{"id":7054889,"date":"2026-09-02T16:31:33","date_gmt":"2026-09-02T16:31:33","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/what-do-gguf-q4ks-a3b-mean\/"},"modified":"2026-09-02T16:31:33","modified_gmt":"2026-09-02T16:31:33","slug":"what-do-gguf-q4ks-a3b-mean","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=7054889","title":{"rendered":"What do gguf, q4ks, A3B mean?"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div id=\"article-start\">\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img fetchpriority=\"high\" decoding=\"async\" width=\"688\" height=\"676\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-18.png\" alt=\"Capabilities of Qwen 3.8 27B\" class=\"wp-image-257287\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-18.png 688w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-18-300x295.png 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-18-150x147.png 150w\" sizes=\"(max-width: 688px) 100vw, 688px\"\/><\/figure>\n<\/div>\n<p class=\"wp-block-paragraph\">If you have ever tried downloading a local LLM, you have\u00a0probably seen\u00a0model names that look like this:\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><code>Qwen3.8-27B-A3B-It-2507-gguf-q2ks-mixed-AutoRound<\/code><\/p>\n<p class=\"wp-block-paragraph\">At first, it looks like meaningless technical shorthand.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">It\u00a0isn\u2019t!\u00a0<\/p>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"2560\" height=\"611\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image2-2-scaled.png\" alt=\"Qwen3.5 naming decoded\" class=\"wp-image-257288\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image2-2-scaled.png 2560w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image2-2-300x72.png 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image2-2-768x183.png 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image2-2-1536x366.png 1536w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image2-2-2048x489.png 2048w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image2-2-150x36.png 150w\" sizes=\"auto, (max-width: 2560px) 100vw, 2560px\"\/><\/figure>\n<\/div>\n<p class=\"wp-block-paragraph\">Every part of that name tells you something about the model:\u00a0<em>how large it is, how it is built, how much of it is used at a time, how its weights are stored<\/em>, and <em>what format the file uses<\/em>.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">Once you understand those pieces, choosing a local model becomes <em>much easier<\/em>.\u00a0<\/p>\n<h2 id=\"h-1-7b-14b-35b-how-large-is-the-model\" class=\"wp-block-heading\">1. 7B, 14B, 35B\u2026\u00a0How Large Is the Model?<\/h2>\n<p class=\"wp-block-paragraph\">The first number you usually see is the model\u2019s\u00a0<strong>parameter count<\/strong>.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">The\u00a0<strong>B<\/strong>\u00a0means billion.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">So:\u00a0<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>7B<\/strong>\u00a0= 7 billion parameters\u00a0<\/li>\n<li><strong>14B<\/strong>\u00a0= 14 billion parameters\u00a0<\/li>\n<li><strong>35B<\/strong>\u00a0= 35 billion parameters\u00a0<\/li>\n<li><strong>70B<\/strong>\u00a0= 70 billion parameters\u00a0<\/li>\n<\/ul>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1408\" height=\"768\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-24.png\" alt=\"LLM Parameters\" class=\"wp-image-257311\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-24.png 1408w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-24-300x164.png 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-24-768x419.png 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-24-150x82.png 150w\" sizes=\"auto, (max-width: 1408px) 100vw, 1408px\"\/><\/figure>\n<\/div>\n<p class=\"wp-block-paragraph\">Parameters are the learned values that make up the model.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">For local AI, parameter count matters because a larger model\u00a0generally requires\u00a0more memory to run.