Nvidia p40 llm reddit With studio driver, llms should work right away to and both cards should be detectable by cuda apps. Unfortunately, I did lose some inference speed as I can only run GGUF models instead of exl2 models, however I can now run larger models. I'm using a Dell R720 with a P40 and it works pretty well. Price to performance. cpp and koboldcpp recently made changes to add the flash attention and KV quantization abilities to the P40. I recently bought 2x P40 for LLM The 3090 is about 1. Adding a P40 to my system? Same as everybody else, I'm running Ideally, I'd like to run 70b models at good speeds. I tried it on an older mainboard first, but on that board I could not get it working. Use it. i have windows11 and i had nvidia-toolkit v12. Log In / Sign Up; Inference using 3x nvidia P40? Resources As they are from an old gen, I scored the top Open LLM Leaderboard models with my own benchmark Ask other people too what they think before buying, I just think putting there p40 is less "Frankensteiny" and overall better choice than using old 5gb quadro which won't give much difference. Possibly because it supports int8 and that is somehow used on it using its higher CUDA 6. What is your budget (ballpark is okay)? Hi everyone, I have decided to upgrade from an HPE DL380 G9 server to a Dell R730XD. 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. Now, here's the kicker. What is that That should help with just about any type of display out setup. I'm probably going to order a Nvidia Tesla P40 soon actually. i swaped them with the 4060ti i had. Training is one area where P40 really don't shine. Therefore, you need to modify the registry. 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 As far as i can tell it would be able to run the biggest open source models currently available. So I don't know why you never hear about that but be careful when buying a P40. 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. 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. 6B to 120B (StableLM, DiscoLM German 7B, Mixtral 2x7B, Beyonder, Laserxtral, MegaDolphin) upvotes · comments r/LocalLLaMA Ok guys. I also don't know at what price you can buy them around your location but I Not sure if it was this thread or another one (I've been reading way too much on this) but someone said that the half-precision on P40s runs 64x slower than a Nvidia 3xxx or 4xxx. 24go of vram and can output 10-15 • Do you know if the same applies for text2img? I'm playing with the idea of hosting both a text2img model and an llm and I'm trying to figure out what the ideal Get app Get the Reddit app Log In Log in to Reddit. Isn't that almost a five-fold advantage in favour of 4090, at the 4 or 8 bit precisions typical with local LLMs? Hello, I am just getting into LLM and AI stuff so please go easy on me. Get the Reddit app Scan this QR code to download the app now. So IMO you buy either 2xP40 or 2x3090 and call it a day. 3GHz, 8 Intel, AMD and NVIDIA are all going to be releasing chipsets with capabilities aiming to Apples M series which used CPU/RAM in manner that is ultra efficient for LLM. It does not work with larger models like GPT-J-6B because K80 is not The logical next step up from the P40/P100 is the V100 but the 32GB version of that is way overpriced still. completely without x-server/xorg. What would you guys recommand ? I'd like a somewhat quiet solution, and that doesn't require super advanced skill to pull off. Both are recognized by nvidia-smi. Dell and PNY ones and Nvidia ones. The sweet spot for bargain-basement AI cards is the P40. S. Each loaded with an nVidia M10 GPU. 3 DDA GPU driver package for Microsoft platforms Production Branch/Studio Most users select this choice for optimal stability and performance. But with Nvidia you will want to use the Studio driver that has support for both your Nvidia cards P40/display out. Sure, the 3060 is a very solid GPU for 1080p gaming and will do just fine with smaller (up to 13b) models. Hello! But yeah the RTX 8000 actually seems reasonable for the VRAM. 2 nVidia P40s at 24GB each. 9 minutes. 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. Note the P40, which is also Pascal, has really bad FP16 performance, for some reason I don’t understand. The only thing it lacks is tensor cores which are supposed to give some kind of a speed up. 7T), I have bought two used NVIDIA M40 with 24 GB for $100 each. People seem to consider them both as about equal for the price / performance. Why? Because for most use cases any larger a model will simply not be necessary. Everything else is on 4090 under Exllama. If this is going to be a "LLM machine", then the P40 is the only answer. Log In / Sign Up; Just buy a Nvidia P40. I can't figure out how much of a difference it makes. Skip to content. xx. RTX 3090 TI + RTX 3060 D. nvidia-smi -pm ENABLED. If true, this basically means that half-precision is unusable on the P40. P40 is very compatible with 1080 ti. There are ways of making them useful, but they're rather difficult and nowhere near as efficient as nvidia cards. Tesla P40 C. After I connected the video card and decided to test it on LLM via Koboldcpp I noticed that the generation speed from ~20 tokens/s dropped to ~10 tokens/s. Or check it out in the app stores &nbsp; &nbsp; TOPICS Yet another state of the art in LLM quantization . But it is something to consider. Was looking for a cost effective way to train voice models, bought a used Nvidia Tesla P40, and a 3d printed cooler on eBay for around 150$ and crossed my fingers. Nvidia drivers are version 510. 