{"id":362,"date":"2026-09-09T17:14:19","date_gmt":"2026-09-09T08:14:19","guid":{"rendered":"https:\/\/localmodelwatch.tsuchitsuchi.com\/2026\/09\/09\/nex-agi-announces-nex-n25-pro-agent-model\/"},"modified":"2026-11-03T16:08:19","modified_gmt":"2026-11-03T07:08:19","slug":"nex-agi-announces-nex-n25-pro-agent-model","status":"publish","type":"post","link":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/2026\/09\/09\/nex-agi-announces-nex-n25-pro-agent-model\/","title":{"rendered":"Nex-N2.5-Pro Long-Horizon Agent Model: ~98GB Memory, GGUF Builds"},"content":{"rendered":"<p><!-- lmw:facts --><\/p>\n<h2>At a Glance<\/h2>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Item<\/th>\n<th>Value<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Repository<\/td>\n<td><a href=\"https:\/\/huggingface.co\/nex-agi\/Nex-N2.5-Pro\">nex-agi\/Nex-N2.5-Pro<\/a><\/td>\n<\/tr>\n<tr>\n<td>Family guide<\/td>\n<td><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/model-nex-agi-nex-n2-5-en\/\">Nex-N2.5 guide (2 articles)<\/a><\/td>\n<\/tr>\n<tr>\n<td>Publisher guide<\/td>\n<td><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/publisher-nex-agi-en\/\">Nex-AGI: models and licenses<\/a><\/td>\n<\/tr>\n<tr>\n<td>Published<\/td>\n<td>2026-09-08<\/td>\n<\/tr>\n<tr>\n<td>License<\/td>\n<td>apache-2.0<\/td>\n<\/tr>\n<tr>\n<td>Formats<\/td>\n<td><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/format-safetensors-en\/\">safetensors<\/a><\/td>\n<\/tr>\n<tr>\n<td>Source type<\/td>\n<td>Primary source (the publisher itself)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p><em>Values determined by this site&#8217;s code when the information was collected. Dates are JST.<\/em><\/p>\n<p><!-- \/lmw:facts --><\/p>\n<p><!-- lmw:lab-summary --><\/p>\n<p><strong>What we checked ourselves<\/strong><\/p>\n<ul>\n<li>It needs about 21% fewer tokens than the Qwen3 tokenizer for the same Japanese text, so about 1.26\u00d7 as much Japanese fits in the same context length, and generation is faster per character.<\/li>\n<\/ul>\n<p>Details and conditions are in \u201cOur Own Measurements\u201d below.<\/p>\n<p><!-- \/lmw:lab-summary --><\/p>\n<h2>Overview<\/h2>\n<p>Nex-AGI has announced &#8220;Nex-N2.5&#8221;, a family of agent models designed for real-world long-horizon tasks. Available in three sizes\u2014mini, Pro, and Max\u2014the model weights are open-sourced on platforms like Hugging Face. A key feature is the enhanced continuous action and self-correction capabilities driven by visual feedback.<\/p>\n<h2>Specifications<\/h2>\n<ul>\n<li>License: apache-2.0<\/li>\n<li>Library: transformers<\/li>\n<li>Pipeline task: text-generation<\/li>\n<\/ul>\n<h2>Performance<\/h2>\n<p>Evaluation results, based on model card descriptions and publisher measurements, include both text and multimodal benchmarks. Scores for Nex-N2.5-mini, Nex-N2.5-Pro, and Nex-N2.5-Max are listed alongside major competitor models for comparison.