Tesla p40 exllama reddit. It's so dramatic that running a 3.

Tesla p40 exllama reddit I personally run voice recognition and voice generation on P40. ASUS ESC4000 G3. We just recently purchased two PowerEdge R740's each with a Tesla P40 from Dell. Q4_K_M. My PSU only has one EPS connector but the +12V rail is rated for 650W. As it stands, with a P40, I can't get higher context GGML models to work. I know I'm a little late but thought I'd add my input since I've done this mod on my Telsa P40. I don't currently have a GPU in my server and the CPU's TDP is only 65W so it should be able to handle the 250W that the P40 can pull. Or check it out in the app stores So, it's still a great evaluation speed when we're talking about $175 tesla p40's, but do be mindful that this is a thing. Question about low GPU utilization using 2 x Tesla P40s with Ollama upvotes r/LocalLLaMA. But either way, great to hear. So it will perform like a 1080 Ti but with more VRAM. Or check it out in the app stores Using a Tesla P40 I noticed that when using llama. Controversial. 179K subscribers in the LocalLLaMA community. Still, the only better used option than P40 is the 3090 and it's quite a step up in price. Choose the r720 due to explicit P40 mobo support in the Dell manual plus Running solely on the P40 seems a wee bit slower, but that could also just be because it's not in a full 16x PCIe slot. I'm considering installing an NVIDIA Tesla P40 GPU in a Dell Precision Tower 3620 workstation. All posts must be related to Tesla, its business, products, or people. Trouble getting Tesla P40 working in Windows Server 2016. gguf. I have a rtx 4070 and gtx 1060 (6 gb) working together without problems with exllama. Server recommendations for 4x tesla p40's . What you can do is split the model into two parts. Tesla P40 . When I first tried my P40 I still had an install of Ooga with a newer bitsandbyes. The easiest way I've found to get good performance is to use llama. I would love to run a bigger context size without sacrificing the split mode = 2 performance boost. OobaTextUI is latest version (updated yday / 27jun Get the Reddit app Scan this QR code to download the app now. Who knows. It seems to have gotten easier to manage larger models through Ollama, FastChat, ExUI, EricLLm, exllamav2 supported projects. If someone has the right settings I From the look of it, P40's PCB board layout looks exactly like 1070/1080/Titan X and Titan Xp I'm pretty sure I've heard the pcb of the P40 and titan cards are the same. I have the two 1100W power supplies and the proper power cable (as far as I understand). 0 16x lanes, 4GB decoding, to locally host a 8bit 6B parameter AI chatbot as a personal project. Members Online. So a 4090 fully loaded doing nothing sits at 12 Watts, and unloaded but idle = 12W. Or check it out in the app stores running Ubuntu 22. And GGUF Q4/Q5 makes it quite incoherent. If they could get ExLlama optimized for the P40 and get even 1/3 of the speeds they're getting out of the newer hardware, I'd go the P40 route without a 77 votes, 56 comments. But the P40 sits at 9 Watts unloaded and unfortunately 56W loaded but idle. Training and fine-tuning tasks would be a different story, P40 is too old for some of the fancy features, some toolkits and frameworks don't support it at all, and those that might run on it, will likely run significantly slower on P40 with only f32 math, than on other cards with good f16 performance or lots of tensor cores. I put 12,6 on the gpu-split box and the average tokens/s is 17 with 13b models. Possibly because it supports int8 and that is somehow used on it using its higher CUDA 6. Not sure of the difference other than a couple more cuda cores With the update of the Automatic WebUi to Torch 2. plus he is a regular here on Reddit. I can't find any documentation on how to do this with a Tesla P40. 