Tesla p40 fp16 reddit No, it just doesn't support fp16 well, and so code that runs LLMs shouldn't use FP16 on that card. on model "TheBloke/Llama-2-13B-chat-GGUF**" "llama-2-13b-chat. I just bought a 3rd P40 on Friday 🕺allure of 8x22 was too strong to resist I chose second box approach for these, kept the primary rig FP16 friendly and optimize the second for RAM bandwidth (two CPUs to get 2x channels) and many P40 I got a pile of x8 slots Can you please share what motherboard you use with your p40 gpu. Cost on ebay is about $170 per card, add shipping, add tax, add cooling, add GPU cpu power cable, 16x riser cables. Got myself an old Tesla P40 Datacenter-GPU (GP102 like GTX1080-silicon but I graduated from dual M40 to mostly Dual P100 or P40. Help Hi all, A reddit dedicated to the profession of Computer System Administration. Telsa P40 - 24gb Vram, but older and crappy FP16. As a result, inferencing is slow. Hello, I have 2 GPU in my workstation 0: Tesla p40 24GB 1: Quadro k4200 4GB My main GPU is Tesla, every time i run comfyui, it insists to run using Quadro, even through the Nvidia control panel I select to run it with tesla p40. I'm not sure what to do about it, because adding a whole second code path for FP32 would be a lot of Tesla P40 has really bad FP16 performance compared to more modern GPU's: FP16 (half) =183. Having a very hard time finding benchmarks though. Adding to that, it seems the P40 cards have poor FP16 performance and there's also the fact they're "hanging on the edge" when it comes to support since many of the major projects seem to be developed mainly on 30XX cards up. What I suspect happened is it uses more FP16 now because the tokens/s on my Tesla P40 got halved along with the power consumption and memory controller load. offloaded 29/33 layers to GPU Nvidia Announces 75W Tesla T4 for inferencing based on the Turing Architecture 64 Tera-Flops FP16, 130 TOPs INT 8, 260 TOPs INT 4 at GTC Japan 2018 Trouble getting Tesla P40 working in Windows Server 2016. are installed correctly I believe. Open comment sort options. 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 If you can stand the fan noise, ESC4000 G3 servers are running for around $200-$500 on e-bay right now, and can run 4x P40's at full bandwidth (along with a 10gbe nic and hba card or nvme. Server recommendations for 4x tesla p40's . What I suspect happened is it uses more FP16 now because the tokens/s on my Tesla P40 got halved along with the power consumption and memory controller load. Get the Reddit app Scan this QR code to download the app now. I also Purchased a RAIJINTEK Morpheus II Core Black Heatpipe VGA Kühler to cool it. Nvidia drivers are version 510. View community ranking In the Top 10% of largest communities on Reddit. It seems to have gotten easier to manage larger models through Ollama, FastChat, ExUI, EricLLm, exllamav2 supported projects. I updated to the latest commit because ooba said it uses the latest llama. GP102/104) will turn out to be a significant downside for what I wanna do, but I don't know. No video output and should be easy to pass-through. Log In / Sign Up; 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. 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. RTX 3090 TI + RTX 3060 D. Or check it out in the app stores running Ubuntu 22. Only GGUF provides the most performance on Pascal cards in my experience. I have the two 1100W power supplies and the proper power cable (as far as I understand). And the fact that the K80 is too old to do anything I wanted to do with it. Cuda drivers, conda env etc. Since Cinnamon already occupies 1 GB VRAM or more in my case. While I can guess at the performance of the P40 based off 1080 Ti and Titan X(Pp), P40 isn't very well supported (yet). I saw a couple deals on used Nvidia P40's 24gb and was thinking about grabbing one to install in my R730 running proxmox. P40 Pros: 24GB VRAM is more future-proof and there's a chance I'll be able to run language models. Hi there im thinking of buying a Tesla p40 gpu for my homelab. Or check it out in the app stores Nvidia Tesla P40 performs amazingly well for llama. 8. 04 LTS Desktop and which also has an Nvidia Tesla P40 card installed. Exllama loaders do not work due to dependency on FP16 instructions. Discussion This community is for the FPV pilots on Reddit. With Tesla P40 24GB, I've got 22 tokens/sec. 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. P40-motherboard compatibility It's 16 bit that is hobbled (1/64 speed of 32 bit). 