{"id":2968,"date":"2026-09-23T08:13:59","date_gmt":"2026-09-22T23:13:59","guid":{"rendered":"https:\/\/localmodelwatch.tsuchitsuchi.com\/2026\/09\/23\/ifm-k2-horizon-375b-a23b-nvfp4-released-2\/"},"modified":"2026-11-08T19:08:37","modified_gmt":"2026-11-08T10:08:37","slug":"ifm-k2-horizon-375b-a23b-nvfp4-released-2","status":"publish","type":"post","link":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/2026\/09\/23\/ifm-k2-horizon-375b-a23b-nvfp4-released-2\/","title":{"rendered":"K2-Horizon-375B-A23B-NVFP4 Text Generation Model: ~257GB Memory"},"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\/IFM\/K2-Horizon-375B-A23B-NVFP4\">IFM\/K2-Horizon-375B-A23B-NVFP4<\/a><\/td>\n<\/tr>\n<tr>\n<td>Family guide<\/td>\n<td><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/model-ifm-k2-horizon-en\/\">K2-Horizon guide (3 articles)<\/a><\/td>\n<\/tr>\n<tr>\n<td>Publisher guide<\/td>\n<td><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/publisher-ifm-en\/\">IFM (MBZUAI): models and licenses<\/a><\/td>\n<\/tr>\n<tr>\n<td>Published<\/td>\n<td>2026-09-22<\/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>safetensors<\/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 18% fewer tokens than the Qwen3 tokenizer for the same Japanese text, so about 1.22\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>IFM has released &#8220;K2-Horizon-375B-A23B-NVFP4&#8221;, which is the NVFP4 quantized version of its flagship model &#8220;K2-Horizon-375B-A23B&#8221; built on the MoE (Mixture-of-Experts) architecture. This model reduces memory usage and speeds up inference by quantizing the linear layers (weights and activations) of the routed experts into the NVFP4 format. Meanwhile, the attention mechanism, shared experts, router, the first three dense layers, and lm_head maintain BF16 precision. This model is intended for use on NVIDIA Blackwell generation (B series) and newer GPUs that natively support NVFP4.<\/p>\n<h2>Specifications<\/h2>\n<ul>\n<li>Total parameters: 375B<\/li>\n<li>Activated parameters: 23B<\/li>\n<li>Architecture: MoE<\/li>\n<li>Context length: 512K (524,288 tokens)<\/li>\n<\/ul>\n<h2>Performance<\/h2>\n<h3>NVFP4 vs. BF16<\/h3>\n<p>Regarding the impact of quantization on performance, evaluation results in the model card indicate that the NVFP4 version experiences a very slight performance decrease compared to the original BF16 version. Specifically, the average score drops from 91.7 to 91.2, and GSM8K in the mathematics domain slightly decreases from 96.06 to 95.53. However, this performance drop is extremely minor, and the design compensates for this slight accuracy difference with the benefits of reduced memory footprint and improved inference speed on NVFP4-supported hardware.<\/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>K2-Horizon-375B-A23B<\/th>\n<th>IFEval (Prompt)<\/th>\n<th>GSM8K<\/th>\n<th>MBPP<\/th>\n<th>MMLU-Pro<\/th>\n<th>GPQA-Diamond<\/th>\n<th>BBH (3-shot)<\/th>\n<th>AIME 26 (avg @ 32)<\/th>\n<th>Average<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>BF16<\/td>\n<td>90.02<\/td>\n<td>96.06<\/td>\n<td>97.00<\/td>\n<td>84.22<\/td>\n<td>85.80<\/td>\n<td>94.73<\/td>\n<td>94.38<\/td>\n<td>91.7<\/td>\n<\/tr>\n<tr>\n<td>NVFP4<\/td>\n<td>88.72<\/td>\n<td>95.53<\/td>\n<td>96.60<\/td>\n<td>83.98<\/td>\n<td>85.45<\/td>\n<td>94.26<\/td>\n<td>93.65<\/td>\n<td>91.2<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<h3>Benchmark Results<\/h3>\n<p>Based on measurement results published by the creators for the original model &#8220;K2-Horizon-375B-A23B&#8221;, a table focusing on major comparison targets is shown below.