32G of memory will be limiting. Or check it out in the app stores on a Tesla P40 with these settings: 4k context runs about 18-20 t/s! So Exllama performance is terrible. Subreddit to discuss about Llama, the large language model created by Meta AI. So, the GPU is severely throttled down and stays at around 92C with 70W power consumption. Everything else is on 4090 under Exllama. c. Coolers for Tesla P40 cards Discussion Are there any GTX or Quadro cards with coolers i can transplant onto the Tesla P40 with no or minimal modification, I'm wondering if Maxwell coolers like the 980TI would work if i cut a hole for the power connector. I even think I could run Falcon 180B on this, with one card worth of offload to my 7950x. There might be something like that you can do for loaders that are I installed a Tesla P40 in the server and it works fine with PCI passthrough. offloaded 29/33 layers to GPU Yes! the P40's are faster and draw less power. The K80 is a generation behind that, as I understand it, and is mega at risk of not working, which is why you can find K80's with 24GB VRAM (2x12) for $100 on ebay. Its stupid fast compared to GPTQ and just like GGUF it supports various compression levels, from 2 to 8 bit. I know it's the same "generation" as my 1060, but it has four times the memory and more power in general. Tomorrow I'll receive the liquid cooling kit and I sould get constant results. Exllama doesn't work, but other implementations like AutoGPTQ support this setup just fine. But a strange thing is that P6000 is cheaper when I buy them from reseller. KoboldCPP uses GGML files, it runs on your CPU using RAM -- much slower, but getting enough RAM is much cheaper than getting enough VRAM to hold big models. So I think P6000 will be a right choice. My current setup in the Tower 3620 includes an NVIDIA RTX 2060 Super, and I'm exploring the feasibility of upgrading to a Tesla P40 for more intensive AI and deep learning tasks. 58 TFLOPS, FP32 (float) Tesla P40 is a Pascal architecture card with the full die enabled. 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. Just been using it on a local Stable If you have a spare pcie slot that is at least 8x lanes and your system natively supports Resizable Bar ( ≥ Zen2/Intel 10th gen ) then the most cost effective route would be to get a Tesla p40 on eBay for around $170. I am still running a 10 series GPU on my main workstation, they are still relevant in the gaming world and cheap. The Pascal series (P100, P40, P10 ect) is the GTX 10XX series GPUs. com) Seems you need to make some registry setting changes: After installing the driver, you may notice that Built a rig with the intent of using it for local AI stuff, and I got a Nvidia Tesla P40, 3D printed a fan rig on it, but whenever I run SD, it is doing like 2 seconds per iteration and in the resource manager, I am only using 4 GB of VRAM, when So I bought a Tesla P40, for about 200$ (Brand new, good little AI Inference Card). My hardware specs: Dell R930 (D8KQRD2) 4x Xeon 8890v4 24-core at 2. (Code 10) Insufficient system resources exist to complete the API . 4bpw model at Q6 seems more coherent than 4bpw at Q4. (I tried Transformers, AutoGPTQ, all ExLlama loaders), the As a P40 user it needs to be said Exllama is not going to work, and higher context really slows inferencing to a crawl even with llama. ) The P40 was designed by Nvidia for data centers to provide inference, and is a different beast than the P100. My Tesla p40 came in today and I got right to testing, after some driver conflicts between my 3090 ti and the p40 I got the p40 working with some sketchy cooling. b. 0, it seems that the Tesla K80s that I run Stable Diffusion on in my server are no longer usable since the latest version of CUDA that the K80 supports is 11. support Original Post on github (for Tesla P40): JingShing/How-to-use-tesla-p40: A manual for helping using tesla p40 gpu (github. Total system cost with 2KW PSU, was around £2500. Also I wouldn't recc the mi25 cards to anyone, they don't support newer versions of rocm, so things like exllama wont run on it (at least last I checked). I've just discovered that (exllama's) Q6 cache seems to improve Yi 200K's long context performance over Q4. /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. Performance might improve a little more, but I haven't tested the LLM on it yet. 44 Gps at 190W on Cuckoo29 Reply reply This sub-reddit Get the Reddit app Scan this QR code to download the app now. 0 is 11. P41 was faster than I could read. You would also need a cooling shroud and most likely a pcie 8 pin to cpu (EPS) power connector if your PSU doesn't have an extra. on model "TheBloke/Llama-2-13B-chat-GGUF**" "llama-2-13b-chat. If anybody has something better on P40, please share. Or check it out in the app stores   Currently I am torn between the p40 for the 24gb vram and yes I do have above 4g encoding and rebar support and the 3060 because it is just easy to use. It's a pretty good combination, the P40 can generate 512x512 images in about 5 seconds, the 3080 is about 10x faster, I imagine the 3060 will see a similar improvement in generation. P40s can't use these. 