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I run Deepseek V3 locally as my daily driver and I find it affordable, fast and effective. The article assumes GPU which in my opinion is not the best way to se
by ryan_glass 1y ago
I run Deepseek V3 locally as my daily driver and I find it affordable, fast and effective. The article assumes GPU which in my opinion is not the best way to serve large models like this locally. I run a mid-range EPYC 9004 series based home server on a supermicro mobo which cost all-in around $4000. It's a single CPU machine with 384GB RAM (you could get 768GB using 64GB sticks but this costs more). No GPU means power draw is less than a gaming desktop. With the RAM limitation I run an Unsloth Dynamic GGUF which, quality wise in real-world use performs very close to the original. It is around 270GB which leaves plenty of room for context - I run 16k context normally as I use the machine for other things too but can up it to 24k if I need more. I get about 9-10 tokens per second, dropping to 7 tokens/second with a large context. There are plenty of people running similar setups with 2 CPUs who run the full version at similar tokens/second.
- 6Az4Mj4D 1y agoCan we run Deepseek using Ollama or something similar for code generation like Github copilot on a 40 core CPU with about 256GB RAM say 200 GB usable for the model?
- nardi 1y agoWhats your prompt processing speed? That’s more important in this situation than output TPS. If you have to wait minutes to start getting an answer, that makes it much worse than a cloud-hosted version.
- pclmulqdq 1y agoI assume KV caching makes this a non issue, but I'm also curious.
- idonotknowwhy 1y agoIf you're just chatting with it starting with "Hi", that's correct. The conversation remains in the KV cache as it grows gradually. But if you're posting code, writing drafts, or even small snippets of articles, etc in there it becomes a huge problem.
- pclmulqdq 1y agoUsually, when people think about the prompt tokens for a chat model, the initial system prompt is the vast majority of the tokens and it's the same regardless for many usage modes. You might have a slightly different system prompt for code than you have for English or for chatting, but that is 3 prompts which you can permanently put in some sort of persistent KV cache. After that, only your specific request in that mode is uncached.
- ryao 1y agoIf he is doing multiturn conversations, he can reuse the kv cache from the last turn and skip the prompt processing on the history that would make time to first token too slow, by only doing prompt processing on his actual prompt for the current turn. This turns a quadratic amount of tokens to process into a linear number. I am not sure if this is what he is doing, but that is what I would do if I had his hardware.
- ryan_glass 1y agoPrompt eval time varies a lot with context but it feels real-time for short prompts - approx 20 tokens per second but I haven't done much benchmarking of this. When there is a lot of re-prompting in a long back and forth it is still quite fast - I do use KV cache which I assume helps and also quantize the KV cache to Q8 if I am running contexts above 16k. However, if I want it to summarize a document of say 15,000 words it does take a long time - here I walk away and come back in about 20 minutes and it will be complete.
- jeff_carr 1y agoI am impressed. Your personal website is down. HN doesn't allow private messages. I'm Jeff Carr. I co-founded digital ocean. I assume I can't post email addresses here, but I will try. lets see how smart things are from banning me. I am: wit AT wit com
- p12tic 1y agoState of the art of local models is even further. For example, look into https://github.com/kvcache-ai/ktransformers https://github.com/kvcache-ai/ktransformers, which achieve >11 tokens/s on a relatively old two socket Xeon servers + retail RTX 4090 GPU. Even more interesting is prefill speed at more than 250 tokens/s. This is very useful in use cases like coding, where large prompts are common. The above is achievable today. In the mean time Intel guys are working on something even more impressive. In https://github.com/sgl-project/sglang/pull/5150 https://github.com/sgl-project/sglang/pull/5150 they claim that they achieve >15 tokens/s generation and >350 tokens/s prefill. They don't share what exact hardware they run this on, but from various bits and pieces over various PRs I reverse-engineered that they use 2x Xeon 6980P with MRDIMM 8800 RAM, without GPU. Total cost of such setup will be around $10k once cheap Engineering samples hit eBay.
- pclmulqdq 1y agoCPUs are quietly becoming very well-balanced machines for BS 1 inference. The latest Intel Xeons should be at ~20 TPS.
- platevoltage 1y agoImpressive. I need to look more into this. I'm doing my best to limit my LLM usage to what I can run locally.
- jbellis 1y agoimpressive, but that's 1/5 to 1/10 of the throughput that you'd get with a hosted provider, with 1/4 to 1/8 the supported context
- michelsedgh 1y agoDude he's running locally, and I think this setup is the best bang for the buck if you wanna run locally, we're not comparing to data centers, you gotta keep it in perspective. That's very impressive results for running local. Thanks for the numbers you saved me a chatgpt search :)
- carstenhag 1y agoTitle says: locally it's expensive Other person says: I had to spend 4000$ and it's still slow
- justsid 1y agoNot to mention that $4000 is in fact expensive. If anything the OP really makes the point of the articles title.
- BoorishBears 1y agoCPU-only is really terrible bang for your buck, and I wish people would stop pushing these impractical builds on people genuinely curious in local AI. The KV cache won't soften the blow the first time they paste a code sample into a chat and end up waiting 10 minutes with absolutely no interactivity before they even get first token. You'll get an infinitely more useful build out of a single 3090 and sticking to stuff like Gemma 27B than you will out of trying to run Deepseek off a CPU-only build. Even a GH200 struggles to run Deepseek at realistic speeds with bs=1, and there's an entire H100 attached to CPU there: there just isn't a magic way to get "affordable fast effective" AI out of a CPU offloaded model right now.
- ryan_glass 1y agoThe quality on Gemma 27B is nowhere near good enough for my needs. None of the smaller models are.