<\/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><\/th>\n<th>K2-Horizon-375B-A23B<\/th>\n<th>Open-weight models \/ Nemotron 3 Ultra<\/th>\n<th>Open-weight models \/ Inkling (xhigh)<\/th>\n<th>Open-weight models \/ MiniMax-M3<\/th>\n<th>Closed models \/ Claude Sonnet5 (max)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td># Params<\/td>\n<td>375B<\/td>\n<td>550B<\/td>\n<td>975B<\/td>\n<td>428B<\/td>\n<td>&#8212;<\/td>\n<\/tr>\n<tr>\n<td># Activated params<\/td>\n<td>23B<\/td>\n<td>55B<\/td>\n<td>41B<\/td>\n<td>23B<\/td>\n<td>&#8212;<\/td>\n<\/tr>\n<tr>\n<td>Architecture<\/td>\n<td>MoE<\/td>\n<td>MoE<\/td>\n<td>MoE<\/td>\n<td>MoE<\/td>\n<td>Closed<\/td>\n<\/tr>\n<tr>\n<td>Agents<\/td>\n<td><\/td>\n<td><\/td>\n<td><\/td>\n<td><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td>GDPVal-AA Real-world professional tasks (Elo)<\/td>\n<td>1,441<\/td>\n<td>1,162<\/td>\n<td>1,234<\/td>\n<td>1,380<\/td>\n<td>1,584<\/td>\n<\/tr>\n<tr>\n<td>tau3-Banking Agentic tool use<\/td>\n<td>34.0<\/td>\n<td>14.2<\/td>\n<td>29.1<\/td>\n<td>15.3<\/td>\n<td>37.3<\/td>\n<\/tr>\n<tr>\n<td>Coding<\/td>\n<td><\/td>\n<td><\/td>\n<td><\/td>\n<td><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td>Terminal-Bench 2.1 Agentic terminal use<\/td>\n<td>70.2<\/td>\n<td>53.9<\/td>\n<td>55.1<\/td>\n<td>65.2<\/td>\n<td>80.5<\/td>\n<\/tr>\n<tr>\n<td>SciCode Scientific coding<\/td>\n<td>42.7<\/td>\n<td>39.9<\/td>\n<td>46.1<\/td>\n<td>45.4<\/td>\n<td>53.6<\/td>\n<\/tr>\n<tr>\n<td>Scientific Reasoning<\/td>\n<td><\/td>\n<td><\/td>\n<td><\/td>\n<td><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td>Humanity&#8217;s Last Exam (without tools) Expert-level reasoning<\/td>\n<td>32.0<\/td>\n<td>28.4<\/td>\n<td>31.9<\/td>\n<td>39.0<\/td>\n<td>41.3<\/td>\n<\/tr>\n<tr>\n<td>GPQA Diamond Graduate-level science QA<\/td>\n<td>87.3<\/td>\n<td>86.7<\/td>\n<td>87.2<\/td>\n<td>92.9<\/td>\n<td>91.1<\/td>\n<\/tr>\n<tr>\n<td>CritPt Frontier physics reasoning<\/td>\n<td>8.6<\/td>\n<td>3.1<\/td>\n<td>5.4<\/td>\n<td>3.7<\/td>\n<td>16.9<\/td>\n<\/tr>\n<tr>\n<td>General<\/td>\n<td><\/td>\n<td><\/td>\n<td><\/td>\n<td><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td>AA-LCR Long-context reasoning<\/td>\n<td>76.0<\/td>\n<td>71.0<\/td>\n<td>73.3<\/td>\n<td>80.3<\/td>\n<td>77.0<\/td>\n<\/tr>\n<tr>\n<td>AA-Omniscience Accuracy Factual accuracy<\/td>\n<td>23.0<\/td>\n<td>23.0<\/td>\n<td>42.0<\/td>\n<td>17.0<\/td>\n<td>40.0<\/td>\n<\/tr>\n<tr>\n<td>AA-Omniscience Non-Hallucination Non-hallucination rate<\/td>\n<td>74.7<\/td>\n<td>70.0<\/td>\n<td>32.0<\/td>\n<td>82.0<\/td>\n<td>61.0<\/td>\n<\/tr>\n<tr>\n<td>Agentic Evaluations<\/td>\n<td><\/td>\n<td><\/td>\n<td><\/td>\n<td><\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td>Toolathlon Verified Agentic tool use<\/td>\n<td>65.3<\/td>\n<td>34.3<\/td>\n<td>45.5<\/td>\n<td>53.7<\/td>\n<td>71.6<\/td>\n<\/tr>\n<tr>\n<td>Automation Bench Public Workflow automation<\/td>\n<td>25.3<\/td>\n<td>8.0<\/td>\n<td>12.8<\/td>\n<td>20.5<\/td>\n<td>34.7<\/td>\n<\/tr>\n<tr>\n<td>Apex-Agents (pass@1) Long-horizon professional workflows<\/td>\n<td>24.8<\/td>\n<td>9.0<\/td>\n<td>19.0<\/td>\n<td>23.8<\/td>\n<td>31.7<\/td>\n<\/tr>\n<tr>\n<td>MCPMark MCP tool use<\/td>\n<td>67.7<\/td>\n<td>45.7<\/td>\n<td>51.2<\/td>\n<td>48.8<\/td>\n<td>65.3<\/td>\n<\/tr>\n<tr>\n<td>BrowseComp Deep web research<\/td>\n<td>72.8<\/td>\n<td>44.4<\/td>\n<td>77.1<\/td>\n<td>83.5<\/td>\n<td>84.7<\/td>\n<\/tr>\n<tr>\n<td>WildClawBench In-the-wild agentic tasks<\/td>\n<td>50.9<\/td>\n<td>34.2<\/td>\n<td>52.3<\/td>\n<td>56.4<\/td>\n<td>&#8212;<\/td>\n<\/tr>\n<tr>\n<td>SWE-Atlas-QnA Repo-level