\u00a0<\/p>\n<div style=\"margin:24px 0;padding:16px 20px;border-left:4px solid #6366f1;border-radius:8px;background:linear-gradient(135deg,#f5f7ff 0%,#eef2ff 100%);color:#334155;font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,Arial,sans-serif;font-size:15px;line-height:1.6;box-shadow:0 2px 8px rgba(0,0,0,0.04);\">\n<p>Note<\/p>\n<p>Proprietary models like Gemini 3 Pro, Claude Opus 5 etc. can have parameter counts in trillions.<\/p>\n<\/div>\n<p class=\"wp-block-paragraph\">But there is an important complication.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">A model with\u00a0<strong>35B parameters<\/strong>\u00a0does not necessarily use all 35 billion every time it generates a token.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">That brings us to\u00a0<strong>MoE<\/strong>\u00a0models.\u00a0<\/p>\n<h2 id=\"h-2-moe-does-the-model-use-everything-at-once\" class=\"wp-block-heading\">2.\u00a0MoE: Does the Model Use Everything at Once?<\/h2>\n<p class=\"wp-block-paragraph\">There are two broad types of models\u00a0you\u2019ll\u00a0encounter:\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><strong>Dense models<\/strong>\u00a0and\u00a0<a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2024\/12\/mixture-of-experts-models\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Mixture-of-Experts (MoE)<\/strong><\/a><strong>\u00a0models<\/strong>.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">A dense model uses\u00a0essentially its\u00a0entire parameter set for each token.\u00a0So,\u00a0a\u00a0<strong>35B dense model<\/strong>\u00a0uses\u00a0roughly all\u00a035B parameters during inference.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">An\u00a0MoE\u00a0model works differently.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">It\u00a0contains\u00a0a much larger pool of parameters, divided into different\u00a0<strong>experts<\/strong>. A routing mechanism decides which experts should be used for a particular token.\u00a0<\/p>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"990\" height=\"816\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-19.png\" alt=\"What is Mixture of Experts (MoE)?\" class=\"wp-image-257300\" style=\"aspect-ratio:1.2132427613910768;width:608px;height:auto\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-19.png 990w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-19-300x247.png 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-19-768x633.png 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-19-150x124.png 150w\" sizes=\"auto, (max-width: 990px) 100vw, 990px\"\/><\/figure>\n<\/div>\n<p class=\"wp-block-paragraph\">This means an\u00a0MoE\u00a0model can have a large total parameter count without using\u00a0all\u00a0those parameters at once.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">And that is where the next part of the name comes in.\u00a0<\/p>\n<h2 id=\"h-3-a3b-how-many-parameters-are-active\" class=\"wp-block-heading\">3. A3B: How Many Parameters Are Active?<\/h2>\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1597\" height=\"450\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-20-e1788348762708.png\" alt=\"\" class=\"wp-image-257307\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-20-e1788348762708.png 1597w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-20-e1788348762708-300x85.png 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-20-e1788348762708-768x216.png 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-20-e1788348762708-1536x433.png 1536w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-20-e1788348762708-150x42.png 150w\" sizes=\"auto, (max-width: 1597px) 100vw, 1597px\"\/><\/figure>\n<p class=\"wp-block-paragraph\">You might see a model called:\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><strong>35B-A3B<\/strong>\u00a0<\/p>\n<p class=\"wp-block-paragraph\">The first number still means:\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><strong>35B = 35 billion total parameters<\/strong>\u00a0<\/p>\n<p class=\"wp-block-paragraph\">The\u00a0<strong>A3B<\/strong>\u00a0tells you\u00a0approximately how\u00a0many parameters are\u00a0<strong>active for each token<\/strong>.