<\/p>\n<h3>Text Benchmarks<\/h3>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<p class=\"lmw-table-hint\" style=\"margin:0 0 4px;font-size:0.85em;opacity:0.7;\">\u2192 Scroll horizontally to see all columns<\/p>\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Benchmark \/ CODING 3<\/th>\n<th>Nex-N2.5-mini \/ CODING 3<\/th>\n<th>Nex-N2.5-Pro \/ CODING 3<\/th>\n<th>Nex-N2.5-Max \/ CODING 3<\/th>\n<th>Claude Opus 5 \/ CODING 3<\/th>\n<th>GPT-5.6 Sol \/ CODING 3<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Terminal-Bench 2.1<\/td>\n<td>73.4<\/td>\n<td>82.7<\/td>\n<td>86.1<\/td>\n<td>89.1<\/td>\n<td>88.8<\/td>\n<\/tr>\n<tr>\n<td>SWE-Bench Pro<\/td>\n<td>43.8<\/td>\n<td>61.2<\/td>\n<td>65.7<\/td>\n<td>79.2<\/td>\n<td>64.6<\/td>\n<\/tr>\n<tr>\n<td>DeepSWE v1.1<\/td>\n<td>36.1<\/td>\n<td>55.8<\/td>\n<td>65.6<\/td>\n<td>73.7<\/td>\n<td>72.7<\/td>\n<\/tr>\n<tr>\n<td>AGENTIC<\/td>\n<td>AGENTIC<\/td>\n<td>AGENTIC<\/td>\n<td>AGENTIC<\/td>\n<td>AGENTIC<\/td>\n<td>AGENTIC<\/td>\n<\/tr>\n<tr>\n<td>AutomationBench v1.0.6 5<\/td>\n<td>32.3<\/td>\n<td>44.2<\/td>\n<td>50.2<\/td>\n<td>50.3<\/td>\n<td>45.8<\/td>\n<\/tr>\n<tr>\n<td>Toolathlon Verified<\/td>\n<td>54.6<\/td>\n<td>68.5<\/td>\n<td>74.7<\/td>\n<td>76.5<\/td>\n<td>74.9<\/td>\n<\/tr>\n<tr>\n<td>GDPval-AA v2<\/td>\n<td>1446<\/td>\n<td>1628<\/td>\n<td>1713<\/td>\n<td>1831<\/td>\n<td>1711<\/td>\n<\/tr>\n<tr>\n<td>Job Bench<\/td>\n<td>28.5<\/td>\n<td>41.4<\/td>\n<td>53.6<\/td>\n<td>65.7<\/td>\n<td>45.4<\/td>\n<\/tr>\n<tr>\n<td>BrowseComp 6<\/td>\n<td>83.4<\/td>\n<td>89.7<\/td>\n<td>92.6<\/td>\n<td>90.8<\/td>\n<td>90.4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<h3>Multimodal Benchmarks<\/h3>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<p class=\"lmw-table-hint\" style=\"margin:0 0 4px;font-size:0.85em;opacity:0.7;\">\u2192 Scroll horizontally to see all columns<\/p>\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Benchmark<\/th>\n<th>Nex-N2.5-mini<\/th>\n<th>Nex-N2.5-Pro<\/th>\n<th>MiniMax-M3<\/th>\n<th>Claude Opus 5<\/th>\n<th>GPT-5.6 Sol<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>OSWorld-Verified 8<\/td>\n<td>71.2<\/td>\n<td>82.2<\/td>\n<td>75.2<\/td>\n<td>83.4<\/td>\n<td>83.2<\/td>\n<\/tr>\n<tr>\n<td>OSWorld-2<\/td>\n<td>30.5<\/td>\n<td>56.4<\/td>\n<td>22.3<\/td>\n<td>68.3<\/td>\n<td>62.7<\/td>\n<\/tr>\n<tr>\n<td>WebTest 8, 9<\/td>\n<td>48.6<\/td>\n<td>52.8<\/td>\n<td>\u2014<\/td>\n<td>\u2014<\/td>\n<td>54.0<\/td>\n<\/tr>\n<tr>\n<td>WebArena-Verified 8<\/td>\n<td>63.4<\/td>\n<td>67.6<\/td>\n<td>\u2014<\/td>\n<td>\u2014<\/td>\n<td>69.7<\/td>\n<\/tr>\n<tr>\n<td>OSWorld-G<\/td>\n<td>82.9<\/td>\n<td>87.4<\/td>\n<td>\u2014<\/td>\n<td>76.8<\/td>\n<td>77.7<\/td>\n<\/tr>\n<tr>\n<td>Vision2Web 7<\/td>\n<td>52.9<\/td>\n<td>68.2<\/td>\n<td>59.0<\/td>\n<td>\u2014<\/td>\n<td>79.8<\/td>\n<\/tr>\n<tr>\n<td>SWE-MM<\/td>\n<td>25.5<\/td>\n<td>38.2<\/td>\n<td>\u2014<\/td>\n<td>59.4<\/td>\n<td>40.2<\/td>\n<\/tr>\n<tr>\n<td>OmniDoc<\/td>\n<td>89.7<\/td>\n<td>92.2<\/td>\n<td>91.6<\/td>\n<td>\u2014<\/td>\n<td>92.9<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>According to evaluations by the publishers, Nex-N2.5-Pro achieves 82.7 on Terminal-Bench 2.1 and 61.2 on SWE-Bench Pro. It also records a score of 82.2 on OSWorld-Verified. While it demonstrates solid performance in terminal operation and agent-related tasks, it trails behind the top-tier Max model and certain comparison models in some metrics. Note that the <code>reasoning_effort<\/code> parameter can be used to control the thinking mode.<\/p>\n<h2>Strengths and Use Cases<\/h2>\n<ul>\n<li>Computer operation, web browsing, and visually grounded agent capabilities (computer use)<\/li>\n<li>Long-horizon tasks and autonomous program execution\/testing<\/li>\n<li>Scientific research, knowledge work, and complex productivity tasks<\/li>\n<li>Function calling and utilization of reasoning parsers<\/li>\n<\/ul>\n<p><!