code Q&amp;A (strict)<\/td>\n<td>48.4<\/td>\n<td>&#8212;<\/td>\n<td>25.5<\/td>\n<td>42.3<\/td>\n<td>&#8212;<\/td>\n<\/tr>\n<tr>\n<td>SWE Bench Pro Software engineering (strict)<\/td>\n<td>42.6<\/td>\n<td>38.7<\/td>\n<td>43.1<\/td>\n<td>43.8<\/td>\n<td>&#8212;<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>This table shows that this model achieves a very high standard in agent performance. In particular, it records 1,441 in GDPVal-AA (Elo) under the &#8220;Agents&#8221; category, making it extremely powerful among open-weight MoE models. It scores 70.2 in Terminal-Bench 2.1 in the &#8220;Coding&#8221; field, demonstrating high capability in agentic tasks involving terminal operations. Additionally, it records a high score of 87.3 in GPQA Diamond for &#8220;Scientific Reasoning&#8221;.<\/p>\n<p>On the other hand, it falls behind closed models in certain metrics. For example, in Toolathlon, this model scores 65.3 compared to 71.6 for Claude Sonnet5 (max), suggesting that there is still room for improvement in agent tool utilization capabilities when compared to top-tier closed models. However, it displays performance that surpasses open-weight MoE models up to 2.6 times its size, making it an exceptionally efficient model.<\/p>\n<h2>Strengths and Use Cases<\/h2>\n<p>Based on the characteristics of the original model, this model has specialized capabilities in advanced agent task execution, coding, and long-form reading comprehension. Specifically, it is expected to be utilized in the following applications:<\/p>\n<ul>\n<li><strong>Agent and Tool Use<\/strong>: Demonstrates high performance in terminal operations, agent tasks involving complex workflows, and tool utilization using MCP (Model Context Protocol).<\/li>\n<li><strong>Coding<\/strong>: Suitable for agentic coding tasks involving terminal operations, repository-level code Q&amp;A, and software engineering tasks.<\/li>\n<li><strong>Scientific and Advanced Reasoning<\/strong>: Capable of handling questions requiring doctoral-level scientific knowledge, expert-level reasoning tasks, and mathematical reasoning.<\/li>\n<li><strong>Long Context Processing<\/strong>: Natively supports an extremely long context window of 512K (524,288 tokens), enabling tasks that handle massive amounts of information at once.<\/li>\n<\/ul>\n<p>Additionally, the model is recommended for use with specific sampling parameters such as <code>reasoning_effort=\"high\"<\/code>, temperature of 1.0, and top_p of 0.95. The reasoning process (thinking) is structured to be output as <code>reasoning_content<\/code>, and the final answer as <code>content<\/code>.<\/p>\n<p><!-- lmw:hardware --><\/p>\n<h2>Hardware Requirements<\/h2>\n<p><strong>Estimated requirements (calculated by Local Model Watch)<\/strong><\/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 257GB of VRAM (multi-GPU or CPU offload required)<\/td>\n<td>NVFP4<\/td>\n<td>213.8GB<\/td>\n<td>256.6GB<\/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 2026-11-08): llama.cpp: registered, vLLM: registered, MLX (mlx-lm): not registered. &#8220;Not registered&#8221; means the name is absent from that registry today, not that the model cannot run.<\/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. 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>Not usable in Ollama, LM Studio and llama.cpp yet \u2014 we have found no GGUF build.<\/strong><\/p>\n<p>The publisher ships safetensors only. However, llama.cpp&#8217;s registry does list this architecture, so <strong>conversion to GGUF is possible<\/strong> and the model will run once someone publishes a converted build. Today it can be run with transformers or vLLM, using the memory figures in the table above.