Llm on amd gpu reddit gaming.

Llm on amd gpu reddit gaming 2x AMD Epyc 9124, 16C/32T, 3. 7800x3d has integrated GPU All 7000 series CPU have RDNA2 based igpus, compared to previous gen when there were only in the G variants. Start chatting! We would like to show you a description here but the site won’t allow us. Threadripper CPUs are OP for modern multithreaded games, but Xeons are still better and cheaper for datacenter workloads when you factor in energy The Radeon Subreddit - The best place for discussion about Radeon and AMD products. Metal was released in 2014 long before Apple went to ARM. I think they're doing this because running it on GPU drains battery very quickly. i managed to push it to 5 tok/s by allowing15 logical cores. , Stable Diffusion), on AMD hardware? I too have several AMD RX 580 8 Gig cards (17, I think) that I would like to do machine learning with. If you have an AMD Ryzen AI PCyou can start chatting! a. Look what inference tools support AMD flagship cards now and the benchmarks and you'll be able to judge what you give up until the SW improves to take better advantage of AMD GPU / multiples of them. 119 votes, 33 comments. QLoRA is an even more efficient way of fine-tuning which truly democratizes access to fine-tuning (no longer requiring expensive GPU power) It's so efficient that researchers were able to fine-tune a 33B parameter model on a 24GB consumer GPU (RTX 3090, etc. but with 7B models you can load that up in either of the exe and run the models locally. On a totally subjective speed scale of 1 to 10: 10 AWQ on GPU 9. Mar 22, 2024 · On the right hand side are all the settings – the key one to check is that LM Studio detected your GPU as “AMD ROCm”. Welcome to /r/AMD — the subreddit for all things AMD; come talk about Ryzen, Radeon… You can run Mistral 7B (or any variant) Q4_K_M with about 75% of layers offloaded to GPU, or you can run Q3_K_S with all layers offloaded to GPU. The AI ecosystem for AMD is simply undercooked, and will not be ready for consumers for a couple of years. However, the dependency between layers means that you can't simply put half the model in one GPU and the other half in the other GPU, so if, say, Llama-7b fits in a single GPU with 40GB VRAM and uses up 38 gigs, it might not necessarily fit into two GPUs with 20GB VRAM each under a model parallelism approach. . Mar 6, 2024 · 6. The larger mod It’s probably the easiest way to get 32GB+ RAM on a GPU. asrock b550 phantom gaming nicely fit 2x 3090 i upgraded now to supermicro with epyc and no difference in gpu inference 1. Are there significant limitations or performance issues when running CUDA-optimized projects, like text-to-image models (e. It can be turned into a 16GB VRAM GPU under Linux and works similar to AMD discrete GPU such as 5700XT, 6700XT, . Use llama. Apparently there are some issues with multi-gpu AMD setups that don't run all on matching, direct, GPU<->CPU PCIe slots - source. GPT4ALL is very easy to setup. I've got a supermicro server- i keep waffling between grabbing a gpu for that (Need to look up power board it's using), so i can run something on it it rather then my desktop, or put a second GPU in my desktop and dedicate one to LLM, another to regular usage, or just droping 128gb of ram into my desktop and seeing if that makes the system We would like to show you a description here but the site won’t allow us. I have gone through the posts recommending renting cloud GPU and started with that approach. 29 votes, 81 comments. My goal is to achieve decent inference speed and handle popular models like Llama3 medium and Phi3 which possibility of expansion. 00 tok/s stop reason: completed gpu layers: 13 cpu threads: 15 mlock: true token count: 293/4096 We would like to show you a description here but the site won’t allow us. The ROCm Platform brings a rich foundation to advanced computing by seamlessly integrating the CPU and GPU with the goal of solving real-world problems. Has anyone tested how the inter-gpu bus speed affects inference speed? Name: ZOTAC Gaming GeForce RTX™ 3090 Trinity OC 24GB GDDR6X 384-bit 19. With newer generation CPU and GPU, it may be able to pull out some AI simple tasks. 0 Advanced Cooling, Spectra 2. So, I have gone through some of the videos and posts that mentioned AMD is catching fast with all the ROCm things, and videos showing that SD is working on AMD. Reddit also has a lot of users who are actively engaging in getting AMD competitive in this space, so Reddit is actually probably a very good way to find out the most recent developments. 