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">So:\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><strong>35B-A3B<\/strong>\u00a0<\/p>\n<p class=\"wp-block-paragraph\">means roughly:\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><strong>35B total parameters \u2192 3B active parameters per token<\/strong>\u00a0<\/p>\n<p class=\"wp-block-paragraph\">The\u00a0<strong>A<\/strong>\u00a0refers to the activated parameter count.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">This is why an\u00a0MoE\u00a0model can have a large total parameter count without requiring the same amount of computation as a dense model of the same size.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">For example:\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><strong>35B dense<\/strong>\u00a0<\/p>\n<p class=\"wp-block-paragraph\">\u2192 35B parameters active\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><strong>35B-A3B\u00a0MoE<\/strong>\u00a0<\/p>\n<p class=\"wp-block-paragraph\">\u2192 35B parameters available\u00a0<\/p>\n<p class=\"wp-block-paragraph\">\u2192 ~3B active for each token\u00a0<\/p>\n<p class=\"wp-block-paragraph\">The model still has 35B parameters. A3B does\u00a0<strong>not<\/strong>\u00a0mean the model is a 3B model.\u00a0<\/p>\n<h2 id=\"h-4-base-vs-instruct-how-was-the-model-tuned\" class=\"wp-block-heading\">4. Base vs Instruct: How Was the Model Tuned?<\/h2>\n<p class=\"wp-block-paragraph\">You may see two versions of the same model\u00a0labelled\u00a0something like:\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><strong>Qwen3.5-35B-A3B-Base<\/strong>\u00a0<\/p>\n<p class=\"wp-block-paragraph\">and\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><strong>Qwen3.5-35B-A3B-Instruct<\/strong>\u00a0<\/p>\n<p class=\"wp-block-paragraph\">The difference is\u00a0<strong>how the model was trained after its\u00a0initial\u00a0pretraining<\/strong>.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">A\u00a0<strong>base model<\/strong>\u00a0is the raw pretrained version. It has learned patterns from its training data, but it\u00a0hasn\u2019t\u00a0been specifically tuned to behave like a helpful assistant that follows user instructions.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">An\u00a0<strong>instruct model<\/strong>\u00a0has gone through\u00a0additional\u00a0training, commonly called\u00a0<strong>instruction tuning<\/strong>\u00a0or\u00a0<strong>instruction fine-tuning<\/strong>, to make it better at following commands, answering\u00a0questions\u00a0and carrying out tasks in a conversational format.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">So, broadly:\u00a0<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>Base model\u00a0<\/strong>\u2192 learns to predict and generate text\u00a0<\/li>\n<\/ul>\n<ul class=\"wp-block-list\">\n<li><strong>Instruct model\u00a0<\/strong>\u2192 further tuned to follow instructions and interact with users\u00a0<\/li>\n<\/ul>\n<p class=\"wp-block-paragraph\">This means the two versions can have the\u00a0<strong>same architecture, parameter\u00a0count\u00a0and quantization<\/strong>, while behaving quite differently.\u00a0<\/p>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1904\" height=\"594\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-02-at-3.54.04-PM-1.png\" alt=\"Base model vs instruction tuned model\" class=\"wp-image-257302\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-02-at-3.54.04-PM-1.png 1904w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-02-at-3.54.04-PM-1-300x94.png 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-02-at-3.54.04-PM-1-768x240.png 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-02-at-3.54.04-PM-1-1536x479.png 1536w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/Screenshot-2026-09-02-at-3.54.04-PM-1-150x47.png 150w\" sizes=\"auto, (max-width: 1904px) 100vw, 1904px\"\/><\/figure>\n<\/div>\n<p class=\"wp-block-paragraph\">For example:\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><strong>35B-A3B-Base-Q4<\/strong>\u00a0<\/p>\n<p class=\"wp-block-paragraph\">and\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><strong>35B-A3B-Instruct-Q4<\/strong>\u00a0<\/p>\n<p class=\"wp-block-paragraph\">can both be 4-bit versions of the same underlying model, but the\u00a0<strong>Instruct<\/strong>\u00a0version is\u00a0generally the\u00a0one\u00a0you\u2019d\u00a0want for a chatbot or general interactive use.