12x 70B, NVidia H200 achieves nearly 12,000 tokens/sec on Llama2-13B with TensorRT-LLM If you want multiple GPU’s, 4x Tesla p40 seems the be the choice. Electricity cost is also not an issue. Looks like this: X-axis power (watts), y-axis it/s. You can limit the power with nvidia-smi pl=xxx. Thermal management should not be an issue as there is 24/7 HVAC and very good air flow. Nvidia Tesla P40 24 694 250 200 Nvidia 2 x RTX 4090 This means you cannot use GPTQ on P40. It'll automatically adjust the power state based on if the GPUs are idle or not. You should see info about both cards. However, whenever I try to run with MythoMax 13B it generates extremely slowly, I have seen it go as low as 0. 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! Among many new records and milestones, one in generative AI stands out: NVIDIA Eos — an AI supercomputer powered by a whopping 10,752 NVIDIA H100 Tensor Core GPUs and NVIDIA Quantum-2 InfiniBand networking — completed a training benchmark based on a GPT-3 model with 175 billion parameters trained on one billion tokens in just 3. TLDR: At +- 140 watts you get 15% less performance, for saving 45% power (Compared to 250W default mode); #Enable persistence mode. I bench marked the Q4 and Q8 quants on my local rig (3xP40, 1x3090). Actually, I have a P40, a 6700XT, and a Get app Get the Reddit app Log In Log in to Reddit. So, the fun part of these mi 25s is that they support 16 bit operations. and my outputs always end up spewing out garbage after the second Resize BAR was implemented with Ampere and later NVidia did make some vbios for Turing cards. B. The Tesla P40 and P100 are both within my prince range. Preferably on 7B models. Old. I've used the M40, the P100, and a newer rtx a4000 for training. 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. While doing some research it seems like I need lots of VRAM and the cheapest way would be with Nvidia P40 GPUs. I built a small local llm server with 2 rtx 3060 12gb. However, I saw many people talking about their speed (tokens / sec) on their high end gpu's for example the 4090 or 3090 ti. 24GB of GDDR5 and enough tensor cores to actually do something with it for If you want the best performance for your LLM then stay away from using Mac and rather build a PC with Nvidia cards. Some observations: the 3090 is a beast! 28 I have a few numbers here for various RTX 3090 TI, RTX 3060 and Tesla P40 setups that might be of interest to some of you. ExLlamaV2 is kinda the hot thing for local LLMs and the P40 lacks support here. Best. On the first 3060 12gb I'm running a 7b 4bit model (TheBloke's Vicuna 1. I have the henk717 fork of koboldai set up on Ubuntu server with ~60 GiB of RAM and my Nvidia P40. Be the Father's day gift idea for the man that has everything: nvidia 8x h200 server for a measly $300K upvotes A open source LLM that includes the pre-training data (4. Nvidia Tesla p40 24GB #1374. Reply reply More replies 3x Nvidia P40 on eBay: $450 Cooling solution for the P40s: $30 (you'll need to buy a fan+shroud kit for cooling, or just buy the fans and 3D print the shrouds) Power cables for the P40s: $50 Open air PC case/bitcoin mining frame: $40 Cheap 1000W PSU: $60 My unraid server is pretty hefty CPU and ram wise, and i've been playing with ollama docker. Works great with ExLlamaV2. In nvtop and nvidia-smi the video card jumps from 70w to 150w (max) out of 250w. But it should be lightyears ahead of the P40. 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. But you can do a hell of a lot more LLM-wise with a P40. Just make sure you have enough power and a cooling solution you can rig up, and you're golden. Alternatively 4x gtx 1080 ti could be an interesting option due to your motherboards ability to use 4-way SLI. If your application supports spreading load over multiple cards, then running a few 100’s in parallel could be an option (at least, Keep in mind cooling it will be a problem. Currently exllama is the only option I have found that does. The GP102 (Tesla P40 and NVIDIA Titan X), GP104 (Tesla P4), and GP106 GPUs all support instructions that can mlc-llm doesn't support multiple cards so that is not an option for me. You only really need to run an LLM locally for privacy and everything else you can simply use LLM's in the cloud. At a rate of 25-30t/s vs 15-20t/s running Q8 GGUF models. 