0. (p100 doesn't) @dross50 those are really bad numbers, check if you have ecc memory enabled; Disable ecc on ram, and you'll likely jump some 30% in performance. 5 in an AUTOMATIC1111 Dec 31, 2024 · The NVidia Tesla P40 is a GPU that is almost ten years old and is actually intended for servers. Both GPUs running PCIe3 x16. The P40 was designed by Nvidia for data centers to provide inference, and is a different beast than the P100. Aug 12, 2024 · The P40 is doing prompt processing twice as fast, which is a big deal with a lot use cases. I like the P40, it wasn't a huge dent in my wallet and it's a newer architecture than the M40. I'm running CodeLlama 13b instruction model in kobold simultaneously with Stable Diffusion 1. 1 and that includes the instructions required to run it. I tried it on an older mainboard first, but on that board I could not get it working. Essentially, it’s a P40 but with only 10GB of VRAM. Both are recognized by nvidia-smi. Smaller codebook sizes allow for faster inference at the cost of slightly worse performance. Cons: Most slots on server are x8. My budget for now is around $200, and it seems like I can get 1x P40 with 24GB of VRAM for around $200 on ebay/from china. The build I made called for 2X P40 GPU's at $175 each, meaning I had a budget of $350 for GPU's. But I have questions I hope experienced people can answer: how plug and play would this be? B. Just make sure you have enough power and a cooling solution you can rig up, and you're golden. 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 Llama. It uses the GP102 GPU chip, and the VRAM is slightly faster. So lots to spare. And the fact that the K80 is too old to do anything I wanted to do with it. Note the P40, which is also Pascal, has really bad FP16 performance, for some reason I don’t understand. Once drivers were sorted, it worked like absolute crap. 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. Since llama. cpp now provides good support for AMD GPUs, it is worth looking not only at NVIDIA, but also on Radeon AMD. Note for the K80, that's 2 GPUs in it, but for SD that doesn't combine well in the software. P6000 is the exact same core architecture as P40 (GP102), so driver installation and compatibility is a breeze. 2x 2tb SSDs Linux Ubuntu TL;DR. 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… Nvidia has some more results on there page here. P. Would the whole "machine" suffice to run models like MythoMax 13b, Deepseek Coder 33b and CodeLlama 34b (all GGUF) A place for everything NVIDIA, come talk about news, drivers, rumors, GPUs, the industry, show-off your build and more. The new NVIDIA Tesla P100, powered by the GP100 GPU, can perform FP16 arithmetic at twice the throughput of FP32. Now, here's the kicker. 1x p40. Very briefly, this means that you can possibly get some speed increases and fit much larger context sizes into VRAM. 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. Therefore it doesn’t have an own active cooling. 62 B llm_load_print_meta: model size = 48. After that the Emergency Mode activates: BAR1: assigned to efifb but device is disabled and NVRM spams my console with: I picked up the P40 instead because of the split GPU design. It does not work with larger models like GPT-J-6B because K80 is not If you want multiple GPU’s, 4x Tesla p40 seems the be the choice. Nvidia Tesla p40 24GB #1374. The Tesla P40 is much faster at GGUF than the P100 at GGUF. 1, VMM: yes. Currently exllama is the only option I have found that does. 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). 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. I do not have a good cooling fan yet, so I did not actually run anything right now. 7 tokens per second resulting in one response taking several minutes. Alternatively you can try something like Nvidia P40, they are usually $200 and have 24Gb VRAM, you can comfortably run up to 34b models there, and some people are even running Mixtral 8x7b on those using GPU and RAM. 00it/s for 512x512. If you choose to forge ahead with those GPUs, expect headaches and little to no community support. I ran all tests in pure shell mode, i. For what it's worth, if you are looking at llama2 70b, you should be looking also at Mixtral-8x7b. 98 Test Prompt: make a list of 100 countries and their currencies in MD table use a column for numbering P40 only has 340GB/s of memory bandwidth, while P100 or MI60 have 3x that, over 1TB/s due to HBM2. 2GB VRAM out of 24GB. g. 238k cuda. Feb 14, 2019 · P40 is about 70% of P100 performance in almost everything. Enough for 33B models. I also have a 3090 in another machine that I think I'll test against. 