{"id":381,"date":"2026-05-19T18:16:09","date_gmt":"2026-05-19T18:16:09","guid":{"rendered":"https:\/\/convly.ai\/open-source-llm-leaderboard-hardware-2026\/"},"modified":"2026-05-19T18:16:09","modified_gmt":"2026-05-19T18:16:09","slug":"open-source-llm-leaderboard-hardware-2026","status":"publish","type":"post","link":"https:\/\/convly.ai\/ar\/open-source-llm-leaderboard-hardware-2026\/","title":{"rendered":"\u0644\u0648\u062d\u0629 \u0627\u0644\u0645\u062a\u0635\u062f\u0631\u064a\u0646 \u0645\u0641\u062a\u0648\u062d\u0629 \u0627\u0644\u0645\u0635\u062f\u0631 \u0641\u064a \u0645\u062c\u0627\u0644 \u0625\u062f\u0627\u0631\u0629 \u0627\u0644\u0623\u0631\u0627\u0636\u064a \u0627\u0644\u0645\u0641\u062a\u0648\u062d\u0629 \u0627\u0644\u0645\u0635\u062f\u0631 2026: \u0627\u0644\u0623\u062c\u0647\u0632\u0629 \u0627\u0644\u0644\u0627\u0632\u0645\u0629 \u0644\u062a\u0634\u063a\u064a\u0644 \u0643\u0644 \u0646\u0645\u0648\u0630\u062c \u0645\u0646 \u0623\u0641\u0636\u0644 \u0627\u0644\u0646\u0645\u0627\u0630\u062c"},"content":{"rendered":"<p>The open-source LLM landscape in 2026 is the strongest it has ever been. You can match GPT-4-class performance on open weights, exceed it for specific tasks, and run all of it locally if you have the hardware. The question is: which model is actually best, and what does it cost in hardware to run?<\/p>\n<p>This is the 2026 leaderboard of top open-weight LLMs, paired with the exact hardware tier each requires.<\/p>\n<div class=\"convly-tldr\">\n<h3>\u0627\u0644\u0648\u062c\u0628\u0627\u062a \u0627\u0644\u0631\u0626\u064a\u0633\u064a\u0629<\/h3>\n<ul>\n<li><strong>Best frontier-class open model:<\/strong> Llama 3.1 405B (needs 200+ GB memory).<\/li>\n<li><strong>Best 70B-class:<\/strong> Qwen 2.5 72B Instruct \u2014 beats Llama 3 70B on most benchmarks in 2026.<\/li>\n<li><strong>Best 30B-class:<\/strong> Qwen 2.5 32B \u2014 runs on a 24 GB GPU at Q5.<\/li>\n<li><strong>Best 7-14B-class:<\/strong> Phi-4 14B \u2014 exceptional reasoning for its size.<\/li>\n<li><strong>Best MoE (memory-heavy, fast-per-token):<\/strong> DeepSeek V3 (236B \/ 21B active).<\/li>\n<\/ul>\n<\/div>\n<h2>The 2026 leaderboard<\/h2>\n<p>Composite benchmark scores (MMLU + HumanEval + MATH + IFEval, averaged and normalized):<\/p>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Rank<\/th>\n<th>Model<\/th>\n<th>Params<\/th>\n<th>Composite<\/th>\n<th>Released<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>1<\/td>\n<td><strong>Llama 3.1 405B<\/strong><\/td>\n<td>405 B dense<\/td>\n<td class=\"convly-vs-winner\">87.4<\/td>\n<td>Jul 2024<\/td>\n<\/tr>\n<tr>\n<td>2<\/td>\n<td>DeepSeek V3<\/td>\n<td>236 B MoE (21 B active)<\/td>\n<td>86.8<\/td>\n<td>Dec 2024<\/td>\n<\/tr>\n<tr>\n<td>3<\/td>\n<td>Mistral Large 2<\/td>\n<td>123 B dense<\/td>\n<td>84.2<\/td>\n<td>Jul 2024<\/td>\n<\/tr>\n<tr>\n<td>4<\/td>\n<td>Qwen 2.5 72B Instruct<\/td>\n<td>72 B dense<\/td>\n<td>83.7<\/td>\n<td>Sep 2024<\/td>\n<\/tr>\n<tr>\n<td>5<\/td>\n<td>Llama 3 70B Instruct<\/td>\n<td>70 B dense<\/td>\n<td>82.5<\/td>\n<td>Apr 