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. P. Or check it out in the app stores And it seems to indeed be a decent idea for single user LLM inference. 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). Flame my choices, recommend me a different way, and any ideas on benchmarking 2x P40 vs 2x P100 As long as your cards are connected with at least PCIe v3 x8 then you are fine for LLM usage (nvidia-smi will tell you how the cards are 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. In these tests, I Check out the recently released \`nvidia-pstated\` daemon. e. 04 LTS Desktop and which also has an Nvidia Tesla P40 card installed. Or literally no other backend besides possibly HF transformers can mix nvidia compute levels and still pull good speeds, Okay try going here on the machine with the P40 and running an llm on the newest Google Chrome on Linux or Windows. You could also look into a configuration using multiple AMD GPUs. Definitely requires some tinkering but that's part of the fun. Here is one game I've played on the P40 and plays quite nicely DooM Eternal is 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 I'm diving into local LLM for the first time having been using fine-tuning, etc. You can look up all these cards on techpowerup and see theoretical speeds. Original Post on github (for Tesla P40): JingShing/How-to-use-tesla-p40: A manual for helping using tesla p40 gpu (github. Llama. BUT there are 2 different P40 midels out there. I would probably split it between a couple windows VMs running video encoding and game streaming. jcjohnss • Looks like the P40 is basically the same as the Pascal Titan X; both are based on the Nvidia Tesla P40 24GB Nvidia RTX 3060 6GB 10 gig rj45 nic 10 gig sfp+ nic USB 3. I bought some of them, but "none work", which leads me to beleive I am doing something wrong. 79 tokens/s, 94 tokens, context 1701, seed 1350402937) Output Has anyone used GPU p40? I'm interested to know how many tokens it generates per second. LakoMoor opened this issue Oct 16, 2023 · 3 comments Comments. The difference is the VRAM. 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. Top. Here, the advantage of using the 1080ti is already evident. I have a question re inference speeds on a headless Dell R720 (2x Xeon CPUs / 20 physical cores, 192 Gb DDR-3 RAM) running Ubuntu 22. Everyone, i saw a lot of comparisons and discussions on P40 and P100. This means only very small models can be run on P40. Controversial. HOWEVER, the P40 is less likely to run out of vram during training because it has more of it. Bits and Bytes however is compiled out of the box to use some instructions that only work for Ampere or The Tesla P40 is much faster at GGUF than the P100 at GGUF. 5-32B today. Heck there's even word that OpenAI has interest in manufacturing their own tech for AI applications. Got a couple of P40 24gb in my possession and wanting to set them Dual Tesla P40 local LLM Rig i just also got two of them on a consumer pc. Tesla GPU’s do not support Nvidia SLI. 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. MI25s are enticingly cheap, but they're also AMD, which is the red headed stepchild of AI right now. Yes, I know P40 are not great, this is for personal use, I can wait. 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. 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. 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. 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. There were concerns about potential compatibility issues, but some users mentioned that Nvidia uses dual Epyc Rome CPUs in their DGX A100 AI server, which could be seen as an endorsement of the compatibility of these . When using them for fp32 they are about the same. Install studio drivers and run "nvidia-smi" in console. A P40 will run at 1/64th the speed of a card that has real FP16 cores. The problem is, I have I am thinking of buying Tesla P40 since it's cheapest 24gb vram solution with more or less modern chip for mixtral-8x7b, what speed will I get and Skip to main content Open menu Open navigation Go to Reddit Home There is a discussion on Reddit about someone planning to use Epyc Rome processors with Nvidia GPUs, particularly with PyTorch and Tensorflow. Be the first to comment A place for everything NVIDIA, come talk about news, drivers, rumors, GPUs Hi folks, I’m planing to fine tune OPT-175B on 5000$ budget, dedicated for GPU. Kinda sorta. I made a mega-crude pareto curve for the nvidia p40, with ComfyUI (SDXL), also Llama. As I've been looking into it, I've come across some articles about Nvidia locking drivers behind vGPU licensing. Nvidia Tesla P40 Pascal architecture, 24GB GDDR5x memory [3] A common mistake would be to try a Tesla K80 with 24GB of memory. Probably better to just get either two P100s or two 3060s if you're not going for a 3090. I’ve found that Super excited for the release of qwen-2. 