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">The important thing to remember is that\u00a0<strong>Base vs Instruct has nothing to do with model size or quantization<\/strong>.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">It describes\u00a0<strong>how the model was trained to behave<\/strong>.\u00a0<\/p>\n<h2 id=\"h-5-fp16-bf16-how-precisely-are-those-parameters-stored\" class=\"wp-block-heading\">5. FP16, BF16: How Precisely Are Those Parameters Stored?<\/h2>\n<p class=\"wp-block-paragraph\">Now we have\u00a0established\u00a0<strong>how many parameters the model\u00a0contains<\/strong>.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">The next question is:\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><strong>How much information is stored for each parameter?<\/strong>\u00a0<\/p>\n<p class=\"wp-block-paragraph\">This is where\u00a0you\u2019ll\u00a0see terms such as:\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><strong>FP16<\/strong>\u00a0and\u00a0<strong>BF16<\/strong>\u00a0<\/p>\n<p class=\"wp-block-paragraph\">Both use\u00a0<strong>16 bits per value<\/strong>, but they\u00a0represent\u00a0those values differently.\u00a0<\/p>\n<div style=\"overflow-x:auto;margin:24px 0;font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,Arial,sans-serif;\">\n<table style=\"width:100%;border-collapse:separate;border-spacing:0;border:1px solid #d9dee7;border-radius:10px;overflow:hidden;background:#ffffff;font-size:15px;line-height:1.5;box-shadow:0 2px 8px rgba(0,0,0,0.04);\">\n<thead>\n<tr>\n<th style=\"padding:13px 16px;background-color:#f1f5f9;border-bottom:1px solid #d9dee7;border-right:1px solid #d9dee7;text-align:left;font-weight:700;color:#1e293b;\"\/>\n<th style=\"padding:13px 16px;background-color:#f1f5f9;border-bottom:1px solid #d9dee7;border-right:1px solid #d9dee7;text-align:left;font-weight:700;color:#1e293b;\">FP16<\/th>\n<th style=\"padding:13px 16px;background-color:#f1f5f9;border-bottom:1px solid #d9dee7;text-align:left;font-weight:700;color:#1e293b;\">BF16<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;border-right:1px solid #e2e8f0;font-weight:600;color:#334155;\">Bits<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;border-right:1px solid #e2e8f0;color:#475569;\">16-bit<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;color:#475569;\">16-bit<\/td>\n<\/tr>\n<tr>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;border-right:1px solid #e2e8f0;font-weight:600;color:#334155;\">Exponent bits<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;border-right:1px solid #e2e8f0;color:#475569;\">5<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;color:#475569;\"><strong>8<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;border-right:1px solid #e2e8f0;font-weight:600;color:#334155;\">Fraction bits<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;border-right:1px solid #e2e8f0;color:#475569;\">10<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;color:#475569;\">7<\/td>\n<\/tr>\n<tr>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;border-right:1px solid #e2e8f0;font-weight:600;color:#334155;\">Precision<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;border-right:1px solid #e2e8f0;color:#475569;\">Higher<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;color:#475569;\">Lower<\/td>\n<\/tr>\n<tr>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;border-right:1px solid #e2e8f0;font-weight:600;color:#334155;\">Numeric range<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;border-right:1px