-- lmw:hardware --><\/p>\n<h2>Hardware Requirements<\/h2>\n<p><strong>Estimated requirements (calculated by Local Model Watch)<\/strong> \u2014 396.8B parameters<\/p>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Your VRAM<\/th>\n<th>Quantization<\/th>\n<th>File size<\/th>\n<th>Est. memory needed<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>More than 98GB of VRAM (multi-GPU or CPU offload required)<\/td>\n<td>IQ1_S<\/td>\n<td>81.8GB<\/td>\n<td>98.2GB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p><strong>Inference engine support<\/strong> (architecture name matched against each project&#8217;s own model registry in its source code, checked 2026-11-03): llama.cpp: registered, vLLM: registered, MLX (mlx-lm): registered.<\/p>\n<p><em>Memory estimates add a 20% runtime overhead (KV cache, etc.) to the actual size of the distributed files. Actual usage varies with context length, batch size and inference engine. These figures are computed by this site from file sizes, not published by the model&#8217;s authors. File sizes are measured from the converted build <a href=\"https:\/\/huggingface.co\/bartowski\/Nex-N2.5-Pro-GGUF\">bartowski\/Nex-N2.5-Pro-GGUF<\/a>. Compare with other models in our <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/vram-guide-en\/\">VRAM quick reference<\/a>. What the quantization names mean: <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/glossary-quantization-en\/\">glossary<\/a>.<\/em><\/p>\n<p><!-- \/lmw:hardware --><\/p>\n<p><!-- lmw:runnability --><\/p>\n<h2>Can You Run It Locally?<\/h2>\n<p><strong>Runs in Ollama, LM Studio and llama.cpp via a converted build.<\/strong><\/p>\n<p>The publisher ships safetensors, but <a href=\"https:\/\/huggingface.co\/bartowski\/Nex-N2.5-Pro-GGUF\">bartowski\/Nex-N2.5-Pro-GGUF<\/a> provides a GGUF build you can use.<\/p>\n<p><strong>License \u2014 <code>apache-2.0<\/code> (Commercial use allowed):<\/strong> Permits commercial use, modification and redistribution. Redistribution requires including the license and stating changes; includes a patent grant.<\/p>\n<p><em>Compiled by this site&#8217;s code from the published formats, converted builds we have found, and each engine&#8217;s own model registry. &#8220;Not found&#8221; means we have not seen such a build, not that none exists. License summaries are not legal advice \u2014 check the publisher&#8217;s original terms before relying on them.<\/em><\/p>\n<p><!-- \/lmw:runnability --><\/p>\n<p><!-- lmw:lab --><\/p>\n<h2>Our Own Measurements<\/h2>\n<p>Values we measured ourselves on our server (no GPU) by actually reading and running this model&#8217;s files \u2014 not figures copied from the model card. How we measure, and the results for every model: <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/observations-en\/\">Our Measurements<\/a>.<\/p>\n<h3>Japanese Token Efficiency<\/h3>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Tokenizer<\/th>\n<th>Tokens per 1,000 Japanese characters<\/th>\n<th>Ratio to the same text in English<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>This model<\/strong><\/td>\n<td><strong>546<\/strong><\/td>\n<td><strong>0.99\u00d7<\/strong><\/td>\n<\/tr>\n<tr>\n<td>Qwen3<\/td>\n<td>688<\/td>\n<td>1.26\u00d7<\/td>\n<\/tr>\n<tr>\n<td>Llama 3.2<\/td>\n<td>744<\/td>\n<td>1.36\u00d7<\/td>\n<\/tr>\n<tr>\n<td>Gemma 3<\/td>\n<td>564<\/td>\n<td>1.03\u00d7<\/td>\n<\/tr>\n<tr>\n<td>gpt-oss<\/td>\n<td>795<\/td>\n<td>1.45\u00d7<\/td>\n<\/tr>\n<tr>\n<td>LLM-jp-3<\/td>\n<td>497<\/td>\n<td>0.85\u00d7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>It needs about 21% fewer tokens than the Qwen3 tokenizer for the same Japanese text, so about 1.26\u00d7 as much Japanese fits in the same context length, and generation is faster per character.