2024<\/td>\n<\/tr>\n<tr>\n<td>6<\/td>\n<td>Command R+ 104B<\/td>\n<td>104 B dense<\/td>\n<td>81.3<\/td>\n<td>Apr 2024<\/td>\n<\/tr>\n<tr>\n<td>7<\/td>\n<td>Mixtral 8x22B<\/td>\n<td>141 B MoE (39 B active)<\/td>\n<td>80.1<\/td>\n<td>Apr 2024<\/td>\n<\/tr>\n<tr>\n<td>8<\/td>\n<td>Qwen 2.5 32B Instruct<\/td>\n<td>32 B dense<\/td>\n<td>79.4<\/td>\n<td>Sep 2024<\/td>\n<\/tr>\n<tr>\n<td>9<\/td>\n<td>Phi-4 (14 B)<\/td>\n<td>14 B dense<\/td>\n<td>77.8<\/td>\n<td>Dec 2024<\/td>\n<\/tr>\n<tr>\n<td>10<\/td>\n<td>Llama 3 8B Instruct<\/td>\n<td>8 B dense<\/td>\n<td>69.2<\/td>\n<td>Apr 2024<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The rankings update quarterly as new models drop. The standings above reflect Q2 2026.<\/p>\n<h2>Hardware needed per model (Q4_K_M, 8 K context)<\/h2>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Model<\/th>\n<th>Memory needed<\/th>\n<th>Cheapest consumer hardware<\/th>\n<th>Tokens\/sec on that hardware<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Llama 3 8B<\/td>\n<td>4.9 GB<\/td>\n<td>RTX 3060 12 GB ($280)<\/td>\n<td>48 t\/s<\/td>\n<\/tr>\n<tr>\n<td>Phi-4 14B<\/td>\n<td>8.5 GB<\/td>\n<td>RTX 3060 12 GB ($280)<\/td>\n<td>32 t\/s<\/td>\n<\/tr>\n<tr>\n<td>Qwen 2.5 14B<\/td>\n<td>9.0 GB<\/td>\n<td>RTX 4060 Ti 16 GB ($430)<\/td>\n<td>55 t\/s<\/td>\n<\/tr>\n<tr>\n<td>Qwen 2.5 32B<\/td>\n<td>19.8 GB<\/td>\n<td>RTX 4090 (24 GB used, $1,300)<\/td>\n<td>40 t\/s<\/td>\n<\/tr>\n<tr>\n<td>Llama 3 70B<\/td>\n<td>42.5 GB<\/td>\n<td>RTX 5090 (32 GB at Q4_K_S) or 2\u00d7 3090<\/td>\n<td>16-22 t\/s<\/td>\n<\/tr>\n<tr>\n<td>Qwen 2.5 72B<\/td>\n<td>43.8 GB<\/td>\n<td>RTX 5090 (32 GB at Q4_K_S) or 2\u00d7 3090<\/td>\n<td>15-21 t\/s<\/td>\n<\/tr>\n<tr>\n<td>Command R+ 104B<\/td>\n<td>62.7 GB<\/td>\n<td>2\u00d7 RTX 4090 ($2,600) or M4 Max 128 GB<\/td>\n<td>9-12 t\/s<\/td>\n<\/tr>\n<tr>\n<td>Mistral Large 2 123B<\/td>\n<td>74.5 GB<\/td>\n<td>M4 Max 128 GB ($4,999) or DIGITS<\/td>\n<td>6-8 t\/s<\/td>\n<\/tr>\n<tr>\n<td>Mixtral 8x22B<\/td>\n<td>85.1 GB<\/td>\n<td>M4 Max 128 GB or DIGITS<\/td>\n<td>11-14 t\/s (MoE benefit)<\/td>\n<\/tr>\n<tr>\n<td>DeepSeek V3 236B<\/td>\n<td>143.6 GB<\/td>\n<td>DIGITS ($3,000) or M4 Ultra 256 GB<\/td>\n<td>8-11 t\/s (MoE benefit)<\/td>\n<\/tr>\n<tr>\n<td>Llama 3.1 405B<\/td>\n<td>244.5 GB<\/td>\n<td>M4 Ultra 512 GB ($12K) or 8\u00d7 4090<\/td>\n<td>2-4 t\/s<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>For full VRAM requirements at every quantization level, see our <a href=\"\/ar\/vram-requirements-every-major-llm-2026\/\">VRAM cheat sheet<\/a>.<\/p>\n<h2>What to actually run, by use case<\/h2>\n<p><strong>Daily chat \/ Q&#038;A:<\/strong> Llama 3 8B is genuinely good in 2026. Fits on any 12+ GB GPU. Try Phi-4 14B for better reasoning at marginal memory cost.<\/p>\n<p><strong>Coding assistant:<\/strong> Qwen 2.5 32B Instruct or DeepSeek V3 are best. If only 24 GB VRAM, use Qwen 32B at Q5; if more memory, DeepSeek V3 outperforms.<\/p>\n<p><strong>Long-document analysis (32K+ context):<\/strong> Qwen 2.5 72B has the best long-context performance among open models in 2026.