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. My goal is for multiple VM's to share the same Tesla P40. Discussion First off, do these cards work with nicehash? If so, what’s the gap between the two in profit? 23 cents KwH. For example, if I get 120FPS in a game with Tesla P40, then I get something like 70FPS is RTX T10-8. Will the SpeedyBee F405 V4 stack fit in the iFlight Nazgul Evoque 5 Get the Reddit app Scan this QR code to download the app now. I’ve decided to try a 4 GPU capable rig. I would probably split it between a couple windows VMs running video encoding and game streaming. Question | Help Has anybody tried an M40, and if so, what are the speeds, especially compared to the P40? /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. Tesla p40 24GB i use Automatic1111 and ComfyUI and i'm not sure if my performance is the best or something is missing, so here is my results on AUtomatic1111 with these Commanline: -opt-sdp-attention --upcast-sampling --api /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app I'm developing AI assistant for fiction writer. exlla With the tesla cards the biggest problem is that they require Above 4G decoding. I'm considering Quadro P6000 and Tesla P40 to use for machine learning. A few details about the P40: you'll have to figure out cooling. 76 TFLOPS. llama. What models/kinda speed are you getting? I have one on hand as well as a few P4s, can't decide what to do with them. (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 Can I run the Tesla P40 off the Quadro drivers and it should all work together? New to the GPU Computing game, sorry for my noob question (searching didnt help much) Share Add a Comment Trying LLM Locally with Tesla P40 Question | Help 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 Recently I felt an urge for a GPU that allows training of modestly sized and inference of pretty big models while still staying on a reasonable budget. It's so dramatic that running a 3. But in RTX supported games, of course RTX Tesla T10-8 is much better. Possibly slightly slower than a 1080 Ti due to ECC memory. But the Tesla series are not gaming cards, they are compute nodes. I use KoboldCPP with DeepSeek Coder 33B q8 and 8k context on 2x P40 I just set their Compute Mode to compute only using: Note: Reddit is dying due to terrible leadership from CEO /u/spez. Hi all, I got ahold of a used P40 and have it installed in my r720 for machine-learning purposes. There is a flag for gptq/torch called use_cuda_fp16 = False that gives a massive speed boost -- is it possible to do I have been researching a replacement GPU to take the budget value crown for inference. cpp instances, but also to switch them completely independently of each other to the lower performance mode when no task is running on the respective GPU and to the higher performance mode when a task has been started on it. I'll pass :) I have 3090 + 3x P40, and like it quite well. Now I’m debating yanking out four P40 from the Dells or four P100s. I fear I have made a mistake in buying my components as I realise I have bought a Nvidia Tesla P40 and a Ryzen 3 4100 (which doesn't have integrated graphics) and the Tesla does not output display. Tesla P40 (Size reference) Tesla P40 (Original) In my quest to optimize the performance of my Tesla P40 GPU, I ventured into the realm of cooling solutions, transitioning from passive to active cooling. 