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. gguf. Hi all, I got ahold of a used P40 and have it installed in my r720 for machine-learning purposes. Anyone here have any 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 have run fp16 models on my (even older) K80 so it probably "works" as the driver is likely just casting at runtime, but be warned you may run into hard barriers. 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) 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). I installed a Tesla P40 in the server and it works fine with PCI passthrough. 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. Q4_K_M. gguf"** The performance degrade as soon as the GPU overheat up to 6 tokens/sec, and temperature increase up to 95C. e. So I think P6000 will be a right choice. Curious on this as well. I’ve decided to try a 4 GPU capable rig. Will the SpeedyBee F405 V4 stack fit in the iFlight Nazgul Evoque 5 Note the P40, which is also Pascal, has really bad FP16 performance, for some reason I don’t understand. The 16g P100 is a better buy, it has stronger FP16 performance with the added 8g. 58 TFLOPS FP32: 12. Honestly the biggest factor for me right now is probably the fact that the P40's chip was also built into consumer cards which in turn have been tested for all kinds of AI inference tasks - maybe the bad fp16 performance (GP100 vs. And P40 has no merit, comparing with P6000. I have two P100. It is designed for single precision GPU compute tasks as well as to accelerate graphics in virtual remote workstation environments. However, the server fans don't go up when the GPU's temp rises. Anyone try to mix up Tesla P40 24G and Tesla P100 16G for dual card LLM inference? It works slowly with Int4 as vLLM seems to use only the optimized kernels with FP16 instructions that are slow on the P40, but Int8 and above works fine. -3xNvidia Tesla P40 (24gb) - one was actually a P41 but it shows in devices as P40 and I still don't know the difference between a P40 and P41 despite some googling My understanding is that the main quirk of inferencing on P40's is you need to avoid FP16, as it will result in slow-as-crap computations. I've found some ways around it technically, but the 70b model at max context is where things got a bit slower. Still, the only better used option than P40 is the 3090 and it's quite a step up in price. So a 4090 fully loaded doing nothing sits at 12 Watts, and unloaded but idle = 12W. In comparison to Gaming GPUs a lot of resources are spent on FP16/64. Best. completely without x-server/xorg. 8tflops for the P40, 26. Llamacpp runs rather poorly vs P40, no INT8 cores hurts it. RTX 3090: FP16 (half) = 35. For example, in text generation web ui, you simply select the "don't use fp16" option, and you're fine. 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. The P40 for instance, benches just slightly worse than a 2080 TI in fp16 -- 22. Modern cards remove FP16 cores entirely and either upgrade the FP32 cores to allow them to run in 2xFP16 mode or Note: Some models are configured to use fp16 by default, you would need to check if you can force int8 on them - if not just use fp32 (anything is faster than fp16 pipe on p40. Compared to the Pascal Titan X, the P40 has all SMs With the update of the Automatic WebUi to Torch 2. This device cannot start. The P100 a bit slower around 18tflops. cpp the video card is only half loaded (judging by power consumption), but the speed of the 13B Q8 models is quite acceptable. I noticed this metric is missing from your table I'm building an inexpensive starter computer to start learning ML and came across cheap Tesla M40\P40 24Gb RAM graphics cards. So total $725 for 74gb of extra Vram. P6000 has higher memory bandwidth and active cooling (P40 has passive cooling). The P40 is restricted to llama. I bought an extra 850 power supply unit. However the ability to run larger models and the recent developments to GGUF make it worth it IMO. I would probably split it between a couple windows VMs running video encoding and game streaming. 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 24 GB are available. More info: https://rtech. I want to point out most models today train on fp16/bf16. I was aware of the fp16 issue w/ p40 but wasn’t Optimization for Pascal graphics cards (GTX 10XX, Tesla P40) Question Using a Tesla P40 I noticed that when using llama. THough the P40's crusted it With the tesla cards the biggest problem is that they require Above 4G decoding. So in practice it's more like having 12GB if you are locked in at FP16. 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. It sux, cause the P40's 24GB VRAM and price make it Tesla M40 vs P40 speed . ExLlamaV2 is kinda the hot thing for local LLMs and the P40 lacks support here. 