Update README.md

README.md

@@ -232,15 +232,21 @@ DRY
 - https://www.reddit.com/r/KoboldAI/comments/1e49vpt/dry_sampler_questionsthat_im_sure_most_of_us_are/
 - https://www.reddit.com/r/KoboldAI/comments/1eo4r6q/dry_settings_questions/
 
-Samplers : https://gist.github.com/kalomaze/4473f3f975ff5e5fade06e632498f73e
+Samplers:  
 
-Creative Writing -> https://www.reddit.com/r/LocalLLaMA/comments/1c36ieb/comparing_sampling_techniques_for_creative/
+https://gist.github.com/kalomaze/4473f3f975ff5e5fade06e632498f73e
 
-General Parameters => https://arxiv.org/html/2408.13586v1
+Creative Writing :
 
-Benchmarking-and-Guiding-Adaptive-Sampling-Decoding https://github.com/ZhouYuxuanYX/Benchmarking-and-Guiding-Adaptive-Sampling-Decoding-for-LLMs
+https://www.reddit.com/r/LocalLLaMA/comments/1c36ieb/comparing_sampling_techniques_for_creative/
 
-Depending on the AI/LLM "apps" you are using, additional reference material for parameters / samplers may also exist.
+General Parameters:
+
+https://arxiv.org/html/2408.13586v1
+
+Benchmarking-and-Guiding-Adaptive-Sampling-Decoding 
+
+https://github.com/ZhouYuxuanYX/Benchmarking-and-Guiding-Adaptive-Sampling-Decoding-for-LLMs
 
 LLAMACPP-SERVER EXE:
 
@@ -248,6 +254,10 @@ https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md
 
 I have also added notes too in the sections below as well.
 
+OTHER:
+
+Depending on the AI/LLM "apps" you are using, additional reference material for parameters / samplers may also exist.
+
 ---
 
 <h2>Class 1, 2, 3 and 4 model critical notes:</h2>
@@ -316,7 +326,7 @@ Generally it is recommended to run the highest quant(s) you can on your machine
 
 The smaller the size of model, the greater the contrast between the smallest quant and largest quant in terms of operation, quality, nuance and general overall function.
 
-There is an exception to this , see "Neo Imatrix" below.
+There is an exception to this , see "Neo Imatrix" below and "all quants" (cpu only operation).
 
 IMATRIX:
 
@@ -356,6 +366,32 @@ The Imatrix process has NO effect on Q8 or F16 quants.
 
 F16 is full precision, just in GGUF format.
 
+CPU ONLY CONSIDERATIONS:
+
+This section DOES NOT apply to most "Macs" because of the difference in O/S Memory, Vram and motherboard VS other frameworks)
+
+Running quants on CPU will be a lot slower than running them on a video card(s). 
+
+In this special case however it may be preferred to run AS SMALL a quant as possible for token per second generation reasons.
+
+On a top, high end (and relatively new) CPU expect token per second speeds to be 1/4 (or less) a standard middle of the road video card.
+
+Older machines/cpus will be a lot slower - but models will STILL run on these as long as you have enough ram.
+
+Here are some rough comparisons:
+
+On my video card (Nvidia 16GB 4060TI) I get 160-190 tokens per second with 1B LLama 3.2 Instruct, CPU speeds are 50-60 token per second.
+
+On my much older machine (8 years old)(2 core), token per second speed (same 1B model) is in the 10ish token per second (CPU).
+
+Roughly 8B-12B models are limit for CPU only operation (in terms of "usable" tokens/second) - at the moment. 
+
+This is changing as new cpus come out, designed for AI usage.
+
+ARM QUANTS:
+
+These are new quants that are specifically for computers/devices that can run "ARM" quants. If you try to run these on a "non arm" machine/device, the token per second will be VERY SLOW.
+
 <B>NEO Imatrix Quants / Neo Imatrix X Quants</B>
 
 NEO Imatrix quants are specialized and specifically "themed" datasets used to slightly alter the weights in a model. All Imatrix datasets do this to some degree or another, however NEO Imatrix datasets