12 votes, 21 comments. Tesla M40 vs P40 speed . gguf"** The performance degrade as soon as the GPU overheat up to 6 tokens/sec, and temperature increase up to 95C. I can't get Superhot models to work with the additional context because Exllama is not properly supported on p40. The P40 is sluggish with Hires-Fix and Upscaling but it does V interesting post! Have R720+1xP40 currently, but parts for an identical config to yours are in the mail; should end up like this: R720 (2xE-2670,192gb ram) 2x P40 2x P4 1100w psu Am in the proces of setting up a cost-effective P40 setup with a cheap refurb Dell R720 rack server w/ 2x xeon cpus w/ 10 physical cores each, 192gb ram, sata ssd and P40 gpu. I have a old pc that has a 1070ti and a 8700k in it doing not much of anything ATM, I am planning on selling the 1070ti and buying 2 p40 for rendering away slowly on the cheap, I already have a 3090 that also has 24gb but having larger projects rendering on it still takes a long time which i could use on gaming or starting other projects if I could use a spare pc to be a work horse, I The Upgrade: Leveled up to 128GB RAM and two Tesla P40's. No promoting or discussing the bypassing of Tesla safety features. 8. the 1080 water blocks fit 1070, 1080, 1080ti and many other cards, it will defiantly work on a tesla P40 (same pcb) but you would have to use a short block (i have never seen one myself) or you use a full size block and cut off some of the acrylic at the end to make room for the power plug that comes out the back of the card. P40 was reading speed. I did a quick test with 1 active P40 running dolphin-2. Subreddit to discuss about Llama, the A 4060Ti will run 8-13B models much faster than the P40, though both are usable for user interaction. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which Exllama is for GPTQ files, it replaces AutoGPTQ or GPTQ-for-LLaMa and runs on your graphics card using VRAM. 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 With Tesla P40 24GB, I've got 22 tokens/sec. Got a couple of P40 24gb in my possession and wanting to set them up to do inferencing for 70b models. So if I have a model loaded using 3 RTX and 1 P40, but I am not doing anything, all the power states of the RTX cards will revert back to P8 even though VRAM is maxed out. Title. But now I have a Tesla P40. DDA / GPU Passthrough flaky for Tesla P40, but works perfectly for consumer 3060 I've been attempting to create a Windows 11 VM for testing AI tools. xx. I get between 2-6 t/s depending on the model. Mind that it uses an older architecture and not everything might work of require fiddling. 7 GFLOPS , FP32 (float) = 11. Maybe it would be better to buy 2 P100s, it might fit in 24+32 and you'll preserve exllama support. I loaded my model Just wanted to share that I've finally gotten reliable, repeatable "higher context" conversations to work with the P40. are installed correctly I believe. The Tesla M40 and M60 are both based on Maxwell, but the Tesla P40 is based on Pascal. Q5_K_M. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers I saw a couple deals on used Nvidia P40's 24gb and was thinking about grabbing one to install in my R730 running proxmox. Comments on posts should stay on topic and add to the discussion, and there should be no attempts to threadjack. Any third party runs full boar. The P40 offers slightly more VRAM (24gb vs 16gb), but is GDDR5 vs HBM2 in the P100, meaning it has far lower bandwidth, Tesla P40 has really bad FP16 performance compared to more modern GPU's: FP16 (half) =183. Nvidia drivers are version 510. For example if you use an Nvidia card, you'd be able to add a cheap $200 p40 for 24gb of vram right? Then you'd be able to split whatever much you could to your main GPU and the rest to the p40. r/LocalLLaMA. This sub is for discussions about Tesla Inc. P6000 has higher memory bandwidth and active cooling (P40 has passive cooling). I have the drivers installed and the card shows up in nvidia-smi and in tensorflow. With regular exllama you can't change as many generation This means you cannot use GPTQ on P40. I'm not sure about exact example for equivalent but I can tell some FPS examples. Here we discuss the next generation of Internetting in a Tesla M40 vs. 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. Then each card That should mean you have a Dell branded card. In a month when i receive a P40 i´ll try the same for 30b models, trying to use 12,24 with exllama and see if it works. Or check it out in the app stores     TOPICS Decrease cold-start speed on inference (llama. 