<\/p>\n<p>Counted with the <code>tokenizer.json<\/code> of <a href=\"https:\/\/huggingface.co\/nex-agi\/Nex-N2.5-Pro\">nex-agi\/Nex-N2.5-Pro<\/a> on a fixed text we wrote ourselves (876 Japanese characters across news, conversation, technical docs, a formal email, travel writing and a recipe) and its English translation. Fewer tokens mean more Japanese fits in the context window.<\/p>\n<h3>Inside the GGUF File<\/h3>\n<p>File: <a href=\"https:\/\/huggingface.co\/bartowski\/Nex-N2.5-Pro-GGUF\/blob\/main\/Nex-N2.5-Pro-Q4_K_M\/Nex-N2.5-Pro-Q4_K_M-00001-of-00007.gguf\">Nex-N2.5-Pro-Q4_K_M\/Nex-N2.5-Pro-Q4_K_M-00001-of-00007.gguf<\/a> (233.61GB, <code>Q4_K_M<\/code>). We read only the header (metadata) of the file, not the weights.<\/p>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Item<\/th>\n<th>Value<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Architecture (as named in the GGUF)<\/td>\n<td><code>qwen35moe<\/code><\/td>\n<\/tr>\n<tr>\n<td>Maximum trained context length<\/td>\n<td>262,144 tokens<\/td>\n<\/tr>\n<tr>\n<td>Layers<\/td>\n<td>60<\/td>\n<\/tr>\n<tr>\n<td>Experts<\/td>\n<td>10 active out of 512<\/td>\n<\/tr>\n<tr>\n<td>Vocabulary size<\/td>\n<td>248,320<\/td>\n<\/tr>\n<tr>\n<td>Chat template<\/td>\n<td>Included (mentions tool calls, has a thinking switch)<\/td>\n<\/tr>\n<tr>\n<td>imatrix<\/td>\n<td>Used (<code>Nex-N2.5-Pro-calibration-v6.txt<\/code>, 573 chunks)<\/td>\n<\/tr>\n<tr>\n<td>Weight types (share of parameters)<\/td>\n<td>Q4_K 78.0% \/ Q6_K 16.8% \/ Q8_0 5.1% \/ F32 0.0%<\/td>\n<\/tr>\n<tr>\n<td>Average bits per weight<\/td>\n<td>5.06 bits<\/td>\n<\/tr>\n<tr>\n<td>Embedding \/ output layer type<\/td>\n<td>Q8_0 \/ Q8_0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>The quant name in the file name describes the file as a whole; in practice layers mix several types. The average bits per weight is the measured file data divided by the number of weights.<\/p>\n<p><!-- \/lmw:lab --><\/p>\n<p><!-- lmw:peers --><\/p>\n<h2>Recent Models in the Same Size Class<\/h2>\n<p><em>Models with <\/em><em>over 40B<\/em><em> parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site&#8217;s estimates; licenses are as stated on the model cards.<\/em><\/p>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<p class=\"lmw-table-hint\" style=\"margin:0 0 4px;font-size:0.85em;opacity:0.7;\">\u2192 Scroll horizontally to see all columns<\/p>\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Model<\/th>\n<th>Parameters<\/th>\n<th>Smallest VRAM tier<\/th>\n<th>License<\/th>\n<th>Our article<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>deepseek-ai\/DeepSeek-V4-Pro-0813<\/td>\n<td>1650.5B<\/td>\n<td>\u2014<\/td>\n<td>mit<\/td>\n<td><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/2026\/09\/26\/deepseek-v4-pro-0813-released\/\">DeepSeek-V4-Pro-0813 Text Generation Model: ~998GB Memory, GGUF Builds<\/a> (2026-09-26)<\/td>\n<\/tr>\n<tr>\n<td>nvidia\/DeepSeek-V4-Pro-0813-nvfp4-DSpark<\/td>\n<td>1650.5B<\/td>\n<td>\u2014<\/td>\n<td>mit<\/td>\n<td><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/2026\/09\/10\/nvidia-releases-deepseek-v4-pro-nvfp4\/\">DeepSeek-V4-Pro-0813-nvfp4-DSpark: ~1005GB Memory<\/a> (2026-09-10)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p><!