solid #e2e8f0;color:#475569;\">Smaller<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;color:#475569;\"><strong>Much larger<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"padding:12px 16px;border-right:1px solid #e2e8f0;font-weight:600;color:#334155;\">Common use<\/td>\n<td style=\"padding:12px 16px;border-right:1px solid #e2e8f0;color:#475569;\">Inference\/training<\/td>\n<td style=\"padding:12px 16px;color:#475569;\"><strong>Training + modern AI workloads<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p class=\"wp-block-paragraph\">\u00a0For example, a model with 35 billion parameters stored at 16 bits requires roughly:\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><strong>35B \u00d7 16 bits \u2248 70 GB<\/strong>\u00a0<\/p>\n<p class=\"wp-block-paragraph\">just for its weights.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">That is far too much for many consumer machines.\u00a0So\u00a0people <span style=\"text-decoration: underline;\">compress<\/span> the weights.\u00a0<\/p>\n<h2 id=\"h-6-q4-q5-q6-q8-quantization\" class=\"wp-block-heading\">6. Q4, Q5, Q6, Q8: Quantization<\/h2>\n<p class=\"wp-block-paragraph\">This is where\u00a0<strong>Q4, Q5, Q6 and Q8<\/strong>\u00a0come in.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">These are\u00a0different levels\u00a0of\u00a0<strong>quantization<\/strong>.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">Instead of storing model weights using 16 bits, quantization stores them using fewer bits.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">You will commonly see:\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><strong>Q8<\/strong>\u00a0\u2192\u00a0roughly 8-bit\u00a0<br \/><strong>Q6<\/strong>\u00a0\u2192\u00a0roughly 6-bit\u00a0<br \/><strong>Q5<\/strong>\u00a0\u2192\u00a0roughly 5-bit\u00a0<br \/><strong>Q4<\/strong>\u00a0\u2192\u00a0roughly 4-bit\u00a0<br \/><strong>Q3<\/strong>\u00a0\u2192\u00a0roughly 3-bit\u00a0<\/p>\n<p class=\"wp-block-paragraph\">The lower the number, the smaller the model\u00a0generally becomes.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">That can make an enormous difference.\u00a0<\/p>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1676\" height=\"784\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image6-2.png\" alt=\"Model size vs Model quality\" class=\"wp-image-257291\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image6-2.png 1676w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image6-2-300x140.png 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image6-2-768x359.png 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image6-2-1536x719.png 1536w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image6-2-150x70.png 150w\" sizes=\"auto, (max-width: 1676px) 100vw, 1676px\"\/><\/figure>\n<\/div>\n<p class=\"wp-block-paragraph\">A 35B model at 16-bit precision is roughly:\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><strong>70 GB<\/strong>\u00a0<\/p>\n<p class=\"wp-block-paragraph\">At\u00a0roughly 4\u00a0bits per weight, the same model is closer to:\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><strong>18 GB<\/strong>\u00a0<\/p>\n<p class=\"wp-block-paragraph\">The exact size varies because real quantization schemes have\u00a0additional\u00a0metadata and\u00a0don\u2019t\u00a0always use exactly the nominal number of bits for every value.\u00a0<\/p>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1834\" height=\"1234\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image7.png\" alt=\"Qwen Parameter Distribution by Component\" class=\"wp-image-257292\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image7.png 1834w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image7-300x202.png 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image7-768x517.png 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image7-1536x1033.png 1536w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image7-150x101.png 150w\" sizes=\"auto, (max-width: 1834px) 100vw, 1834px\"\/><\/figure>\n<\/div>\n<p class=\"wp-block-paragraph\">But the principle is simple:\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><strong>Lower-bit quantization reduces memory requirements, usually at the cost of some model quality.