0 Gaming Graphics Card, IceStorm 2. 00-3. You can do inference in Windows and Linux with AMD cards. ROCm is drastically inferior to CUDA in every single way and AMD hardware has always been second rate. 9 Fakespot Reviews Grade: A Adjusted Fakespot Rating: 3. true. 1. You can disable the integrated GPU in the BIOS. For example, my 6gb vram gpu can barely manage to fit the 6b/7b LLM models when using the 4bit versions. cpp also works well on CPU, but it's a lot slower than GPU acceleration. With 7 layers offloaded to GPU. 5 Gbps PCIE 4. I have a pair of MI100s and a pair of W6800s in one server and the W6800s are faster. I was always a bit hesitant because you hear things about Intel being "the standard" that apps are written for, and AMD was always the cheaper but less supported alternative that you might need to occasionally tinker with to run certain things. We are discussing AMD cards for LLM inference where VRAM is arguably the most important aspect of a GPU and AMD just threw in the towel for this cycle. Also, the people using AMD for all the stuff you mentioned, are using XDNA server GPUs, not a 7900xt. 13s gen t: 15. 2 SSD NVMe Mainboard: Gigabyte B550 Gaming X V2 - AM4 Seen two P100 get 30 t/s using exllama2 but couldn't get it to work on more than one card. AMD did not put much into getting these older cards up to speed with ROCm so the hardware might look like its fast on paper, but that may not be the case in real world use. I was thinking about it for Davinci Resolve and Blender - but, especially with Blender - it's often advised against using an AMD gpu including the RDNA 3 series - e. Valheim Genshin View community ranking In the Top 5% of largest communities on Reddit. 99 (New reddit? Click 3 dots at end of this message) Privated to protest Reddit's upcoming Jan 5, 2025 · Does anyone have experience of using AMD GPU's for offline AI? I'm currently running a 10gb RTX 3080 in my SFF living room PC connected to a 4k LG OLED TV. Finetune Llm on amd gpu rx 580 . Mar 6, 2024 · Did you know that you can run your very own instance of a GPT based LLM-powered AI chatbot on your Ryzen ™ AI PC or Radeon ™ 7000 series graphics card? AI assistants are quickly becoming essential resources to help increase productivity, efficiency or even brainstorm for ideas. I'm considering buying a new GPU for gaming, but in the meantime I'd love to have one that is able to run LLM quicker. Now Nvidia denies this little nugget but the Maxwell series chips did support SLI and the Tesla cards have SLI traces marked out if you remove the case and alter the AMD Ryzen AI 9 HX 370 APU Benchmarks Leak: 12-Core 20% Faster In Multi-Threading, 40% Faster "Radeon 890M" GPU Performance Versus 8945HS Rumor wccftech. Basically AMD's consumer GPUs are great for graphics, but not nearly as versatile as Nvidia's offerings. But, it's really good to get actual feedback with the gpu and the user case - in this particular case, the LLM and ROCm experience. 11 to macOS 11 operating system NVIDIA Graphics with Maxwell architecture or Pascal AMD became more or less pain free in Wayland era only, so, basically, in the last 5 years or so, but they still struggle to get support from a lot of CUDA related software devs, so their graphics solutions are locked around gaming or "office" work still. io with TheBloke's Local LLM docker images using oobabooga's text-generation-webui. Upcoming new desktop CPUs don't seem to contain NPU at all so it make sense. Edit: If you weren't a grad student I would have suggested AMD's MI300X/MI300 but it would be too much on my conscience to make a grad student go through the quirks of AMD's ROCm vs the more established CUDA. AMD have been trying to improve their presence with the release of Rocm and traditionally there hasn’t been much information on the RX6 and 7 series cards. I have two systems, one with dual RTX 3090 and one with a Radeon pro 7800x and a Radeon pro 6800x (64 gb of vRam). Exactly. cpp + AMD doesn't work well under Windows, you're probably better off just biting the bullet and buying NVIDIA. 