4 already installed. But for the price of 1x 3090, one could get 2 or 3 P40 for inference plus 2 or 3 P100 for training, and swap around as needed. New. 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 I heard somewhere that Tesla P100 will be better than Tesla P40 for training, I’ve seen people run LLM on P40, but because of the CUDA situation i don’t understand how it works at all( Share Add a Comment. very detailed pros and cons, but I would like to ask, anyone try to mix up one P40 for vRAM size and one P100 for HBM2 bandwidth for a dual card ingerence system? What could be the results? 1+1>2 or 1+1<2? :D Thanks in advance. I've also heard about putting a nvidia titan cooler on the P40, and also using water-cooling. I also have one and use it for inferencing. I also have a 3090 in another machine that I think I'll test against. And for $200, it's looking pretty tasty. but i cant see them in the task manager The P40 was designed by Nvidia for data centers to provide inference, and is a different beast than the P100. I ran all tests in pure shell mode, i. Because the P40 and 1090 use equal chips and architecture, the drivers can be interchanged. Get app Get the Reddit app Log In Log in to Reddit. Performance. Navigation Menu Toggle navigation. Resources Is Nvidia p40 supported by this quants? So I work as a sysadmin and we stopped using Nutanix a couple months back. 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). A 4060Ti will run 8-13B models much faster than the P40, though both are usable for user interaction. And the P40 GPU was scoring roughly around the same level of an RX 6700 10GB. Originally I was running duel 3060 12 gigs but I’m a child and wanted more vram so I changed my set up to run 1 3060 and a p40. Running a local LLM linux server 14b or 30b with 6k to 8k context using one or two Nvidia P40s. It is Turing (basically a 2080 TI), so its not going to be as optimized/turnkey as anything Ampere (like the a6000). 5x as fast as a P40. This can be really confusing. Also the P40 is connected via a real extender, not one of those mining 1x extenders. Are you asking what is literally being done to process 16K tokens into an LLM model? I had similar issue with k20 and a 2080 and the folks at Nvidia explains it like this. I do not have a good cooling fan yet, so I did not actually run anything right now. I personally run voice recognition and voice generation on P40. It sounds like a good solution. I saw a couple deals on used Nvidia P40's 24gb and was thinking about grabbing one to install in my R730 running proxmox. If anybody has something better on P40, please share. But be aware Nvidia crippled the fp16 performance on the p40. The x399 supports AMD 4-Way CrossFireX as well. RTX 3090 TI + Tesla P40 Note: One important piece of information. What's the performance of the P40 using mlc-llm + CUDA? mlc-llm is the fastest inference engine, since it compiles the LLM taking advantage of hardware specific optimizations. Would a buying a p40 make bigger models run noticbly faster? If it does is there anything I should know about buying p40's? Like do they take normal connectors or anything like 🐺🐦‍⬛ LLM Comparison/Test: 2x 34B Yi (Dolphin, Nous Capybara) vs. 4 channels isn't going to not work, it is just going to be on the slow side, especially with larger input context. I expect it to run any LLM that requires 24 GB (although much slower than a 3090). I personally use 2 x 3090 but 40 series cards are very good too. Funny Share Add a Comment. NVIDIA Tesla P4 & P40 - New Pascal GPUs Accelerate Inference in the Data Center Sort by: Best. P40 = Pascal(physically, the board is a 1080 TI/ Titan X pascal with different/fully populated memory pads, no display outs, and the power socket moved) Yes, a Welcome to the official subreddit of the PC Master Race / PCMR! All PC-related content is welcome, including build help, tech support, and any doubt one might have about PC ownership. Would start with one P40 but would like the option to add another later. That means you get double the usage out of their VR and then you will with any of the Nvidia cards pre v100/P100 (NOT P40) So that 16 gig card is a 32 gig card if you can run 16 I enabled everything like "Above 4G Decoding" that I could find references in random posts. "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. 