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. cpp that improved performance. Modded RTX 2080 Ti with 22GB Vram. These questions have come up on Reddit and elsewhere, but there are a The price of used Tesla P100 and P40 cards have fallen hard recently (~$200-250). support Was looking for a cost effective way to train voice models, bought a used Nvidia Tesla P40, and a 3d printed cooler on eBay for around 150$ and crossed my fingers. New Note: Reddit is dying due to terrible leadership from CEO /u/spez. Or check it out in the app stores on a Tesla P40 with these settings: 4k context runs about 18-20 t/s! With about 7k context it slows to 3-4 t/s. And keep in mind that the P40 needs a 3D printed cooler to function in a consumer PC. Got a couple of P40 24gb in my possession and wanting to set them up to do inferencing for 70b models. Cant choose gpu on comfyui . 76 TFLOPS FP64: 0. I like the P40, it wasn't a huge dent in my wallet and it's a newer architecture than the M40. The Tesla line of cards should definitely get a significant performance boost out of fp16. A full order of magnitude slower! I'd read that older Tesla GPUs are some of the top value picks when it comes to ML applications, but obviously with this level of performance that isn't the case at all. Vega FE FP16/32/64 Performance vs Gaming . I'm running CodeLlama 13b instruction model in kobold simultaneously with Stable Diffusion 1. Autodevices at lower bit depths (Tesla P40 vs 30-series, FP16, int8, and int4) Hola - I have a few questions about older Nvidia Tesla cards. But the P40 sits at 9 Watts unloaded and unfortunately 56W loaded but idle. But a strange thing is that P6000 is cheaper when I buy them from reseller. Then each card P40 has more Vram, but sucks at FP16 operations. ASUS ESC4000 G3. cpp still has a CPU backend, so you need at least a decent CPU or it'll bottleneck. 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. /r/StableDiffusion is back open after the protest of Reddit killing open API access 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. About 1/2 the speed at inference. For what it's worth, if you are looking at llama2 70b, you should be looking also at Mixtral-8x7b. If you dig into the P40 a little more, you'll see its in a pretty different class than anything in the 20- or 30- series. Comments on posts should stay on topic and add to the discussion, and there should be no attempts to threadjack. Anyway, it is difficult to track down information on Tesla P40 FP16 performance, but according to a comment on some forum it does have 2:1 FP16 ratio. 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. So, the GPU is severely throttled down and stays at around 92C with 70W power consumption. This is because Pascal cards have dog crap FP16 performance as we all know. GPU: MSI 4090, Tesla P40 Share Add a Comment. For the vast majority of people, the P40 makes no sense. The journey was marked by experimentation, challenges, and ultimately, a successful DIY transformation. My question is how much would it cost in order to have it working with esxi and So I bought a Tesla P40, for about 200$ (Brand new, good little AI Inference Card). I ran all tests in pure shell mode, i. I'm considering Quadro P6000 and Tesla P40 to use for machine learning. /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 Does anyone have experience with running StableDiffusion and older NVIDIA Tesla GPUs, such as the K-series or M-series? Most of these accelerators have around 3000-5000 CUDA cores and 12-24 GB of VRAM. You can fix this by The Tesla P40 and other Pascal cards (except the P100) are a unique case since they support FP16 but have abysmal performance when used. Sort by: Best. Tomorrow I'll receive the liquid cooling kit and I sould get constant results. 179K subscribers in the LocalLLaMA community. maybe tesla P40 does not support FP16? thks We compared two Professional market GPUs: 24GB VRAM Tesla P40 and 12GB VRAM Tesla M40 to see which GPU has better performance in key specifications, benchmark tests, power Get the Reddit app Scan this QR code to download the app now. Now I’m debating yanking out four P40 from the Dells or four P100s. I've seen several github issues where they don't work until until specific code is added to give support for older 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. ) Tiny PSA about Nvidia Tesla P40 . b. Therefore I have been looking at hardware upgrades and opinions on reddit. The Tesla P40 and other Pascal cards (except the P100) are a unique case since they support FP16 but have abysmal performance when used. 7 GFLOPS , FP32 (float) = 11. 