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. mlc-llm doesn't support multiple cards so that is not an option for me. Get the Reddit app Scan this QR code to download the app now And it seems to indeed be a decent idea for single user LLM inference. They work, I use them. Cost on ebay is about $170 per card, add shipping, add tax, add cooling, add GPU cpu power cable, 16x riser cables. P40 works better than expected for just messing around when paired with a 3060 12gig. But at the moment I don't think there are any based on Pyg. The most important of those are the number of codebooks and codebook size. I'm planning to build a server focused on machine learning, inferencing, and LLM chatbot experiments. 9 is where TensorRT-LLM really shines, offering multiples in performance beyond other architectures. I really appreciate the breakdown of the timings as well. . Why? Because for most use cases any larger a model will simply not be necessary. If you're willing to tinker, I recommend getting the Nvidia Tesla P40, to add on to the 1080Ti. 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. 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 i just also got two of them on a consumer pc. 2023. Nvidia Tesla P40 24 694 P40 has lots of memory (24GiB), 100 has HBM which is important if you're memory bandwidth limited. FYI it's also possible to unblock the full 8GB on the P4 and Overclock it to run at 1500Mhz instead of the stock 800Mhz And the P40 GPU was scoring roughly around the same level of an RX 6700 10GB. Hi everyone, I have decided to upgrade from an HPE DL380 G9 server to a Dell R730XD. You'll have 24 + 11 = 35GB VRAM total. and my outputs always end up spewing out garbage after the second generation with almost We would like to show you a description here but the site won’t allow us. It works nice with up to 30B models (4 bit) with 5-7 tokens/s (depending on context size). 52 GiB (2. nVIDIA locks many of the Tesla drivers behind a licence paywall. Possibly because it supports int8 and that is somehow used on it using its higher CUDA 6. No issues so far. You only really need to run an LLM locally for privacy and everything else you can simply use LLM's in the cloud. Not sure where you get the idea the newer card is slower. Dell 7810 Xeon 2660 v4 192 gigs of ram 1x 3060 12 gig. I typically upgrade the slot 3 to x16 capable, but reduces total slots by 1. 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. 341/23. 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 Hello everyone, i'm planning to buy a pair of nvidia p40 for some HPC projects and ML workloads for my desktop PC (i am aware that p40 is supposed to be used in a server chassis and i'm also aware about the cooling). TL;DR: There's 8 bit precision operations (dp4a/dp2a) that are highly efficient on cards as far back as the P40, and across most every current graphics card as well. 4it/s at 512x768. Inference The P40 is a graphics card with computing power close to that of the 1080, which is not particularly remarkable, but it has 24GB of memory, which is a level that is difficult for most consumer cards on the market to reach. I'd like to turn my small collection of old GPUs (around 12-15 gpus in total) to an in-house LLM crunch base. The Tesla P40 and P100 are both within my prince range. Both are dual slot though. I really want to run the larger models. For 7B models, performance heavily depends on how you do -ts pushing fully into the 3060 gives best I imagine the future of the best local LLM's will be in the 7B-13B range. At least as long as it's about inference, I think this Radeon Instinct Mi50 could be a very interesting option. NVidia have given consumers some absolutely incredible hardware with amazing capabilities. Yeah, it's definitely possible to pass through graphics processing to an iGPU w/ some elbow grease (a search for "nvidia p40 gaming" will bring up videos and discussion), but there still won't be display outputs on the P40 hardware itself! As title says, i'm planning to build a server build for localLLM. I also have one and use it for inferencing. 0x00 前言. Mar 11, 2019 · The P40 has normal power states, so aspm does it. While I’d prefer a P40, they’re currently going for around $300, and I didn’t have the extra cash. ive got a couple of k80s and m40 12gb and those loaded perfectly fine, i even chucked my 3080 in there for the gigs and passing thru to whatever os was fine as long as i had the parameters Which brings to the P40. The p100 is much faster at fp16 workloads (we are talking in excess of 30x faster for fp16). 6 on Pascal. 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. gguf" from Bartowski) and would that be noticeably better than the Q5 quant? (I need an intelligent model—I'm a copywriter and need an LLM to be able to reason through things logically with me as we write professional business content). I'm unclear of how both CPU and GPU could be saturated at the same time. 