<\/strong>\u00a0<\/p>\n<p class=\"wp-block-paragraph\">You may now\u00a0encounter\u00a0something like:\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><strong>Q4_K_M<\/strong><\/p>\n<p class=\"wp-block-paragraph\">You already know what\u00a0<strong>Q4<\/strong>\u00a0means: <em>it is a 4-bit-class quantization.<\/em>\u00a0<\/p>\n<p class=\"wp-block-paragraph\">But what are\u00a0<strong>K<\/strong>\u00a0and\u00a0<strong>M<\/strong>?\u00a0<\/p>\n<p class=\"wp-block-paragraph\">They\u00a0identify\u00a0the\u00a0<strong>specific quantization scheme<\/strong>.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">Modern quantization methods\u00a0don\u2019t\u00a0necessarily store every weight in\u00a0exactly the same\u00a0way. They can use different groupings,\u00a0scales\u00a0and precisions to achieve a better balance between model size and quality.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">That is why\u00a0you\u2019ll\u00a0encounter\u00a0names such as:\u00a0<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>Q4_K_M<\/strong>\u00a0<\/li>\n<li><strong>q2ks<\/strong> (Same thing just with underscores removed)<\/li>\n<li><strong>Q6_K_s<\/strong>\u00a0<\/li>\n<li><strong>Q8_0<\/strong>\u00a0<\/li>\n<\/ul>\n<p class=\"wp-block-paragraph\">You\u00a0don\u2019t\u00a0need to memorize the implementation details of every variant.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">For most users, the useful information is:\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><strong>Q4_K_M <\/strong>= a commonly used 4-bit-class quantization designed to balance size and quality.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">So\u00a0when comparing two versions of the same model,\u00a0<strong>Q4_K_M <\/strong>and<strong> Q6_K<\/strong>,\u00a0you\u2019re\u00a0primarily comparing different quantization levels and schemes.\u00a0<\/p>\n<h2 id=\"h-8-gguf-what-is-the-file\" class=\"wp-block-heading\">8. GGUF: What Is the File?<\/h2>\n<p class=\"wp-block-paragraph\">Finally, you may see:\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><code>GGUF<\/code>\u00a0<\/p>\n<p class=\"wp-block-paragraph\">This is different from everything\u00a0we\u2019ve\u00a0discussed so far.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">GGUF is a\u00a0<strong>model file format<\/strong>.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">It tells the software how the model is packaged and stored.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">That means a filename like:\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><code>Qwen3-30B-A3B-Instruct-2507-q2ks-mixed-AutoRound-gguf<\/code><\/p>\n<p class=\"wp-block-paragraph\">Can be read as:\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><strong>Qwen3<\/strong> \u2192 which model<br \/><strong>30B<\/strong> \u2192 how many parameters exist<br \/><strong>A3B<\/strong> \u2192 how many are active per token<br \/><strong>Instruct<\/strong> \u2192 how it was tuned<br \/><strong>2507<\/strong> \u2192 version\/date identifier<br \/><strong>gguf<\/strong> \u2192 container\/file format<br \/><strong>q2ks<\/strong> \u2192 quantization format<br \/><strong>mixed<\/strong> \u2192 not every layer gets the same bit width<br \/><strong>AutoRound<\/strong> \u2192 quantization algorithm<\/p>\n<p class=\"wp-block-paragraph\">That\u2019s\u00a0the entire \u201calphabet soup.\u201d\u00a0<\/p>\n<h2 id=\"h-putting-it-all-together\" class=\"wp-block-heading\">Putting It All Together<\/h2>\n<p class=\"wp-block-paragraph\">Now take the scary-looking filename again:\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><strong>Qwen3.5-35B-A3B-Q4_K_M-GGUF<\/strong>\u00a0<\/p>\n<p class=\"wp-block-paragraph\">Read it from left to right. It is\u00a0basically a\u00a0<mark style=\"background-color:#7bdcb5\" class=\"has-inline-color\">spec sheet compressed into one line<\/mark>.