I see a lot of talk of Ollama here, which I personally don’t like because:
The quantizations they use tend to be suboptimal
It abstracts away llama.cpp in a way that, frankly, leaves a lot of performance and quality on the table.
It abstracts away things that you should really know for hosting LLMs.
I don’t like some things about the devs. I won’t rant, but I especially don’t like the hint they’re cooking up something commercial.
So, here’s a quick guide to get away from Ollama.
First step is to pick your OS. Windows is fine, but if setting up something new, linux is best. I favor CachyOS in particular, for its great python performance. If you use Windows, be sure to enable hardware accelerated scheduling and disable shared memory.
Ensure the latest version of CUDA (or ROCm, if using AMD) is installed. Linux is great for this, as many distros package them for you.
Install Python 3.11.x, 3.12.x, or at least whatever your distro supports, and git. If on linux, also install your distro’s “build tools” package.
As far as I am concerned, 3 matter to “home” hosters on consumer GPUs:
Exllama (and by extension TabbyAPI), as a very fast, very memory efficient “GPU only” runtime, supports AMD via ROCM and Nvidia via CUDA: https://github.com/theroyallab/tabbyAPI
Aphrodite Engine. While not strictly as vram efficient, its much faster with parallel API calls, reasonably efficient at very short context, and supports just about every quantization under the sun and more exotic models than exllama. AMD/Nvidia only: https://github.com/PygmalionAI/Aphrodite-engine
This fork of kobold.cpp, which supports more fine grained kv cache quantization (we will get to that). It supports CPU offloading and I think Apple Metal: https://github.com/Nexesenex/croco.cpp
Now, there are also reasons I don’t like llama.cpp, but one of the big ones is that sometimes its model implementations have… quality degrading issues, or odd bugs. Hence I would generally recommend TabbyAPI if you have enough vram to avoid offloading to CPU, and can figure out how to set it up. So:
Open a terminal, run git clone https://github.com/theroyallab/tabbyAPI.git
This can go wrong, if anyone gets stuck I can help with that.
Next, figure out how much VRAM you have.
Figure out how much “context” you want, aka how much text the llm can ingest. If a models has a context length of, say, “8K” that means it can support 8K tokens as input, or less than 8K words. Not all tokenizers are the same, some like Qwen 2.5’s can fit nearly a word per token, while others are more in the ballpark of half a work per token or less.
Exllama has a feature called “kv cache quantization” that can dramatically shrink the VRAM the “context” of an LLM takes up. Unlike llama.cpp, it’s Q4 cache is basically lossless, and on a model like Command-R, an 80K+ context can take up less than 4GB! Its essential to enable Q4 or Q6 cache to squeeze in as much LLM as you can into your GPU.
There are all sorts of finetunes… and a lot of straight-up garbage. But I will post some general recommendations based on total vram:
4GB: A very small quantization of Qwen 2.5 7B. Or maybe Llama 3B.
6GB: IMO llama 3.1 8B is best here. There are many finetunes of this depending on what you want (horny chat, tool usage, math, whatever). For coding, I would recommend Qwen 7B coder instead: https://huggingface.co/models?sort=trending&search=qwen+7b+exl2
8GB-12GB Qwen 2.5 14B is king! Unlike it’s 7B counterpart, I find the 14B version of the model incredible for its size, and it will squeeze into this vram pool (albeit with very short context/tight quantization for the 8GB cards). I would recommend trying Arcee’s new distillation in particular: https://huggingface.co/bartowski/SuperNova-Medius-exl2
16GB: Mistral 22B, Mistral Coder 22B, and very tight quantizations of Qwen 2.5 34B are possible. Honorable mention goes to InternLM 2.5 20B, which is alright even at 128K context.
20GB-24GB: Command-R 2024 35B is excellent for “in context” work, like asking questions about long documents, continuing long stories, anything involving working “with” the text you feed to an LLM rather than pulling from it’s internal knowledge pool. It’s also quite goot at longer contexts, out to 64K-80K more-or-less, all of which fits in 24GB. Otherwise, stick to Qwen 2.5 34B, which still has a very respectable 32K native context, and a rather mediocre 64K “extended” context via YaRN: https://huggingface.co/DrNicefellow/Qwen2.5-32B-Instruct-4.25bpw-exl2
32GB, same as 24GB, just with a higher bpw quantization. But this is also the threshold were lower bpw quantizations of Qwen 2.5 72B (at short context) start to make sense.