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. Bits and Bytes however is compiled out of the box to use some instructions that only work for Ampere or newer cards even though they do not need to be. TensorRT does work only on a single GPU, while TensorRT-LLM support multi GPU hardware. The latest SoA models, Replit-code-v1–3b It also has 256GB of RAM and of course multiple Nvidia Tesla GPUs. 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) We would like to show you a description here but the site won’t allow us. Other than using ChatGPT, Stable Diff So I work as a sysadmin and we stopped using Nutanix a couple months back. Jun 3, 2023 · tested chatbot one performance core of CPU (CPU3) is 100% (i9-13900K) other 23 cores are idle P40 is 100%. While doing some research it seems like I need lots of VRAM and the cheapest way would be with Nvidia P40 GPUs. Definitely requires some tinkering but that's part of the fun. TensorRT supports Pascal architecture up to TensorRT 9, but Nvidia recommend to use 8. That's already double the P40's iterations per second. Q4_0. Posted by u/My_Unbiased_Opinion - 6 votes and 6 comments Jul 11, 2023 · 折腾P40显卡本地运行LLM的环境. But 24gb of Vram is cool. 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. Hello, TLDR: Is an RTX A4000 "future proof" for studying, running and training LLM's locally or should I opt for an A5000? Im a Software Engineer and yesterday at work I tried running Picuna on a NVIDIA RTX A4000 with 16GB RAM. Any recommendations would be appreciated. Performance. They're slowly being depreciated due to the fact they can't run the same Cuda code as GPUs like the 3090. The performance of P40 at enforced FP16 is half of FP32 but something seems to happen where 2xFP16 is used because when I load FP16 models they work the same and still use FP16 memory footprint. People often pick up a nvidia P40 for increasing the VRAM amount. LakoMoor opened this issue Oct 16 As they are from an old gen, we can find some quite cheap on ebay, what about a good cpu, 128Gb of ram and 3 of them (24Gb each) ? My target is to run something like mistral 7b with a great throughout (30tk/s or more) or even try mistral 8x7b (quantitized I guess), and serve only a few concurent users users (poc/beta test) Sell them and buy Nvidia. P40 supports Cuda 6. P40 still holding up ok. If inference takes double the time M40 vs P40, and your rig is 10% utilized / 90% idle on P40, it would be 20%/80% on M40 given same tasks. It does fit in a regular case, P40 is much smaller than a RTX 3090, but don’t forget that you need some more space for a cooling. The server came with a 6C/6T E5-2603 v4, which is actually fine since I am running on the P40 mostly. I can do video out through integrated graphics to free up my full VRAM for 40gb which should run 70b Q4 + some context. 6, VMM: yes. ExLlamaV2 is kinda the hot thing for local LLMs and the P40 lacks support here. May 7, 2025 · For local LLM enthusiasts, Pascal cards like the Tesla P40, with its generous 24GB of GDDR5 VRAM, have been a cornerstone for running larger models (e. The GP102 (Tesla P40 and NVIDIA Titan X), GP104 (Tesla P4), and GP106 GPUs all support instructions that can perform integer dot products on 2- and4-element 8-bit vectors, with accumulation into a 32-bit integer. A batch of 2 512x768 images with R-ESRGAN 4x+ upscaling to 1024x1536 took 2:48. I believe that they can not use current versions (12. , ASUS WS C621E SAGE or Supermicro H11DSi) As far as i can tell it would be able to run the biggest open source models currently available. LakoMoor opened this issue Oct 16 You can follow the tutorials for building gaming PC's that are all over. The only compute advantage they might have is FP64, as Nvidia restricts that on consumer GPUs. Price to performance. Not that these results are with power limit set to 50% (125W) and it is thermally limited even below this (80W-90W) as I didn't receive the blower fan yet so I'm just pointing a couple of fans at the GPU. It's a fast GPU (with performance comparable or better than a RTX 4090), using one of Nvidia's latest GPU architectures (Ada lovelace), with Nvidia tensor cores, and it has a lot of 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. I have the henk717 fork of koboldai set up on Ubuntu server with ~60 GiB of RAM and my Nvidia P40. Average it/s for Mixtral models is 20. You can run SDXL on the P40 and expect about 2. 