- refibrillator 1y ago> Unsloth Dynamic GGUF which, quality wise in real-world use performs very close to the original How close are we talking? I’m not calling you a liar OP, but in general I wish people perpetuating such broad claims would be more rigorous. Unsloth does amazing work, however as far as I’m aware even they themselves do not publish head to head evals with the original unquantized models. I have sympathy here because very few people and companies can afford to run the original models, let alone engineer rigorous evals. However I felt compelled to comment because my experience does not match. For relatively simple usage the differences are hard to notice, but they become much more apparent in high complexity and long context tasks.
- ryan_glass 1y agoYou are right that I haven't been rigorous - it's easy to benchmark tokens/second but quality of output is more difficult to nail down. I couldn't find any decent comparisons for Unsloth either. So I just tried a few of their models out, looking for something that was 'good enough' i.e. does all I need: coding, summarizing documents, troubleshooting anything and everything. I would like to see head to head comparisons too - maybe I will invest in more RAM at some stage but so far I have no need for it. I ran some comparisons between the smaller and larger versions of the Unsloth models and interestingly (for me anyway) didn't notice a huge amount of difference in quality between them. But, the smaller models didn't run significantly faster so I settled for the biggest model I could fit in RAM with a decent context. For more complex coding I use Deepseek R1 (again the Unsloth) but since it's a reasoning model it isn't real-time so no use as my daily driver.
- ysosirius 1y agoHow do you find the quality of the output compares to that of, say, o3 or Sonnet 4?
- ryan_glass 1y agoTo be honest I haven't used o3 or Sonnet as the code I work with is my own proprietary code which I like to keep private, which is one reason for the local setup. For troubleshooting day to day things I have found it at least as good as than the free in-browser version of ChatGPT (not sure which model it uses).
- 3eb7988a1663 1y agoDo you have hard numbers on the idle/average/max power draw? I assumed that server machines are built as if they are going to red-lined constantly so put less effort into low-utilization optimizations.
- ryan_glass 1y agoNo hard numbers I'm afraid in that I don't monitor the power draw. But the machine uses a standard ATX power supply: a Corsair RM750e 750W PSU and the default TDP of the CPU is 280W - I have my TDP set at 300W. It is basically built like a desktop - ATX form factor, fans spin down at idle etc.
- 3eb7988a1663 1y agoApproximation is still better than I was expecting. You said supermicro and I was assuming a pizza box with dual power supplies sucking down 1kw at idle. That it can run with a large, but not unreasonable PSU says enough.
- dotancohen 1y agoJust curious what your use cases are? What type of texts are you producing? Thank you.
- ysosirius 1y agoI've always wondered this as well, and never seem to get an answer. Why would someone want to do this when they can get a better result either renting in the cloud, or just using a subscription? Obviously I see the value in having something local from a control and privacy perspective, but it's surely always a net loss in terms of quality and capability of output, right?
- ryan_glass 1y agoCoding, my own proprietary code hence my desire for local hosting, a decent amount of legacy code. General troubleshooting of anything and everything from running Linux servers to fixing my car. Summarizing and translation of large documents occasionally. Also, image generation and other automations but obviously not LLMs for this.
- dotancohen 1y agoTerrific, thank you. If you don't mind another question, how do you adapt the LLM to your codebase? Keep the whole thing in context? Fine tune on your own code? Fine tune on lots of code in whatever language you're using (e.g. Python, Rust)? Just rely on the original model training? Thank you very much!
- mechagodzilla 1y agoI use a dual-socket 18-core (so 36 total) xeon with 768GB of DDR4, and get about 1.5-2 tokens/sec with a 4-bit quantized version of the full deepseek models. It really is wild to be able to run a model like that at home.
- stirfish 1y agoDumb question: would something like this have a graphics card too? I assume not
- mechagodzilla 1y agoYeah, it was just a giant HP workstation - I currently have 3 graphics cards in it (but only 40GB total of VRAM, so not very useful for deepseek models).
- twotwotwo 1y agoThe latest V3 strikes me as a really practical go-to among open-weights models. Lots of tasks don't need the reasoning tokens, and not having to wait for them is nice. (If something does need it you can always switch.) If you're not running it yourself a couple providers have it with full context, 80tps, and a promise not to use your data. 9004 home server is awesome!
- blindriver 1y agoI thought GPUs with a lot of extremely fast memory was required for inference. Are you saying that we can accomplish inference with just a large amount of system memory that is non-unified and no GPU? How is that possible?
- adastra22 1y agoI’m confused as to why you think a GPU is necessary? It’s just linear algebra.
- oreoftw 1y agomost likely he was referring the fact that you need plenty of GPU-fast memory to keep the model, and GPU cards have it.
- deleted 1y ago[deleted]
- adastra22 1y agoThere is nothing magical about GPU memory though. It’s just faster. But people have been doing CPU inference since the first llama code came out.
- ryan_glass 1y agoBasically it comes down to memory bandwidth of server CPUs being decent. A bit of oversimplification here but... The model and context have to be pulled through RAM (or VRAM) every time a new token is generated. CPUs that are designed for servers with lots of cores have decent bandwidth - up to 480GB/s with the EPYC 9 series and they can use 16 channels simultaneously to process memory. So, in theory they can pull 480GB through the system every second. GPUs are faster but you also have to fit the entire model and context into RAM (or VRAM) so for larger models they are extremely expensive because a decent consumer GPU only has 24GB of VRAM and costs silly money, if you need 20 of them. Whereas you get a lot of RDIMM RAM for a couple thousand bucks so you can run bigger models and 480GB/s gives output faster than most people can read.
- goldielox 1y agoSo, in your opinion, hardware wise, as a general purpose tinkering/learning self lab hardware, how would you grade the decked out framework desktop for 2.7k?