48GB: Llama 3.1 70B (for longer context) or Qwen 2.5 72B (for 32K context or less)
Again, browse huggingface and pick an exl2 quantization that will cleanly fill your vram pool + the amount of context you want to specify in TabbyAPI. Many quantizers such as bartowski will list how much space they take up, but you can also just look at the available filesize.
Put it in your TabbyAPI models folder, and follow the documentation on the wiki.
There are a lot of options. Some to keep in mind are chunk_size (higher than 2048 will process long contexts faster but take up lots of vram, less will save a little vram), cache_mode (use Q4 for long context, Q6/Q8 for short context if you have room), max_seq_len (this is your context length), tensor_parallel (for faster inference with 2 identical GPUs), and max_batch_size (parallel processing if you have multiple user hitting the tabbyAPI server, but more vram usage)
And be careful with your sampling settings when using LLMs. Different models behave differently, but one of the most common mistakes people make is using “old” sampling parameters for new models. In general, keep temperature very low (<0.1, or even zero) and rep penalty low (1.01?) unless you need long, creative responses. If available in your UI, enable DRY sampling to tamp down repition without “dumbing down” the model with too much temperature or repitition penalty. Always use a MinP of 0.05 or higher and disable other samplers. This is especially important for Chinese models like Qwen, as MinP cuts out “wrong language” answers from the response.
Now, once this is all setup and running, I’d recommend throttling your GPU, as it simply doesn’t need its full core speed to maximize its inference speed while generating. For my 3090, I use something like sudo nvidia-smi -pl 290, which throttles it down from 420W to 290W.
Sorry for the wall of text! I can keep going, discussing kobold.cpp/llama.cpp, Aphrodite, exotic quantization and other niches like that if anyone is interested.
Bookmarked and will come back to this. One thing that may be if interest to add is for AMD cards with 20gb of ram. I’d suppose that it would be Qwen 2.5 34B with maybe less strict quant or something.
Also, it may be interesting to look at the AllenAI molmo related models. I’m kind of planning to do this myself but haven’t had time as yet.
Yep. 20GB is basically 24GB, though its too tight for 70B models.
One quirk for 7900 owners is that installing flash attention for long context usage can be a pain. Apparently it is doable now, I need to dig up the link, but it might just be easier to use kobold.cpp rocm with its native flash attention.
As for vision models, that is a whole different can of worms. Exllama does not support this, so you’d need a framework that does.
Aphrodite is a fork of vllm. You should check it out!
If you are looking for raw batched speed, especially with some redundant context, I would actually recommend sglang instead. Check out its experimental flags too.
I’d be interested in setting up the highest quality models to run locally, and I don’t have the budget for a GPU with anywhere near enough VRAM, but my main server PC has a 7900x and I could afford to upgrade its RAM - is it possible, and if so how difficult, to get this stuff running on CPU? Inference speed isn’t a sticking point as long as it’s not unusably slow, but I do have access to an OpenAI subscription so there just wouldn’t be much point with lower quality models except as a toy.
If your “FIRST STEP” is to choose an OS: Fuck that.
You should never have to change your OS just to use this crap. It’s all written in Python. It should work on every OS available. Your first step is installing the prerequisites.
If you’re using something like Continue for local coding tasks, CodeQwen is awesome, and you’ll generally want a context window of 120k or so because for coding, you want all the code context - or else the LLM starts spitting out repetitious stuff, or can’t ingest all of your context so it’ll rewrite stuff that’s already there.
CodeQwen 1.5 is pretty old at this point, afaik made obsolete by their latest release.
The Qwen models (at least 2.5) are really only good to like 32K, which is still a ton of context. But I’ve been testing Qwen 32B at 64K -90K and even that larger model is… Not great.
32K is generally enough to get the jist of whatever you’re trying to fill in.
Llamafile is fine, but it still leaves a lot of performance on the table.
You can setup kobold.cpp with Q8 flash attention without ever having to install pytorch, which is the real headache. It does have a little python launch script, but its super minimal.
You can use the native llama.cpp server for absolutely zero python usage.
I know this is not the theme of this post, but I wonder if there’s an LLM that doesn’t hallucinate when asked to summarize information of a group of documents. I tried Gpt4all for simple queries like finding out which documents mentioned a certain phrase. It often gave me filenames that didn’t actually exist. Hallucinating contents is one thing but making up data source is just horrible.
Command R is built for this if you have the vram to swing it, otherwise GLM4 (or MiniG as linked below) is great. The later, unfortunately, doesn’t work with TabbyAPI, so you have to use something like Kobold.cpp.