\u00a0<\/p>\n<h3 id=\"h-the-cheat-sheet\" class=\"wp-block-heading\">The Cheat Sheet<\/h3>\n<div style=\"overflow-x:auto;margin:24px 0;font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,Arial,sans-serif;\">\n<table style=\"width:100%;border-collapse:separate;border-spacing:0;border:1px solid #d9dee7;border-radius:10px;overflow:hidden;background:#ffffff;font-size:15px;line-height:1.5;box-shadow:0 2px 8px rgba(0,0,0,0.04);\">\n<thead>\n<tr>\n<th style=\"padding:13px 16px;background-color:#f1f5f9;border-bottom:1px solid #d9dee7;border-right:1px solid #d9dee7;text-align:left;font-weight:700;color:#1e293b;\">Term<\/th>\n<th style=\"padding:13px 16px;background-color:#f1f5f9;border-bottom:1px solid #d9dee7;text-align:left;font-weight:700;color:#1e293b;\">What it means<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;border-right:1px solid #e2e8f0;font-weight:600;color:#334155;\">7B \/ 35B \/ 70B<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;color:#475569;\">Total number of parameters<\/td>\n<\/tr>\n<tr>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;border-right:1px solid #e2e8f0;font-weight:600;color:#334155;\">MoE<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;color:#475569;\">Mixture-of-Experts architecture<\/td>\n<\/tr>\n<tr>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;border-right:1px solid #e2e8f0;font-weight:600;color:#334155;\">A3B<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;color:#475569;\">Approximate active parameters per token<\/td>\n<\/tr>\n<tr>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;border-right:1px solid #e2e8f0;font-weight:600;color:#334155;\">FP16<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;color:#475569;\">16-bit floating-point representation<\/td>\n<\/tr>\n<tr>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;border-right:1px solid #e2e8f0;font-weight:600;color:#334155;\">BF16<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;color:#475569;\">16-bit bfloat representation<\/td>\n<\/tr>\n<tr>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;border-right:1px solid #e2e8f0;font-weight:600;color:#334155;\">Q4 \/ Q5 \/ Q6 \/ Q8<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;color:#475569;\">Quantization level<\/td>\n<\/tr>\n<tr>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;border-right:1px solid #e2e8f0;font-weight:600;color:#334155;\">Q4_K_M<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;color:#475569;\">Specific quantization scheme<\/td>\n<\/tr>\n<tr>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;border-right:1px solid #e2e8f0;font-weight:600;color:#334155;\">it \/ be<\/td>\n<td style=\"padding:12px 16px;border-bottom:1px solid #e2e8f0;color:#475569;\">Instruction-tuned model or base model<\/td>\n<\/tr>\n<tr>\n<td style=\"padding:12px 16px;border-right:1px solid #e2e8f0;font-weight:600;color:#334155;\">GGUF<\/td>\n<td style=\"padding:12px 16px;color:#475569;\">Model file format<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<h2 id=\"h-frequently-asked-questions\" class=\"wp-block-heading\">Frequently Asked Questions<\/h2>\n<div class=\"schema-faq wp-block-yoast-faq-block\">\n<div class=\"schema-faq-section\" id=\"faq-question-1788342372760\"><strong class=\"schema-faq-question\">Q1. What do 7B, 35B, and 70B mean in LLM model names?<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. They indicate the model\u2019s total number of parameters, with B representing billions.<\/p>\n<\/p><\/div>\n<div class=\"schema-faq-section\" id=\"faq-question-1788342509536\"><strong class=\"schema-faq-question\">Q2. What does A3B mean in an MoE model?<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. <code>A3B<\/code> indicates the approximate number of parameters active for each token during inference.<\/p>\n<\/p><\/div>\n<div class=\"schema-faq-section\" id=\"faq-question-1788342515991\"><strong class=\"schema-faq-question\">Q3. What does Q4_K_M mean in an LLM?