6. I have a few Crypto motherboards that would allow me to plug 5 or 6 cards in to a single mb and hopefully work together, creating a decent AI / ML machine. While AMD is less preferred in AI content generation, raw firepower isn't that bad. Oct 18, 2024 · Still, many pieces of software widely used for locally hosting open-source large language models such as Ollama, LM Studio, Gpt4All and OobaBooga WebUI are fully compatible with AMD graphics cards and are pretty straightforward to use without an NVIDIA GPU. More specifically, AMD Radeon™ RX 7900 XTX gives 80% of the speed of NVIDIA® GeForce RTX™ 4090 and 94% of the speed of NVIDIA® GeForce RTX™ 3090Ti for single batch Llama2-7B/13B 4bit inference. If you want to run an LLM that's 48GB in size and your GPU has 24GB of VRAM, for every token your GPU computes, your GPU needs to read 24GB twice from either your RAM or SSD/HDD (depending on your cache settings). RISC-V (pronounced "risk-five") is a license-free, modular, extensible computer instruction set architecture (ISA). I'll also give you a heads up: amd gpu support is behind nvida for a lot of software. I really don't want to taint an all AMD build just because GPGPU is needlessly complicated and possibly broken. Yeah, they're a little long in the tooth, and the cheap ones on ebay have been basically been running at 110% capacity for the several years straight in mining rigs and are probably a week away from melting down, and you have to cobble together a janky cooling solution, but they're still by far the best bang-for-the-buck for high-VRAM AI purposes. 8M subscribers in the Amd community. It supports AMD GPU's on windows machine. A 4090 won't help if it can't get data fast enough. 10 CH32V003 microcontroller chips to the pan-European supercomputing initiative, with 64 core 2 GHz workstations in between. Welcome to r/gaminglaptops, the hub for gaming laptop enthusiasts. If you're using Windows, and llama. 6k), and 94% of the speed of NVIDIA RTX 3090Ti (previously $2k). So, the results from LM Studio: time to first token: 10. Good question, for existing LLM service providers (inference engines) from the date the paper was published: " Second, the existing systems cannot exploit the opportunities for memory sharing. 7900 xtx. The top-end RDNA4 GPU will have 16GB RAM. Setup: Processor: AMD Ryzen9 7900X Motherboard: MSI X670-P Pro WIFI GPU: MSI RX7900-XTX Gaming trio classic (24GB VRAM) Welcome to /r/AMD — the subreddit for all things AMD; come talk about Ryzen, Radeon, Zen4, RDNA3, EPYC, Threadripper, rumors, reviews, news and more. Slow though at 2t/sec. Gaming. The only reason to offload is because your GPU does not have enough memory to load the LLM (a llama-65b 4-bit quant will require ~40GB for example), but the more layers you are able to run on GPU, the faster it will run. I have a hard time finding what GPU to buy (just considering LLM usage, not gaming). More specifically, AMD RX 7900 XTX ($1k) gives 80% of the speed of NVIDIA RTX 4090 ($1. The Radeon Subreddit - The best place for discussion about Radeon and AMD products. AMD does not seem to have much interest in supporting gaming cards in ROCm. The GPU, an RTX 4090, looks great, but I'm unsure if the CPU is powerful enough. If you have an AMD Radeon™ graphics card, please: i. NVIDIA releasing their AI tuned CPU chipsets next year doing the same. That said, I don't see much slow down when running a 5_1 and leaving the the CPU to do some of the work, at least on my system with the latest CPU/RAM speeds. Move the slider all the way to “Max”. 16 votes, 34 comments. Plus tensor cores speed up neural networks, and Nvidia is putting those in all of their RTX GPUs (even 3050 laptop GPUs), while AMD hasn't released any GPUs with tensor cores. ai or runpod. I do think there will be a shift in gaming on the Mac. g. MLC LLM makes it possible to compile LLMs and deploy them on AMD GPUs using its ROCm backend, getting competitive performance. 00 tok/s stop reason: completed gpu layers: 13 cpu threads: 15 mlock: true token count: 293/4096 Welcome to /r/AMD — the subreddit for all things AMD; come talk about Ryzen, Radeon, Zen4, RDNA3, EPYC, Threadripper, rumors, reviews, news and more. 70GHz, tray with CPU coolers: 2x DYNATRON J2 AMD SP5 1U 24x Kingston FURY Renegade Pro RDIMM 16GB, DDR5-4800, CL36-38-38, reg ECC, on-die ECC However, I don't know of anyone who has built such a system, so it's all theoretical. Only people that are benefitting from AMD’s cheaper VRAM are gamers, because they can actually make use of AMD’s gaming software features. I anticipate the future of consumer AI / LLM won’t be GPU driven, at least in the way we understand it now I am still running a 10 series GPU on my main workstation, they are still relevant in the gaming world and cheap. Generally the bottlenecks you'll encounter are roughly in the order of VRAM, system RAM, CPU speed, GPU speed, operating system limitations, disk size/speed. LLM services often use advanced decoding algorithms, such as parallel sampling and beam search, that generate multiple outputs per request. 