<\/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>565<\/strong><\/td>\n<td><strong>1.04\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 18% fewer tokens than the Qwen3 tokenizer for the same Japanese text, so about 1.22\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\/IFM\/K2-Horizon-375B-A23B-NVFP4\">IFM\/K2-Horizon-375B-A23B-NVFP4<\/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<p><!-- \/lmw:lab --><\/p>\n<h2>How to Get It<\/h2>\n<ul>\n<li><strong>Distribution format<\/strong>: safetensors<\/li>\n<li><strong>Supported engines<\/strong>: vLLM, SGLang, Transformers<\/li>\n<\/ul>\n<p>Verified recipes for SGLang and serving methods for vLLM have been published. Note that this model is released under the Apache 2.0 license.<\/p>\n<p><!-- lmw:related --><\/p>\n<h2>Related Articles<\/h2>\n<ul>\n<li><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/2026\/09\/23\/ifm-k2-horizon-32b-nvfp4-released\/\">K2-Horizon-32B-NVFP4: Our Test Answers, 32GB+ VRAM<\/a><\/li>\n<li><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><\/li>\n<li><a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/2026\/10\/05\/qwen-3-8-flash-next-released\/\">Qwen3.8-Flash-Next Multimodal MoE Model: ~402GB Memory<\/a><\/li>\n<li><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><\/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 257GB) \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-ifm-k2-horizon-en\/\">K2-Horizon family overview (3 articles, 1 converted builds)<\/a><\/li>\n<li><strong>Engines that run this model<\/strong> \u2192 <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/engine-vllm-en\/\">vLLM<\/a><\/li>\n<li><strong>What NVFP4 mean and where to get this model<\/strong> \u2192 <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/format-fp4-en\/\">NVFP4 \/ MXFP4 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-ifm-en\/\">IFM (MBZUAI): 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\/IFM\/K2-Horizon-375B-A23B-NVFP4\">https:\/\/huggingface.co\/IFM\/K2-Horizon-375B-A23B-NVFP4<\/a><\/li>\n<\/ul>\n<p><!-- lmw:updates --><\/p>\n<h2>Update History<\/h2>\n<ul>\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.<\/li>\n<\/ul>\n<p><!-- \/lmw:updates --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Explore the NVFP4 quantized version of IFM&#8217;s K2-Horizon-375B-A23B MoE model, designed for NVIDIA Blackwell GPUs with reduced memory usage.<\/p>\n","protected":false},"author":1,"featured_media":4070,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[310],"tags":[2051,2053,165,813,169,1547,592,1555],"class_list":["post-2968","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-new-models","tag-ifm-en","tag-k2-horizon-en","tag-moe-en","tag-nvfp4-en","tag-sglang-en","tag-verified","tag-vllm-en","tag--en"],"lang":"en","translations":{"en":2968,"ja":2966},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/2968","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=2968"}],"version-history":[{"count":60,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/2968\/revisions"}],"predecessor-version":[{"id":15535,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/posts\/2968\/revisions\/15535"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media\/4070"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=2968"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/categories?post=2968"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/tags?post=2968"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}