224GB total, 32 cores, 4 GPUs, water cooled. cpp to work with GPU offloadin Tesla P40 is much much better than RTX Tesla T10-8 in normal performance. View community ranking In the Top 1% of largest communities on Reddit [W][EU] Nvidia Tesla P40 24GB I'm interested in buying a Nvidia Tesla P40 24GB. cpp is very capable but there are benefits to the Exllama / EXL2 combination. This makes running 65b sound feasible. So Tesla P40 cards work out of the box with ooga, but they have to use an older bitsandbyes to maintain compatibility. Got myself an old Tesla P40 Datacenter-GPU (GP102 like GTX1080-silicon but Since a new system isn't in the cards for a bit, I'm contemplating a 24GB Tesla P40 card as a temporary solution. gppm will soon not only be able to manage multiple Tesla P40 GPUs in operation with multiple llama. Or check it out in the app stores     TOPICS what's giving more performance right now a p100 running exllama2/fp16 or p40 running whatever it is it runs? so that means the perf gain is small on exllama for p100's compared to gguf/gptq? 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). RTX 3090: FP16 (half) = 35. Anyone here have any Use exllama_hf as the loader with a 4-bit GPTQ model and change the generation parameters to the "Divine Intellect" preset in oobabooga's text-generation-webui. Its really quite simple, exllama's kernels do all calculations on half floats, Pascal gpus other than GP100 (p100) are very slow in fp16 because only a tiny fraction of the devices shaders can do use the following search parameters to narrow your results: subreddit:subreddit find submissions in "subreddit" author:usernameusername The Tesla P40 and P100 are both within my prince range. This means only very small models can be run on P40. 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. I have a Tesla m40 12GB that I tried to get working over eGPU but it only works on motherboards with Above 4G Decoding as a bios setting. cpp, exllama) Question | Help Tesla P40 users - High context is achievable with GGML models + llama_HF loader I'm seeking some expert advice on hardware compatibility. cpp the video card is only half loaded (judging by power consumption), but the speed of the 13B Q8 models is quite acceptable. Nvidia tesla P40 for ai image generation . 04 LTS Desktop and which also has an Nvidia Tesla P40 card installed. Well, I've been tinkering with a tesla M40 24GB and it does: 2. Usually on the lower side. 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. The 3090 can't access the memory on the P40, and just using the P40 as swap space would be even less efficient than using system memory. I'm seeking some expert advice on hardware compatibility. Does anybody have an idea what I might have missed or need to set up for the fans to adjust based on GPU temperature? Tesla p40 24GB i use Automatic1111 and ComfyUI and i'm not sure if my performance is the best or something is missing, so here is my results on AUtomatic1111 with these Commanline: -opt-sdp-attention --upcast-sampling --api /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app This is a misconception. In the past I've been using GPTQ (Exllama) on my main system with the The Tesla P40 is much faster at GGUF than the P100 at GGUF. a. After playing with both a p40, and a p41, my p41 was noticeably faster. . Please use our Discord server instead of supporting a company that acts against its users and unpaid moderators. Only 4090 and the 64GB of DDR5 6000mhz RAM. Cuda drivers, conda env etc. What CPU you have? Because you will probably be offloading layers to the CPU. However, the server fans don't go up when the GPU's temp rises. Motherboard: Asus Prime x570 Pro Processor: Ryzen 3900x System: Proxmox Virtual Environment Virtual Machine: Running LLMs Server: Ubuntu Software: Oobabooga's text-generation-webui 📊 Performance Metrics by Model Size: 13B GGUF Model: Tokens per Second: Around 20 Hi all I am fairly new to the PC building stage and I thought I'd try my hand with making my own system to run a local LLM. cpp with all the layers offloaded to the P40, which does all of its Tiny PSA about Nvidia Tesla P40 . Q&A. As a result, inferencing is slow. Curious to see how these old GPUs are fairing in today's world. 