8 x Nvidia P40 24gb ≈ $1400 ($170 each) 2x HPE 460w switching PSU ≈ $50 ($25 each) 2tb Sata ≈ $100 2300w mining PSU ≈ $70 2x HP cooler heatsink ≈ $ 40 6x pcie 8x to 16x risers ≈ $60 ($10 each) various other stuff ≈ $100 ≈ $2000 for the whole build Jul 5, 2014 · Nothing from nvidia on PCIe passed 24GB until Volta which will still cost thousands unless you have a platform that can utilize OAM modules. I expect it to run any LLM that requires 24 GB (although much slower than a 3090). Full machine. However, whenever I try to run with MythoMax 13B it generates extremely slowly, I have seen it go as low as 0. Cost: As low as $70 for P4 vs $150-$180 for P40 Just stumbled upon unlocking the clock speed from a prior comment on Reddit sub (The_Real_Jakartax) Below command unlocks the core clock of the P4 to 1531mhz nvidia-smi -ac 3003,1531 . P100 does not have power states - as its a hack - relies on nvlink to regulate p states tho it doesn't have it to regulate power states on pcie. We had 6 nodes. I was really impressed by its capabilites which were very similar to ChatGPT. This is fantastic information. The latest TensorRT container is still compatible with Pascal GPUs. Kinda sorta. At a rate of 25-30t/s vs 15-20t/s running Q8 GGUF models. 1. Linux has ROCm. 11 Tags: LLM. 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. Would use a riser cable to run it outside my 4060 Ti rig for now with only the 3090 requiring a new PSU, then if want to run bigger I would build a new rig as a local LLM server. And for $200, it's looking pretty tasty. Thermal management should not be an issue as there is 24/7 HVAC and very good air flow. If anyone is contemplating the use of a p40 and they would like me to test something for them let me know. 44 desktop installer, which recognizes the I'm running a handful of P40s. Time: 2023. Each loaded with an nVidia M10 GPU. It's a very attractive card for the obvious reasons if it can be made to perform well. Tesla P40 C. But it is something to consider. name= microsoft llm_load_print_meta: BOS token = 1 '<s>' llm_load_print_meta: EOS token = 2 '</s>' llm_load_print_meta: UNK token = 0 '<unk I had basically the same choice a month ago and went with AMD. i have windows11 and i had nvidia-toolkit v12. 96 BPW) llm_load_print_meta: general. I'm using a Dell R720 with a P40 and it works pretty well. We would like to show you a description here but the site won’t allow us. 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. 4 already installed. 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. 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. Aug 30, 2024 · I wanted to share my experience with the P102-100 10GB VRAM Nvidia mining GPU, which I picked up for just $40. Here's a suggested build for a system with 4 NVIDIA P40 GPUs: Hardware: CPU: Intel Xeon Scalable Processor or AMD EPYC Processor (at least 16 cores) GPU: 4 x NVIDIA Tesla P40 GPUs Motherboard: A motherboard compatible with your selected CPU, supporting at least 4 PCIe x16 slots (e. In nvtop and nvidia-smi the video card jumps from 70w to 150w (max) out of 250w. e. It sounds like a good solution. Llama cpp and exllama work out of the box for multiple GPU's. (Does your motherboard have 3 slots for it?) It can be found for around $250, second hand, online. Jun 9, 2023 · In order to evaluate of the cheap 2nd-hand Nvidia Tesla P40 24G, this is a little experiment to run LLMs for Code on Apple M1, Nvidia T4 16G and P40. (e. Reply reply Top 1% Rank by size OP's tool is really only useful for older nvidia cards like the P40 where when a model is loaded into VRAM, the P40 always stays at "P0", the high power state that consumes 50-70W even when it's not actually in use (as opposed to "P8"/idle state where only 10W of power is used). cpp and koboldcpp recently made changes to add the flash attention and KV quantization abilities to the P40. P4, P10, P40, P100) The T40 is believed to have the same TU102 die as the T10, but running I have bought two used NVIDIA M40 with 24 GB for $100 each. cpp revision 8f1be0d built with cuBLAS, CUDA 12. Nvidia griped because of the difference between datacenter drivers and typical drivers. The M40 is a great deal and a good way to run smaller models, but I can't help but thing you would be better off getting a 3060 12GB that can do other things as well, and sticking to 8B models which have come really far in the past few months. Mar 9, 2024 · GPU 0: NVIDIA GeForce RTX 3060, compute capability 8. Electricity cost is also not an issue. True cost is closer to $225 each. Has 24GB capacity and 700GB/s memory speed. Sure, the 3060 is a very solid GPU for 1080p gaming and will do just fine with smaller (up to 13b) models. 70B will require a second P40. This can be really confusing. If this is going to be a "LLM machine", then the P40 is the only answer. This Subreddit is community run and does not represent NVIDIA in any capacity unless specified. I’ve found that combining a P40 and P100 would result in a reduction in performance to in between what a P40 and P100 does by itself. I'd like to spend less than ~$2k but would be willing to spend more on a better server if it allowed for upgrades in the future. Oct 16, 2023 · Hello! Has anyone used GPU p40? I'm interested to know how many tokens it generates per second. Anything after that will cost even more. BUT there are 2 different P40 midels out there. 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