5 GPTQ on GPU 9. Oh I don't mind affording the 7800XT for more performance, I just don't want to spend money on something low value like Nvidia's GPUs. Therefore, it sounds like cards like AMD Instinct or 7900xtx, or any other card with high memory bandwidth will be considerably slow at inference in multi GPU configurations if they are locked to a maximum of 30GB/s on pcie gen 4 16x (likely even slower). 5 GGML on GPU (cuda) 8 GGML on GPU (Rocm) 5 GGML on GPU (OpenCL) 2. I've been able to run 30B 4_1 with all layers offloaded to the GPU. Budget: Around $1,500 Requirements: GPU capable of handling LLMs efficiently. 5. Check “GPU Offload” on the right-hand side panel. M series Macs have no heating issues. You need to get the device ids for the GPU. I was able to run Gemma 2B int8 quantization on Intel i5-7200U with 8GB DDR4 RAM. Reply reply Hot_Warthog5798 Hello, I see a lot of posts about "vram" being the most important factor for LLM models. Select the model at the top, then that’s it. 9 Analysis Performed at: 10-18-2022 Welcome to /r/AMD — the subreddit for all things AMD; come talk about Ryzen, Radeon, Zen4, RDNA3, EPYC, Threadripper, rumors, reviews, news and more. Yesterday I even got Mixtral 8x7b Q2_K_M to run on such a machine. If you're not familiar with the technology at play here's a short explanation. 41s speed: 5. 5 GGML split between GPU/VRAM and CPU/system RAM 1 GGML on CPU/system RAM Welcome to /r/AMD — the subreddit for all things AMD; come talk about Ryzen, Radeon, Zen4, RDNA3, EPYC, Threadripper, rumors, reviews, news and more. EDIT: As a side note power draw is very nice, around 55 to 65 watts on the card currently running inference according to NVTOP. An easy way to check this is to use "GPU caps viewer", go to the tab titled OpenCl and check the dropdown next to "No. It thus supports AMD software stack: ROCm. This is a community for engineers, developers, consumers and artists that would like to post content and start discussions that represent AMD GPU technology honestly and At an architectural level AMD and Nvidia's GPU cores differ (duh) and would require separate low-level tuning, which most projects have not done (a bit of a catch-22, but AMD not providing support for any cards developers would have access to, and most end-users not being able to use the code anyway (ROCm platform support has been, and while Nov 29, 2024 · In reality, when using AMD graphics cards with AMD GPU compatible LLM software, you might sometimes come across either a little bit more complex setup (not necessarily always, as you’ve already seen with some of the examples above), and a tad bit slower inference (which will likely become less of an issue as the ROCm’s framework evolves). Each CCD has its preferred memory slots with fastest access, so the code needs to be NUMA-aware to utilize the full potential of the architecture. with only 8gb vram you will be using 7B parameter models but you can push higher parameters but understand that the models will offload layers to the sysram and use cpu too if you do so. With dual 3090, 48 GB VRAM opens up doors to 70b models entirely on VRAM AMD uses chiplets in Zen CPUs, each CCD (core complex die) is basically a small separate CPU connected to the IO die with GMI3 links. AMD refused to support AI/ML in the consumer-level space until literally this January. of CL devices". It would be great if someone can try stable diffusion and text-generation webui and run some smaller models by GPU, and larger LLM by CPU. The amount of effort AMD puts into getting RDNA3 running for AI is laughable compared to their server hardware and software. It would probably never get to where Windows gaming is today. But here I am enjoying it over the last few days. But there is no guarantee for that. That's a massive regression compared to 7900XTX and performance-wise it should be at best at the 7900XTX level. iv. Recently, I wanted to set up a local LLM/SD server to work on a few confidential projects that I cannot move into the cloud. 