You also have to use very low (basically zero) temperature and be careful with other sampling settings, and watch your context length.
There are more sophisticated RAG setups some of these UIs (like open Web UI) integrate, and sometimes you’ll need to host an embeddings model alongside the llm for that to work.
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ollama run llama3.2Bookmarked and will come back to this. One thing that may be if interest to add is for AMD cards with 20gb of ram. I’d suppose that it would be Qwen 2.5 34B with maybe less strict quant or something.
Also, it may be interesting to look at the AllenAI molmo related models. I’m kind of planning to do this myself but haven’t had time as yet.
Yep. 20GB is basically 24GB, though its too tight for 70B models.
One quirk for 7900 owners is that installing flash attention for long context usage can be a pain. Apparently it is doable now, I need to dig up the link, but it might just be easier to use kobold.cpp rocm with its native flash attention.
As for vision models, that is a whole different can of worms. Exllama does not support this, so you’d need a framework that does.
If you are looking for niche models, check out MiniG (which is a continued pretrain of the already very excellent GLM4-9B): https://huggingface.co/bartowski/miniG-GGUF
Llama.cpp support is recent, though I’m not 100% sure its completely fixed. It should work in Aphrodite as well.
vLLM can only run on linux but it’s my personal favorite because of the speed gain when doing batch inference.
Aphrodite is a fork of vllm. You should check it out!
If you are looking for raw batched speed, especially with some redundant context, I would actually recommend sglang instead. Check out its experimental flags too.
I’d be interested in setting up the highest quality models to run locally, and I don’t have the budget for a GPU with anywhere near enough VRAM, but my main server PC has a 7900x and I could afford to upgrade its RAM - is it possible, and if so how difficult, to get this stuff running on CPU? Inference speed isn’t a sticking point as long as it’s not unusably slow, but I do have access to an OpenAI subscription so there just wouldn’t be much point with lower quality models except as a toy.
CPU inference is, unfortunately, slow, even on my 7800X3D.
The one that might be interesting is deepseek code v2 lite, as its a very fast MoE model. IIRC microsoft also released a Phi MoE thats good for CPU.
Keep an eye out for upcoming bitnet models.
Dont bother upgrading RAM though. You will be bandwidth limited anyway, and it doesn’t make a huge difference.
If your “FIRST STEP” is to choose an OS: Fuck that.
You should never have to change your OS just to use this crap. It’s all written in Python. It should work on every OS available. Your first step is installing the prerequisites.
If you’re using something like Continue for local coding tasks, CodeQwen is awesome, and you’ll generally want a context window of 120k or so because for coding, you want all the code context - or else the LLM starts spitting out repetitious stuff, or can’t ingest all of your context so it’ll rewrite stuff that’s already there.
CodeQwen 1.5 is pretty old at this point, afaik made obsolete by their latest release.
The Qwen models (at least 2.5) are really only good to like 32K, which is still a ton of context. But I’ve been testing Qwen 32B at 64K -90K and even that larger model is… Not great.
32K is generally enough to get the jist of whatever you’re trying to fill in.
Choose OS is very relevant when doing cloud stuff.
Or setting up a home server, which I figured some here would do.
Honestly, I’m just gonna stick to llamafile. I really don’t want to mess around with python. It also causes way more trouble than I anticipate
Llamafile is fine, but it still leaves a lot of performance on the table.
You can setup kobold.cpp with Q8 flash attention without ever having to install pytorch, which is the real headache. It does have a little python launch script, but its super minimal.
You can use the native llama.cpp server for absolutely zero python usage.
Frontendwise; Librechat is pretty cool.
I know this is not the theme of this post, but I wonder if there’s an LLM that doesn’t hallucinate when asked to summarize information of a group of documents. I tried Gpt4all for simple queries like finding out which documents mentioned a certain phrase. It often gave me filenames that didn’t actually exist. Hallucinating contents is one thing but making up data source is just horrible.
That’s absolutely on topic, check out https://huggingface.co/spaces/vectara/Hallucination-evaluation-leaderboard
Command R is built for this if you have the vram to swing it, otherwise GLM4 (or MiniG as linked below) is great. The later, unfortunately, doesn’t work with TabbyAPI, so you have to use something like Kobold.cpp.
You also have to use very low (basically zero) temperature and be careful with other sampling settings, and watch your context length.
There are more sophisticated RAG setups some of these UIs (like open Web UI) integrate, and sometimes you’ll need to host an embeddings model alongside the llm for that to work.