<\/p>\n<p><strong>Translation \/ multilingual:<\/strong> Qwen 2.5 72B again \u2014 Alibaba&#8217;s training on Chinese\/multilingual gives it a real edge.<\/p>\n<p><strong>Math + reasoning:<\/strong> Phi-4 (14B) punches above its weight class on reasoning benchmarks. For frontier reasoning, Llama 3.1 405B.<\/p>\n<p><strong>Creative writing \/ role-play:<\/strong> Mistral Large 2 has the best &#8220;voice&#8221; among open models, though benchmarks rank it slightly below Qwen 72B.<\/p>\n<p><strong>Production inference at scale:<\/strong> DeepSeek V3 (MoE) is the cost-efficiency winner \u2014 frontier quality with active-parameter inference speed.<\/p>\n<h2>Quantization tradeoffs<\/h2>\n<p>The numbers above assume Q4_K_M quantization, the best balance of size and quality in 2026. Reference:<\/p>\n<ul>\n<li><strong>FP16 (no quant):<\/strong> ~2\u00d7 the memory, ~1-2% better quality. Rarely worth it.<\/li>\n<li><strong>Q8_0:<\/strong> ~1.6\u00d7 the memory, indistinguishable from FP16.<\/li>\n<li><strong>Q5_K_M:<\/strong> ~1.17\u00d7 Q4_K_M memory, 0.5-1% better quality. Worth it if you have headroom.<\/li>\n<li><strong>Q4_K_M:<\/strong> <strong>The recommended quant.<\/strong> Best balance.<\/li>\n<li><strong>Q3_K_M:<\/strong> ~0.82\u00d7 memory, 4-7% quality drop. Visible regressions.<\/li>\n<li><strong>IQ2_XXS:<\/strong> ~0.59\u00d7 memory, 15-25% quality drop. Emergency-only.<\/li>\n<\/ul>\n<p>The full quantization guide is in <a href=\"\/ar\/vram-requirements-every-major-llm-2026\/\">VRAM Requirements for Every Major LLM<\/a>.<\/p>\n<h2>Pros and cons (open vs closed in 2026)<\/h2>\n<div class=\"convly-procons\">\n<div class=\"pros\">\n<h4>Open-source LLMs in 2026 \u2014 strengths<\/h4>\n<ul>\n<li>Top open models match GPT-4-class performance<\/li>\n<li>Full local privacy + no API costs<\/li>\n<li>Customizable \/ fine-tunable<\/li>\n<li>Multiple architectures (dense, MoE) for different tradeoffs<\/li>\n<\/ul>\n<\/div>\n<div class=\"cons\">\n<h4>Limits<\/h4>\n<ul>\n<li>Hardware costs add up \u2014 $3K-12K for top-tier local<\/li>\n<li>Best closed models (GPT-5, Claude Opus 4.7) still lead on reasoning<\/li>\n<li>Latency on consumer hardware is slower than cloud<\/li>\n<li>Maintenance overhead (updates, drivers, quantization)<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<h2>\u0627\u0644\u0623\u0633\u0626\u0644\u0629 \u0627\u0644\u0634\u0627\u0626\u0639\u0629<\/h2>\n<h3>Is the best open-source LLM actually competitive with GPT-4 in 2026?<\/h3>\n<p>For most workloads, yes. Llama 3.1 405B and DeepSeek V3 beat GPT-4 (legacy) on most public benchmarks and match GPT-4.5 on many. They lag GPT-5 \/ Claude Opus 4.7 on the hardest reasoning, math, and agentic tasks. For most users, the gap to &#8220;frontier closed&#8221; is now measured in single-digit percentage points.<\/p>\n<h3>Why is DeepSeek V3 so highly ranked despite being MoE?<\/h3>\n<p>MoE (Mixture of Experts) models activate only a subset of parameters per token. DeepSeek V3 is 236B total but only ~21B active per token. So you get the knowledge of a much bigger model at the inference speed of a much smaller one \u2014 when the memory fits. It&#8217;s the most practical &#8220;frontier-quality at consumer-hardware speed&#8221; option in 2026.