3D3Metal on Sonoma makes gaming on MacOS fun again. I'm actually using locallama for compressing data. Though I put GPU speed fairly low because I've seen a lot of reports of fast GPUs that are blocked by slow CPUs. So I'm going to guess that unless NPU has dedicated memory that can provide massive bandwidth like GPU's GDDR VRAM, NPUs usefulness for running LLM entirely on it is quite limited. iii. 0 RGB Lighting, ZT-A30900J-10P Company: Amazon Product Rating: 3. Oct 15, 2024 · Hi everyone, I’m upgrading my setup to train a local LLM. I just finished setting up dual boot on my PC since I needed a few linux only specific things, but decided to try inference on the linux side of things to see if my AMD gpu would benefit from it. 11 or later NVIDIA Graphics with Kepler architecture with OS X 10. Name: ZOTAC Gaming GeForce RTX™ 3090 Trinity OC 24GB GDDR6X 384-bit 19. By the end of this year AMD and Intel will have similar chipsets to apple by years End. If it feels like AMD and Nvidia don't care about us it's because from any reasonable financial standpoint they shouldn't - we're a tiny, tiny, tiny niche. Good for casual gaming and programs (backed by 1 comment) Users disliked: Frequent gpu crashes and driver issues (backed by 3 comments) Defective or damaged products (backed by 16 comments) Compatibility issues with bios and motherboards (backed by 2 comments) According to Reddit, AMD is considered a reputable brand. While you can run any LLM on a CPU, it will be much, much slower than if you run it on a fully supported GPU. But the Tesla series are not gaming cards, they are compute nodes. And GPU+CPU will always be slower than GPU-only. Nov 29, 2024 · In reality, when using AMD graphics cards with AMD GPU compatible LLM software, you might sometimes come across either a little bit more complex setup (not necessarily always, as you’ve already seen with some of the examples above), and a tad bit slower inference (which will likely become less of an issue as the ROCm’s framework evolves). I just want to make a good investment and it looks like there isn't one at the moment: you get a) crippled Nvidia cards (4060 Ti 16 GB, crippled for speed, 4070/Ti crippled for VRAM), b) ridiculously overpriced Nvidia cards (4070 TiS, 4080, 4080 S, 4090) or c [GPU] ASRock Radeon RX 6700 XT Challenger D Gaming Graphic Card, 12GB GDDR6 VRAM, AMD RDNA2 (RX6700XT CLD 12G) - $498. I'm new to LLMs, and currently experimenting with dolphin-mixtral, which is working great on my RTX 2060 Super (8 GB). Nobody uses ROCm because for the last decade+, every college student could use and learn CUDA on their nVidia gaming My question is about the feasibility and efficiency of using an AMD GPU, such as the Radeon 7900 XT, for deep learning and AI projects. 03 even increased the performance by x2: " this Game Ready Driver introduces significant performance optimizations to deliver up to 2x inference performance on popular AI models and applications such as My current PC is the first AMD CPU I've bought in a long, long time. With adrenaline installed you installed drivers that the integrated GPU needed. Here comes the fiddly part. I plan to upgrade the RAM to 64 GB and also use the PC for gaming. Discover discussions, news, reviews, and advice on finding the perfect gaming laptop. I dont know how to run them distributed, but on my dedicated server (i9 / 64 gigs of ram) i run them quite nicely on my custom platform. Assuming that AMD invests into making it practical and user-friendly for individuals. The discrete GPU is normally loaded as the second or after the integrated GPU. AMD cards are good for gaming, maybe best, but they are years behind NVIDIA with AI computing. You can test the multi GPU setups on vast. 8% in a benchmark against GPT-3. If you were to step up to the XTX (which for gaming anyway is considered better value) then you get 24 GB VRAM. So I grab the API, make a plot of the prices for the next day and give the data to the LLM and ask it to tell me the smartest time to use my a Hi everyone, I have a 12-year-old Macbook Pro and would like to get into the development of generative AI, especially LLM. According to the AMD 2024 Q1 financial report the "gaming segment" (which is us using their desktop cards) total revenue was $922m and that was down 48% year over year. Amd is a small shop and was late in all in AI( maybe 6 month ish) Lisa su saw OpenAI chat got moment and then quickly pivot the