Naptastic

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====DISCLAIMER====
No guarantees are provided as to the accuracy of this information. I do my best but things move fast and this is hecking hard to figure out.

This page is going to be a mess. I wish there were an easy way to just say ${model} works with ${hardware} using ${software} but
  - It's a really fast-moving target
  - It's really complicated

None of us are thrilled with the state of things, but we have to break compatibility and invalidate documentation constantly just to keep up. **Information older than about a week should be considered obsolete and re-checked.**

=====Anecdotal reports=====
==naptastic==
Using an RTX 3070, I can load Mistral 7b models with ExLlama2 at 4-bit precision and a context of 16k before running out of VRAM. If there's no context, generation runs at 50-60 tokens/second. By the time context reaches 2k, generation is down to 10-20 tokens/second. Making a standard set of prompts for benchmarking speed is on my to-do list. (2023-11-13)

Still on the 3070, I got ~12% increase by installing flash-attention. The error message when it fails to load says that it must be installed from source; I found it was already in pip. With an empty context I get 70~73 tokens/second now.

I have many more observations to put here but most of them aren't going to be relevant until the A770 work is done.

==Your Report Here!==
FIXME

=====What hardware works?=====
Hoffman's Iron Law applies here. You can choose at most two of three desirable properties: cheap, fast, easy. Nvidia is the easiest to use, best-supported, and most expensive. AMD cards are generally less well-supported but still useful, and less expensive. Intel GPUs cost the least but (as of 2023-12 at least) can do the least. CPU-based inference frequently requires no additional investment 

  * CPU
    * Your CPU **must** support (AVX? SSE4.X?) even if you do GPU-only inference.
      * Intel: Newer than (???)
      * AMD: Zen architecture.
      * William Schaub has [[https://blog.longearsfor.life/blog/2023/11/26/building-pytorch-for-systems-without-avx2-instructions/|this blog post]] for people who don't have AVX2 support. He adds: "I ended up doing the same for torchaudio and torchvision because it turns out that the C++ API ended up mismatched from the official packages.  it's the same process except no changes needed in the cmake config."
    * Most users have more CPU-attached DRAM than GPU-attached VRAM, so more models can run via CPU inference.
    * CPU/DRAM inference is orders of magnitude slower than GPU/VRAM inference. (More info needed.)
  * Nvidia newer than FIXME
    * I know one person got a 6GB 1060ti to run a 7b Mistral model
    * Oddly no reports of success on 20XX cards have reached my eyes
    * 3070, 3080, 3090 all work. 3090 seems to be the current (2023-12) favorite.
    * 40XX.
  * AMD newer than FIXME
    * things known not to work: multi-GPU
    * Not all quantization formats are supported.
  * Intel
    * I don't have anything working yet, but it's supposed to be possible to run things on an A770 with Intel's SDK.
  * Partial offload (CPU + GPU)
    * This is possible; there's going to be a section about it below but it's not written yet.

=====How much DRAM/VRAM do I need?=====

**Just to keep things clear** I will use the term **DRAM** to refer to CPU-attached RAM, which is generally DDR4 or DDR5, and **VRAM** to refer to GPU-attached RAM. Being much closer to the GPU itself, and on the same circuit board, GDDR can connect with a much faster, wider bus. [[https://en.wikipedia.org/wiki/High_Bandwidth_Memory|High-bandwidth memory]] goes even faster and wider.

(Or, "what models am I limited to?")
  * If you're using the same GPU for your OS, find out how much that takes and subtract it from your available VRAM.
  * The model itself requires (bits * weights) RAM.
  * Context requires RAM. If a model that "should" fit fails to load with an out of memory error, try reducing the context size (max_seq_length IIRC).
  * Memory requirements work the same way for CPU-attached RAM.

==What fits in 4GB?==
  * Nothing larger than 3b. I have a few; probably worth testing.
==What fits in 6GB?==
  * Anything in the Mistral 7b GPTQ family at 4-bit quantization. Context will be very limited.
==What fits in 8GB?==
  * Mistral 7b GPTQ at 4-bit quantization. I've tested context sizes up to 16k.
==What fits in 12GB?==
==What fits in 16GB?==
==What fits in 20GB?==
Seriously c'mon. If you have this much VRAM, you probably don't need help with the calculations.

=====Can I use my CPU and GPU together?=====
Yes. Please someone write this section.

=====LLM Format Comparison=====
This information is honestly as much for my own use as anyone else's. But here's a tldr:

> "have plenty vram on modern nvidia = use exllamav2+gptq/exl models. for any other scenario, including offloading, its llama.cpp+gguf" -TheLeastMost, 2023-11

Different formats vary in a handful of relevant ways:
  * Loader compatibility: not all loaders support all formats.
  * Disk space: something quantized with more bits (or not quantized) will take more disk space.
  * RAM usage: Ditto
  * Quantization: More bits (bpw) usually gives better results; the current consensus (2023-11) seems to be that 5-6bpw is the best option. Below 5bpw, quality drops sharply; above 6, quality doesn't improve much. (There's a Reddit post with more information; I need to find it.) Not all formats support all quantization options.

So choosing the best format to download for your use case is going to depend on all the same factors: what hardware will be running the model, what you're going to be doing with it, etc. So here are the model formats:

  * HF - //"HF means unquantised, float16 format. You almost never want that.  It's the format that models come in when first released.  It's good for further fine tuning/training, or as a base for quantisation, but isn't really recommended to be used for inference"// -TheBloke
  * AWQ - you almost certainly don't want this. FIXME
  * GPTQ - //"GPTQ is a format designed for GPU inference. If you have a decent GPU with enough VRAM for the model you want to use, it will provide the fastest inference at a decent quality"// -TheBloke
    * GPTQ models are already quantized when they get to you. AFAICT it only supports 4-bit and 8-bit quantization. Right now (2023-11-11) ExLlama won't load 8-bit quantized GPTQ models; it only supports 4-bit models. I don't know if this affects ExLlama2 or _HF variations; I'm not going to waste bandwidth downloading a model I already know won't work with what I have.
  * GGML - Generally not used anymore; GGUF is the new standard. (2023-12)
    * //"GGML is a format that was first designed exclusively for CPU-only inference.  It is very accessible because it doesn't require a GPU at all, only CPU + RAM.  But CPU inference is quite a bit slower than GPU inference.// [...] //Recently the situation has been complicated by the fact that GGML now also supports some GPU acceleration.  It's still not as fast as GPTQ in situations where you have enough VRAM to fully load a model.  But it can enable you to load a model larger than your GPU could support on its own, but still at decent speeds"// -TheBloke
  * GGUF - CPU-based with optional GPU offload. Successor to GGML.
    * .gguf doesn't currently have a published specification, but it has had in the past. It's designed to be future-proof. It also keeps everything in a single file.
    * Quantization can vary within the same model.
    * The filename tells you what you need to know about a model before you download it. The end will be something like:
      * Q♣_0 use the same bpw as they say on the tin for all tensors.
      * Q♣_K_S ("small") uses ♣ bpw for all tensors.
      * Q♣_K_M ("medium") uses more bpw for some specific tensors.
      * Q♣_K_L ("large") uses more bpw for a larger set of tensors.
  * exl2 - GPU only. New hotness. (Why though?) At time of writing (2023-11-11), TheBloke isn't publishing exl2 quants.