-- \/lmw:peers --><\/p>\n<h2>How to Get It<\/h2>\n<ul>\n<li>Distribution: Model weights are available on platforms such as Hugging Face and ModelScope.<\/li>\n<li>Supported Engines &amp; Deployment: Deployment via SGLang (using a custom <code>nexagi\/sglang:v0.5.18-nex-patch<\/code> Docker image) is provided, and can be launched from the command line.<\/li>\n<\/ul>\n<p><!-- lmw:variants --><\/p>\n<h2>Quantized and Converted Variants<\/h2>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<p class=\"lmw-table-hint\" style=\"margin:0 0 4px;font-size:0.85em;opacity:0.7;\">\u2192 Scroll horizontally to see all columns<\/p>\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Added<\/th>\n<th>Publisher<\/th>\n<th>Format<\/th>\n<th>Repository<\/th>\n<th>Smallest VRAM tier (build, est. memory)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>2026-09-18 (9 days after publication)<\/td>\n<td>bartowski<\/td>\n<td>GGUF (imatrix)<\/td>\n<td><a href=\"https:\/\/huggingface.co\/bartowski\/Nex-N2.5-Pro-GGUF\">bartowski\/Nex-N2.5-Pro-GGUF<\/a><\/td>\n<td>IQ1_S 98.2GB (does not fit a single consumer GPU)<\/td>\n<\/tr>\n<tr>\n<td>2026-09-18 (9 days after publication)<\/td>\n<td>DevQuasar<\/td>\n<td>GGUF<\/td>\n<td><a href=\"https:\/\/huggingface.co\/DevQuasar\/nex-agi.Nex-N2.5-Pro-GGUF\">DevQuasar\/nex-agi.Nex-N2.5-Pro-GGUF<\/a><\/td>\n<td>Q2_K 161.8GB (does not fit a single consumer GPU)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>File sizes of each build:<\/p>\n<ul>\n<li>Available builds in bartowski\/Nex-N2.5-Pro-GGUF: IQ1_S 81.8GB \/ IQ1_M 91.3GB \/ IQ2_XXS 107.2GB \/ IQ2_XS 113.1GB \/ IQ2_S 119.0GB \/ IQ2_M 132.5GB \/ Q2_K 140.4GB \/ IQ3_XXS 157.9GB \/ IQ3_XS 165.9GB \/ Q3_K_S 167.7GB \/ Q3_K_M 176.9GB \/ Q3_K_L 186.5GB \/ IQ3_M 195.1GB \/ IQ4_XS 204.5GB \/ Q4_0 210.0GB \/ Q4_K_S 217.5GB \/ Q4_1 232.0GB \/ IQ4_NL 232.4GB \/ Q4_K_M 233.6GB \/ Q5_K_S 263.4GB \/ Q5_K_M 284.6GB \/ Q6_K_S 310.3GB \/ Q6_K 325.8GB \/ Q8_0 392.6GB<\/li>\n<li>Available builds in DevQuasar\/nex-agi.Nex-N2.5-Pro-GGUF: Q2_K 134.8GB \/ Q3_K_M 176.5GB \/ Q4_K_M 224.1GB \/ Q5_K_M 262.4GB<\/li>\n<\/ul>\n<p>In addition, 2 converted build(s) from other uploaders exist on Hugging Face; this site lists only builds from the model&#8217;s publisher or established quantization maintainers.<\/p>\n<p><em>This section is appended automatically by Local Model Watch when a converted build of this model appears after publication. Memory figures are estimated from the size of the distributed files. What the quantization names mean: <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/glossary-quantization-en\/\">glossary<\/a>.<\/em><\/p>\n<p><!-- \/lmw:variants --><\/p>\n<p><!-- lmw:related --><\/p>\n<h2>Related Articles<\/h2>\n<ul>\n<li><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/2026\/09\/09\/nex-n25-mini-released\/\">Nex-N2.5-mini Text Generation Model: Our Test Answers, 16GB+ VRAM<\/a><\/li>\n<\/ul>\n<p><!-- \/lmw:related --><\/p>\n<p><!