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. I really want to run the larger models. I saw mentioned that a P40 would be a cheap option to get a lot of vram. $100. P100 claims to have better FP16 but it's a 16g card so you need more of them and at $200 doesn't seem competitive. So Tesla P40 cards work out of the box with ooga, but they have to use an older bitsandbyes to maintain compatibility. You 77 votes, 56 comments. llama. If you want WDDM support for DC GPUs like Tesla P40 you need a driver that supports it and this is only the vGPU driver. Techpowerup reports the Tesla P40 as crippled in FP16 as well, We're now read-only indefinitely due to Reddit Incorporated's poor management and decisions related to third party platforms and content View community ranking In the Top 1% of largest communities on Reddit. ExLlama relies heavily on FP16 math, and the P40 just has terrible FP16 performance. xx. 29 TFLOPS But that guide assumes you have a GPU newer than Pascal or running on CPU. Members Online. Same idea as as [r/SuddenlyGay Original Post on github (for Tesla P40): JingShing/How-to-use-tesla-p40: A manual for helping using tesla p40 gpu (github. I am looking at upgrading to either the Tesla P40 or the Tesla P100. I just recently got 3 P40's, only 2 are currently hooked up. I have no experience with the P100, but I read the Cuda compute version on the P40 is a bit newer and it supports a couple of data types that the P100 doesn't, making it a slightly better card at inference. Q5_K_M. 76 TFLOPS. 6-mixtral-8x7b. But 24gb of Vram is cool. B. 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. To date I have various Dell Poweredge R720 and R730 with mostly dual GPU configurations. Around $180 on ebay. So you Get the Reddit app Scan this QR code to download the app now I've an old Thinkstation D30, and while it officially supports the Tesla K20/K40, I'm worried the p40 might cause issues (Above 4G can be set, but Resize Bar missing, though there seem to be firmware hacks and I found claims of other Mainboards without the setting working anyway My P40 is about 1/4 the speed of my 3090 at fine tuning. 58 TFLOPS, FP32 (float) I run the fp16 mode on P40 when used tensor RT and it can not speed up. All posts must be related to Tesla, its business, products, or people. 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. the water blocks are all set up for the power plug out the a. A new feature of the Tesla P40 GPU The P40 and K40 have shitty FP16 support, they generally run at 1/64th speed for FP16. Usually on the lower side. The P40 also has basically no half precision / FP16 support, which negates most benefits of having 24GB VRAM. This sub is for discussions about Tesla Inc. Top. . Some say consumer grade motherboard bios may not support this gpu. 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 The Tesla P40 GPU Accelerator is offered as a 250 W passively cooled board that requires system air flow to properly operate the card within its thermal limits. The 24GB on the P40 isn't really like 24GB on a newer card because the FP16 support runs at about 1/64th the speed of a newer card (even the P100). I get between 2-6 t/s depending on the model. VLLM requires hacking setup. 8tflops for the 2080. I chose Q_4_K_M because I'm hoping to I'm seeking some expert advice on hardware compatibility. 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. Tesla P40 C. Works great with ExLlamaV2. P40s can't use these. While it is technically capable, it runs fp16 at 1/64th speed compared to fp32. On Pascal cards like the Tesla P40 you need to force CUBLAS to use the older MMQ kernel instead of using the tensor kernels. The new NVIDIA Tesla P100, powered by the GP100 GPU, can perform FP16 arithmetic at twice the throughput of FP32. The p40/p100s are poor because they have poor fp32 and fp16 performance compared to any of the newer cards. What CPU you have? Because you will probably be offloading layers to the CPU. ) Autodevices at lower bit depths (Tesla P40 vs 30-series, FP16, int8, and int4) Hola - I have a few questions about older Nvidia Tesla cards. These questions have come up on Reddit and elsewhere, but there are a couple of details that I can't seem to get a firm answer to. I'm considering installing an NVIDIA Tesla P40 GPU in a Dell Precision Tower 3620 workstation. The main thing to know about the P40 is that its FP16 performance suuuucks, even compared to similar boards like the P100 Get app Get the Reddit app Log In Log in to Reddit. Please use our Discord server instead of supporting a company that acts against its users and unpaid moderators. Hey, Tesla P100 and M40 owner here. 0 is 11. 