company to all in AI. This software enables the high-performance operation of AMD GPUs for computationally-oriented tasks in the Linux operating system. You can run Mistral 7B (or any variant) Q4_K_M with about 75% of layers offloaded to GPU, or you can run Q3_K_S with all layers offloaded to GPU. 5600G is also inexpensive - around $130 with better CPU but the same GPU as 4600G. I had my 128GB M2 Studio load and run the 90GB Falcon180b model. Subscribe to never miss Radeon and AMD news. So, decided to go ahead with the AMD setup. Supported architecture includes: Intel Processor with Intel HD and Iris Graphics Ivy Bridge series or later with OS X 10. ii. Jan 3, 2025 · Which software works best with my hardware setup? (AMD GPU, Windows OS) Any compatibility issues or optimization tips for these tools? Operating System. Originally designed for computer architecture research at Berkeley, RISC-V is now used in everything from $0. 99 upvotes · comments r/LocalLLaMA For a gpu, whether 3090 or 4090, you need one free pcie slot (electrical), which you will probably have anyway due to the absence of your current gpu – but the 3090/4090 takes physically the space of three slots. cpp even when both are GPU-only. Welcome to /r/AMD — the subreddit for all things AMD; come talk about Ryzen, Radeon, Zen4, RDNA3, EPYC, Threadripper, rumors, reviews, news and more. I never thought I would ever play Control on my Mac. Join our passionate community to stay informed and connected with the latest trends and technologies in the gaming laptop world. If you want to install a second gpu, even a pcie 1x (with riser to 16x) is sufficient in principle. However, this means you need an absurd amount of vram in your gpu for this to work. It's likely that the 7900XT/X and 7800 will get support once the workstation cards (AMD Radeon™ PRO W7900/W7800) are out. For now, Nvidia is the only real game in town. That's an approximate list. Freely discuss news and rumors about Radeon Vega, Polaris, and GCN, as well as AMD Ryzen, FX/Bulldozer, Phenom, and more. For example I have a energy provider that changes the prices hourly but tell you the prices for the next day. An amd gaming pc doesn't tell us a lot about your specs, and llms can really push your workload to its limits. cpp. 4 GHz) GPU: RTX 4090 24 GB RAM: 32 GB DDR4-3600MHz Storage: 1 TB M. I use ollama and lm studio and they both work. [GPU]MSI AMD Radeon RX 6900 XT Gaming Z Trio ($1099-$50 MIR) - $1049. Each of those will work fine. System Specs: AMD Ryzen 9 5900X 32 GB DDR4 3600 Mhz CL16 RAM 2TB SN850 NVME AMD 6900 XT 16GB (Reference Model + Barrow Waterblock) The 4600G is currently selling at price of $95. I have now setup LM Studio which does have AMD OpenCL support which I can get a 13b model like codellama instruct Q8_0 offloaded with all layers onto the GPU but performance is still very bad at ~2tok/s and 60s time to first token. Nvidia is still the market leader purely because of CUDA’s massive optimizations and proven performance in ML/DL applications. We would like to show you a description here but the site won’t allow us. The model is around 15 GB with mixed precision, but my current hardware (old AMD CPU + GTX 1650 4 GB + GT 1030 2 GB) is extremely slow (it’s taking around 100 hours per epoch. So I wonder, does that mean an old Nvidia m10 or an AMD firepro s9170 (both 32gb) outperforms an AMD instinct mi50 16gb? Asking because I recently bought 2 new ones and wondering if I should just sell them and get something else with higher vram I think Amd was only prioritizing MI series for obvious reason. 11 to macOS 11 operating system NVIDIA Graphics with Maxwell architecture or Pascal Welcome to r/gaminglaptops, the hub for gaming laptop enthusiasts. Memory bandwidth /= speed. In my case the integrated GPU was gfx90c and discrete was llama. AMD uses chiplets in Zen CPUs, each CCD (core complex die) is basically a small separate CPU connected to the IO die with GMI3 links. amd doesn't care, the missing amd rocm support for consumer cards killed amd for me. cuda is the way to go, the latest nv gameready driver 532. Besides ROCm, our Vulkan support allows us to generalize LLM deployment to other AMD devices, for example, a SteamDeck with an AMD APU. I don't know about image generation, but text generation on AMD works perfectly. I thought about building a AMD system but they had too many limitations / problems reported as of a couple of years