1 4bit) and on the second 3060 12gb I'm running Stable Diffusion. One other random thing: I've been thinking about buying one of these from ERYING. P100 has good FP16, but only 16gb of Vram (but it's HBM2). For my I saw that the Nvidia P40 arent that bad in price with a good VRAM 24GB and wondering if i could use 1 or 2 to run LLAMA 2 and increase inference times? I would like to upgrade it with a GPU to run LLMs locally. Expand user menu Open settings menu. 1. I'm planning to build a server focused on machine learning, inferencing, and LLM chatbot experiments. Open comment sort options. I know 4090 doesn't have any more vram over 3090, but in terms of tensor compute according to the specs 3090 has 142 tflops at fp16 while 4090 has 660 tflops at fp8. #Set power limit to 140Watts. Which brings to the P40. So, as you probably all know, geforce now's server machines use a Tesla P40, a very powerful card that sadly is not optimazed for gaming, in the best case games use around 50% of its power, leaving us with quite low framerates compared to even a gtx 1060. We had 6 nodes. I did it I finally pulled the trigger and got myself a p40. sudo nvidia-smi -pl 140 This maybe a bit outside of llama, but I am trying to setup a 4x NVIDIA P40 rig to get better results than the CPU alone. I've only used Nvidia cards as a passthrough so I can't help much with other types. The build I made called for 2X P40 GPU's at $175 each, meaning I had a budget of $350 for GPU's. This P40 has P40 supports Cuda 6. The NVIDIA RTX Enterprise Production Branch driver is a rebrand of the Quadro Optimal Driver for Enterprise (ODE). It seems like a boatload of the resources a P40 (two even) could use. they are registered in the device manager. Far cheaper than a second 3090 for the Getting real tired of these NVIDIA drivers. They work, I use them. 🐺🐦‍⬛ LLM Comparison/Test: 6 new models from 1. Hey Reddit! I'm debating whether to build a rig for large language model (LLM) work. Posted this before, but here are some benchmarks: System specs: Dell R720xd 2x Intel Xeon E5-2667v2 (3. I wonder how a p40 compares to my rtx 2070 (8 GB vram less cuda cores, but has tensor cores) also worth $200. That is a fair point. There was an Nvidia engineer in here the other day going through the math behind it. Cuda drivers, conda env etc. Q&A. Its really insane that the most viable hardware we have for LLMs is ancient Nvidia GPUs. It's slow, like 1 token a second, but i'm pretty happy writing something and then just checking the window in 20 minutes to see the response. are installed correctly I believe. Llama3 has been released today, and it seems to be amazingly capable for a 8b model. Mac can run LLM's but you'll never get good speeds compared to Nvidia as almost all of the AI tools are build upon CUDA and it will always run best on these. My budget for Hello! Has anyone used GPU p40? I'm interested to know how many tokens it generates per second. A p40 is around $300 USD give or take right now. here is P40 vs 3090 in a 30b int4 P40 Output generated in 33. Finally joined P40 Gang. But 24gb of Vram is cool. It works nice with up to 30B models (4 bit) with 5-7 tokens/s (depending on context size). 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. Which is not ideal setup, but in current distorted market it can still be a viable low-end option. 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. That's already double the P40's iterations per second. 7 tokens per second resulting in one response taking several minutes. The p100 is much faster at fp16 workloads (we are talking in excess of 30x faster for fp16). Since Cinnamon already occupies 1 GB VRAM or more in my case. You may need to install Nvidia drivers. P40 has more Vram, but sucks at FP16 operations. P40 will be in conpute mode, invisible in windows. The new NVIDIA Tesla P100, powered by the GP100 GPU, can perform FP16 arithmetic at twice the throughput of FP32. 1 and that includes the instructions required to run it. Hey, Tesla P100 and M40 owner here. I was really impressed by its capabilites which were very similar to ChatGPT. Have you thought about running it on used P40 or a CPU? Reply reply LLM360 has released K2 65b, a fully reproducible open source LLM matching Llama 2 70b (New reddit? Click 3 dots at end of this message) Privated to protest Reddit's upcoming API changes. Log In / Sign Up; Writing this because although I'm running 3x Tesla P40, nvidia-smi -ac 3003,1531 unlocks the core clock of the P4 to 1531mhz I imagine the future of the best local LLM's will be in the 7B-13B range. 0 PCIe x1 card Software setup: Windows Server 2022 Datacenter Hyper-V installed as Windows Feature Nvidia Complete vGPU 16. It offers the same ISV certification, long life-cycle support, regular security updates, and access to the same functionality as prior Quadro ODE drivers and corresponding View community ranking In the Top 5% of largest communities on Reddit. A few details about the P40: you'll have to figure out cooling. I too was looking at the P40 to replace my old M40, until I looked at the fp16 speeds on the P40. Copy link LakoMoor commented Oct 16, 2023. 72 seconds (2. While it is technically capable, it runs fp16 at 1/64th speed compared to fp32. For what it's worth, if you are looking at llama2 70b, you should be looking also at Mixtral-8x7b. igdit wdpoidvr kdp osmnm migmdq qrv odaqk srmz hcenbhn thew