-- lmw:next-steps --><\/p>\n<h2>What to Read Next<\/h2>\n<ul>\n<li><strong>Find models by VRAM<\/strong> (This model does not fit even in 80GB; its smallest build needs about 98GB) \u2192 <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/vram-guide-en\/\">VRAM quick reference, including models over 80GB<\/a><\/li>\n<li><strong>Explore the same model family<\/strong> \u2192 <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/model-nex-agi-nex-n2-5-en\/\">Nex-N2.5 family overview (2 articles, 6 converted builds)<\/a><\/li>\n<li><strong>Engines that run this model<\/strong> \u2192 <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/engine-llama-cpp-en\/\">llama.cpp<\/a> \/ <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/engine-ollama-en\/\">Ollama<\/a> \/ <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/engine-vllm-en\/\">vLLM<\/a><\/li>\n<li><strong>What IQ1_S mean and where to get this model<\/strong> \u2192 <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/format-gguf-en\/\">GGUF format guide and models<\/a> \/ <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/glossary-quantization-en\/\">Quantization and model-format glossary<\/a><\/li>\n<li><strong>Learn about the publisher<\/strong> \u2192 <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/publisher-nex-agi-en\/\">Nex-AGI: models, licenses and articles<\/a><\/li>\n<\/ul>\n<p><!-- \/lmw:next-steps --><\/p>\n<h2>Sources<\/h2>\n<ul>\n<li><a href=\"https:\/\/huggingface.co\/nex-agi\/Nex-N2.5-Pro\">https:\/\/huggingface.co\/nex-agi\/Nex-N2.5-Pro<\/a><\/li>\n<\/ul>\n<p><!-- lmw:updates --><\/p>\n<h2>Update History<\/h2>\n<ul>\n<li>2026-09-18: Added converted builds to \u201cQuantized and Converted Variants\u201d: bartowski\/Nex-N2.5-Pro-GGUF, DevQuasar\/nex-agi.Nex-N2.5-Pro-GGUF<\/li>\n<li>2026-09-18: Updated the hardware requirements table with the actual file sizes of bartowski\/Nex-N2.5-Pro-GGUF.<\/li>\n<li>2026-09-24: Rebuilt the article: Added a &#8220;can you run it locally?&#8221; verdict from the distribution formats, converted builds, engine registries, license terms and measured compression<\/li>\n<li>2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).<\/li>\n<li>2026-09-28: Added our own measurements: Japanese token efficiency, what is inside the GGUF file.<\/li>\n<\/ul>\n<p><!-- \/lmw:updates --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Nex-AGI announces the Nex-N2.5 agent model family. Weights for mini, Pro, and Max are available on Hugging Face, with specs and benchmarks.<\/p>\n","protected":false},"author":1,"featured_media":4135,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[571,316,549,573,169,117],"class_list":["post-362","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-agent-en","tag-hugging-face-en","tag-nex-agi-en","tag-nex-n2-5-pro-en","tag-sglang-en","tag--en"],"lang":"en","translations":{"en":362,"ja":360},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/362","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=362"}],"version-history":[{"count":70,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/362\/revisions"}],"predecessor-version":[{"id":14817,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/362\/revisions\/14817"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4135"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=362"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=362"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=362"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}