ago. So i have about $500-600 and already a good server 128-256gb ddr3 and 24 xeon e5-2698 V2 cores so there i don't need an… Free speech is of high importance here so please post anything related to AMD processors and technologies including Radeon gaming, Radeon Instinct, integrated GPU, CPUs, etc. AMD is a potential candidate. ) in 12 hours, which scored 97. Should I set up a separate, dedicated OS (dual boot) just for running local LLMs? Can I install the software on my current gaming OS? What are the potential downsides of doing so? Storage We would like to show you a description here but the site won’t allow us. Also, for this Q4 version I found 13 layers GPU offloading is optimal. Only downside I've found, it does't work with continue dev. I think all LLM interfaces already have support for AMD graphics cards via ROCm, including on Windows. The ideal setup is to cram the entire AI model into your gpu vram, and then have your gpu run the AI. It includes a 6-core CPU and 7-core GPU. com Open Well, exllama is 2X faster than llama. Make sure AMD ROCm™ is being shown as the detected GPU type. /r/AMD is community run and does not represent AMD in any capacity unless specified. AMD will have plenty of opportunities to gain market share the consumer space in the coming decade. Also, I get that you'll have no fine tuning software support for better performance, like ROCm or OpenVino etc. Of course llama. EDIT: I might add the GPU support is nomic Vulkan which only support GGUF model files with Q4_0 or Q4_1. 5cm is enough for cooling them, keep the case flat and open or get mesh design for 24/7 no need for two 16x Even if that might hurt, and makes you want to defend AMD. Still the support for Radeon instinct series is still very good and Radeon gaming card was never promised for AI compute. I'm planning to build a GPU PC specifically for working with large language models (LLMs), not for gaming. Jan 5, 2025 · Does anyone have experience of using AMD GPU's for offline AI? I'm currently running a 10gb RTX 3080 in my SFF living room PC connected to a 4k LG OLED TV. How much does VRAM matter? Now their cards can't do anything outside of gaming and AI/ML has proven itself to have direct use cases in gaming with things like DLSS and Frame generation and indirect ones in using these ML tech takes burden of the GPU, allowing you to push it harder on current gen tech or add next gen features like Ray Tracing. Nice that you have access to the goodies! Use ggml models indeed, maybe wizardcoder15b, starcoderplus ggml. If you only care about local AI model, with no regards for gaming performance, dual 3090 will be way better, as LLM front ends like Oobabooga supports multi-GPU VRAM loading. Source: I've got it working without any hassle on my win11 pro machine and a rx6600. cpp supports AMD GPUs well, but maybe only on Linux (not sure; I'm Linux-only here). Check the “GPU Offload” checkbox, and set the GPU layers slider to max. If you have an AMD Ryzen AI PC you can start chatting! a. Here are the specs: CPU: AMD Ryzen 9 5950X (16 x 3. so I'm not sure if that is just because my GPU isn't good for the model or my GPU isn't being fully correctly you can run llm on windows using either koboldcpp-rocm or llama to load the models. 11 or later AMD Graphics with GCN or RDNA architecture with OS X 10. So, unless it's for business, there's no point in taking Nvidia. I use the PC for a mix of media consumption, gaming (recently discovered Helldivers 2), and some AI (mostly text-generation in LM Studio). I wanted to buy a new Macbook Pro, but Apple products are really expensive in Germany -- for instance, a Mac Mini with 16G RAM and 512G SSD would cost 1260 euros. Grab the highest memory/GPU config you can reasonably afford. The LLM models that you can run are limited though. When I finally upgrade my GPU, if AMD don't have decent GPGPU, I'll be forced to look into nVidia :( AMD have great GPUs, but they throw away the power they possess for no reason. I don't know where lm studio is here, and I keep hearing its getting better- but I also know you should expect more headaches, slightly worse Welcome to r/gaminglaptops, the hub for gaming laptop enthusiasts. whether if it matters is up for debate It’s probably the easiest way to get 32GB+ RAM on a GPU. you can run llm on windows using either koboldcpp-rocm or llama to load the models. cawbb upx qua bquv qoyq xrjoek ywcmgjoy qjp jpqkveo bffvfr