4 iterations per second (~22 minutes per 512x512 image at the same settings). On the previous Maxwell cards any FP16 FP16 will be utter trash, you can see on the NVidia website that the P40 has 1 FP16 core for every 64 FP32 cores. I know I'm a little late but thought I'd add my input since I've done this mod on my Telsa P40. If this 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. I did a quick test with 1 active P40 running dolphin-2. 5 in an AUTOMATIC1111 Tesla P40 24G ===== FP16: 0. py and building from source but also runs well. True cost is closer to $225 each. P100 has good FP16, but only 16gb of Vram (but it's HBM2). No promoting or discussing the bypassing of Tesla safety features. I picked up the P40 instead because of the split GPU design. Note - Prices are localized for my area in Europe. P40 Cons: Apparently due to FP16 weirdness it doesn't perform as well as you'd expect for the applications I'm interested in. You can get these on Taobao for around $350 (plus shipping) A RTX 3090 is around $700 on the local secondhand markets for reference. Yes, you get 16gigs of vram, but that's at the cost of not having a stock cooler (these are built for data centers with constant 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. Sort by: Also P40 has The P40 was designed by Nvidia for data centers to provide inference, and is a different beast than the P100. My PSU only has one EPS connector but the +12V rail is rated for 650W. The 3060 12GB costs about the same but provides much better speed. They are some odd duck cards, 4096 bit wide memory bus and the only Pascal without INT8 and FP16 instead. The Tesla cards will be 5 times slower than that, 20 times slower than the 40 series. Subreddit to discuss about Llama, the large language model created by Meta AI. This is a misconception. videos or gifs of things suddenly or unexpectedly becoming trans. View community ranking In the Top 5% of largest communities on Reddit. When I first tried my P40 I still had an install of Ooga with a newer bitsandbyes. I too was looking at the P40 to replace my old M40, until I looked at the fp16 speeds on the P40. Inference 12 votes, 21 comments. Expand user menu Open settings menu. (Code 10) Insufficient system resources exist to complete the API . (4090 with FP16) I bought an Nvidia Tesla P40 to put in my homelab server and didn't realize it uses EPS rather than PCIe. 183 TFLOPS FP32: 11. What you can do is split the model into two parts. RTX 3090 TI + Tesla P40 Note: One important piece of information. 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 I currently have a Tesla P40 alongside my RTX3070. Note that llama. I have the drivers installed and the card shows up in nvidia-smi and in tensorflow. Neox-20B is a fp16 model, so it wants 40GB of VRAM by default. Except for the P100. 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. Therefore, you need to modify the registry. Or check it out in the app stores NVIDIA Tesla P4 & P40 - New Pascal GPUs Accelerate Inference in the Data Center so it won't have the double-speed FP16 like the P100 but it does have the fast INT8 like the Pascal Titan X. ExLLaMA does fp16 inference for GPTQ so it's unusably Running on the Tesla M40, I get about 0. 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. c. Exllamav2 runs well. Reset laptop BIOS - ASUS G512L(V) 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). 367 TFLOPS another reddit post gave a hint regarding AMD card AMD Instinct MI25 with 4096 stream-processors, and a good performance: AMD Instinct MI25 16G ===== FP16: 24. Mi25 is only $100 but you will have to deal with ROCM and the cards being pretty much as out of support as the P40 or Get the Reddit app Scan this QR code to download the app now. int8 (8bit) should be a lot faster. cpp because of fp16 computations, whereas the 3060 isn't. TLDR: trying to determine if six P4 vs two P40 is better for 2U form factor. cpp is very capable but there are benefits to the Exllama / EXL2 combination. cpp GGUF! Discussion Share Add a Comment. On the previous Maxwell cards any FP16 code would just get executed in the FP32 cores. 4 and the minimum version of CUDA for Torch 2. You can look up all these cards on techpowerup and see theoretical speeds. The Telsa P40 (as well as the M40) have mounting holes of 58mm x 58mm distance. If that's the case, they use like half the ram, and go a ton faster.
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