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). I'm trying to install the Tesla P40 drivers on the host so then the VM's can see the video hardware and get it assigned. Or LoneStriker for exl2 quants for exllama v2. More info: https://rtech. The Telsa P40 (as well as the M40) have mounting holes of 58mm x 58mm distance. I also Purchased a RAIJINTEK Morpheus II Core Black Heatpipe VGA Kühler to cool it. Help Hi all, A reddit dedicated to the profession of Computer System Administration. 20ghz 512GB DDR4 ECC Welcome to the IPv6 community on Reddit. 6-mixtral-8x7b. Discussion This community is for the FPV pilots on Reddit. As openai API gets pretty expensive with all the inference tricks needed, I'm looking for a good local alternative for most of inference, saving gpt4 just for polishing final results. This subreddit has gone Restricted and reference-only as part of a mass protest against Reddit's recent API changes, which break third-party apps and moderation tools. 4 and the minimum version of CUDA for Torch 2. 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. I plan to use it for AI Training/Modeling (I'm completely new when it comes to AI and Machine Learning), and I want to play around with things. I am looking at upgrading to either the Tesla P40 or the Tesla P100. However it's likely more stable/consistent especially at higher I use a P40 and 3080, I have used the P40 for training and generation, my 3080 can't train (low VRAM). I think some "out of the box" 4k models would work but I I graduated from dual M40 to mostly Dual P100 or P40. 1. Edit: Tesla M40*** not a P40, my bad. I don't want ANYONE to buy a P40 for over 180$ (They are So, P40s have already been discussed, and despite the nice 24GB chunk of VRAM, unfortunately aren't viable with ExLlama on account of the abysmal FP16 performance. I've struggled to see I still oom around 38000 ctx on qwen2 72B when I dedicate 1 p40 to the cache with split mode 2 and tensor splitting the layers to 2 other p40's. Or check it out in the app stores   2x Used Tesla P40 GPUs 3&4: 2x Used Tesla P100 Motherboard: Used Gigabyte C246M-WU4 P100s can use exllama and other FP16 things. Exllama loaders do not work due to dependency on FP16 instructions. 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. I’ve found that I'm seeing 20+ tok/s on a 13B model with gptq-for-llama/autogptq and 3-4 toks/s with exllama on my P40. Question - Help Hello is the p40 gpu decent for ai image geneation its has 24gb vram is about 250$ used on alliexpress If you'd used exllama with workstation GPUs, older workstation GPUs (P100, P40) colab, AMD could you share results? (I have a P40) and exllama only does fp16. And P40 has no merit, comparing with P6000. Exllama heavily uses fp16 calculations and AutoGPTQ is unbearably slow compared to Get the Reddit app Scan this QR code to download the app now. 44 desktop installer, which There is the P40 with all it's quirks but also Instinct accelerators from AMD: or similar. The journey was marked by experimentation, challenges, and ultimately, a successful DIY transformation. This device cannot start. Get the Reddit app Scan this QR code to download the app now. I wonder what speeds someone would get with something like a 3090 + p40 setup. I bought an Nvidia Tesla P40 to put in my homelab server and didn't realize it uses EPS rather than PCIe. For example exllama - currently the fastest library for 4bit inference - does not work on P40 because it does not have support for required operations or smth. the water blocks are all set up for the power plug out the Exllama 1 and 2 as far as I've seen don't have anything like that because they are much more heavily optimized for new hardware so you'll have to avoid using them for loading models. At a rate of 25-30t/s vs 15-20t/s running Q8 GGUF models. Dresome_sx • • Edited . Prerequisites I am running the latest code, checked for similar issues and discussions using the keywords P40, pascal and NVCCFLAGS Expected Behavior After compiling with make LLAMA_CUBLAS=1, I expect llama. Old. As the title states I currently don't have a P40. 1MH at 81W on ETH 3. cpp HF. nvet cbdbjo pnt vgrgo bqnb hrgk lasibfmu tvbi jrmvbb jwxpsb