<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. <code>Q4_K_M <\/code>is a 4-bit-class quantization scheme designed to balance model size and quality.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"border-top py-3 author-info my-4\">\n<div class=\"author-card d-flex align-items-center\">\n<div class=\"flex-shrink-0 overflow-hidden\">\n                                    <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/author\/vasudeo321\/\" class=\"text-decoration-none active-avatar\"><br \/>\n                                                                       <img decoding=\"async\" src=\"https:\/\/media.licdn.com\/dms\/image\/v2\/D5603AQHzRdQMu0yJig\/profile-displayphoto-crop_800_800\/B56Z_TV.0sGcAM-\/0\/1785957187757?e=1788393600&amp;v=beta&amp;t=G-ZKYbrWVFuj3Serf4JojaTG7UG9jM8h0-x7QClirv0\" width=\"48\" height=\"48\" alt=\"Vasu Deo Sankrityayan\" loading=\"lazy\" class=\"rounded-circle\"\/><br \/>\n                                                                <\/a>\n                                <\/div>\n<\/p><\/div>\n<p>Studying, evaluating, and explaining AI systems for over 6 years.<\/p>\n<p>\u201c\ud835\ude16\ud835\ude2f\ud835\ude24\ud835\ude26 \ud835\ude2e\ud835\ude26\ud835\ude2f \ud835\ude35\ud835\ude36\ud835\ude33\ud835\ude2f\ud835\ude26\ud835\ude25 \ud835\ude35\ud835\ude29\ud835\ude26\ud835\ude2a\ud835\ude33 \ud835\ude35\ud835\ude29\ud835\ude2a\ud835\ude2f\ud835\ude2c\ud835\ude2a\ud835\ude2f\ud835\ude28 \ud835\ude30\ud835\ude37\ud835\ude26\ud835\ude33 \ud835\ude35\ud835\ude30 \ud835\ude2e\ud835\ude22\ud835\ude24\ud835\ude29\ud835\ude2a\ud835\ude2f\ud835\ude26\ud835\ude34 \ud835\ude2a\ud835\ude2f \ud835\ude35\ud835\ude29\ud835\ude26 \ud835\ude29\ud835\ude30\ud835\ude31\ud835\ude26 \ud835\ude35\ud835\ude29\ud835\ude22\ud835\ude35 \ud835\ude35\ud835\ude29\ud835\ude2a\ud835\ude34 \ud835\ude38\ud835\ude30\ud835\ude36\ud835\ude2d\ud835\ude25 \ud835\ude34\ud835\ude26\ud835\ude35 \ud835\ude35\ud835\ude29\ud835\ude26\ud835\ude2e \ud835\ude27\ud835\ude33\ud835\ude26\ud835\ude26. \ud835\ude09\ud835\ude36\ud835\ude35 \ud835\ude35\ud835\ude29\ud835\ude22\ud835\ude35 \ud835\ude30\ud835\ude2f\ud835\ude2d\ud835\ude3a \ud835\ude31\ud835\ude26\ud835\ude33\ud835\ude2e\ud835\ude2a\ud835\ude35\ud835\ude35\ud835\ude26\ud835\ude25 \ud835\ude30\ud835\ude35\ud835\ude29\ud835\ude26\ud835\ude33 \ud835\ude2e\ud835\ude26\ud835\ude2f \ud835\ude38\ud835\ude2a\ud835\ude35\ud835\ude29 \ud835\ude2e\ud835\ude22\ud835\ude24\ud835\ude29\ud835\ude2a\ud835\ude2f\ud835\ude26\ud835\ude34 \ud835\ude35\ud835\ude30 \ud835\ude26\ud835\ude2f\ud835\ude34\ud835\ude2d\ud835\ude22\ud835\ude37\ud835\ude26 \ud835\ude35\ud835\ude29\ud835\ude26\ud835\ude2e.\u201d \u2014 \ud835\udda5\ud835\uddcb\ud835\uddba\ud835\uddc7\ud835\uddc4 \ud835\udda7\ud835\uddbe\ud835\uddcb\ud835\uddbb\ud835\uddbe\ud835\uddcb\ud835\uddcd, \ud835\udda3\ud835\uddce\ud835\uddc7\ud835\uddbe<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<p><h4 class=\"fs-24 text-dark\">Login to continue reading and enjoy expert-curated content.<\/h4>\n<p>                        <button class=\"btn btn-primary mx-auto d-table\" data-bs-toggle=\"modal\" data-bs-target=\"#loginModal\" id=\"readMoreBtn\">Keep Reading for Free<\/button>\n                    <\/p>\n\n","protected":false},"excerpt":{"rendered":"<p>If you have ever tried downloading a local LLM, you have\u00a0probably seen\u00a0model names that look like this:\u00a0 Qwen3.8-27B-A3B-It-2507-gguf-q2ks-mixed-AutoRound At first, it looks like meaningless technical shorthand.\u00a0 It\u00a0isn\u2019t!\u00a0 Every part of that name tells you something about the model:\u00a0how large it is, how it is built, how much of it is used at a time, how [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":7054890,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12033],"tags":[217329,217327,217328],"dealstore":[],"offerexpiration":[],"class_list":["post-7054889","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-a3b","tag-gguf","tag-q4ks"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>What do gguf, q4ks, A3B mean? 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