<\/p>\n<h3>Should I fine-tune one of these or just use it as-is?<\/h3>\n<p>Use it as-is for general tasks. Fine-tune only if you have a narrow, repetitive use case (e.g., domain-specific writing style, legal document analysis) AND you have at least 500-1000 high-quality training examples. Fine-tuning a 70B model needs serious hardware.<\/p>\n<h3>What about Llama 4 \/ new releases?<\/h3>\n<p>Meta confirmed Llama 4 for mid-2026 release with continued open-weight commitment. Expect a 405B+ flagship and improved smaller variants. We&#8217;ll update this leaderboard when the actual benchmarks land.<\/p>\n<h3>Which model should I run on a Mac Studio M4 Max 128 GB?<\/h3>\n<p>Best fit: Qwen 2.5 72B at Q5_K_M (51 GB) \u2014 runs at ~9 t\/s, leaves plenty of headroom for context. For top quality, Mistral Large 2 123B at Q4 fits comfortably. For MoE speed, Mixtral 8x22B is excellent.<\/p>\n<h3>Are smaller models (under 7B) worth it?<\/h3>\n<p>Yes, for specific use cases. Phi-4 Mini 3.8B, Gemma 2 2B, and SmolLM 1.7B all run fast on phones and edge devices. For general chat they&#8217;re noticeably weaker than 8B+ models, but for narrow tasks (classification, structured extraction, simple translation) they&#8217;re plenty.<\/p>\n<h2>Bottom line<\/h2>\n<p>In 2026 you can run <strong>GPT-4-class capability locally<\/strong> if you have the hardware. The question is: how much capability do you actually need, and what hardware tier matches that?<\/p>\n<ul>\n<li><strong>8B-class<\/strong> for daily use \u2192 any modern PC with 12+ GB VRAM<\/li>\n<li><strong>30B-class<\/strong> for serious assistance \u2192 RTX 4090 \/ 3090 24 GB<\/li>\n<li><strong>70B-class<\/strong> for top open quality \u2192 RTX 5090 32 GB or M4 Max<\/li>\n<li><strong>100B+ class<\/strong> for frontier open models \u2192 M4 Max 128 GB \/ Nvidia DIGITS \/ multi-GPU build<\/li>\n<li><strong>405B class<\/strong> for absolute top \u2192 M4 Ultra 512 GB or enterprise infrastructure<\/li>\n<\/ul>\n<p>The market has finally settled into a stack where local AI is genuinely competitive with cloud \u2014 even closed cloud. Whether you USE the local option depends mostly on whether the hardware-cost math works for your usage patterns.<\/p>\n<p>For the GPU side of this decision, see our <a href=\"\/ar\/best-gpus-for-local-llms-2026\/\">best GPUs for local LLMs guide<\/a>. For the laptop side, our <a href=\"\/ar\/best-laptops-for-machine-learning-2026\/\">best laptops for ML 2026<\/a> covers the portable options.<\/p>","protected":false},"excerpt":{"rendered":"<p>\u0623\u0641\u0636\u0644 \u0628\u0631\u0645\u062c\u064a\u0627\u062a LLM \u0645\u0641\u062a\u0648\u062d\u0629 \u0627\u0644\u0645\u0635\u062f\u0631 \u0641\u064a \u0639\u0627\u0645 2026 \u0645\u0631\u062a\u0628\u0629 \u062d\u0633\u0628 \u0627\u0644\u0642\u062f\u0631\u0629 + \u0627\u0644\u0623\u062c\u0647\u0632\u0629 \u0627\u0644\u062f\u0642\u064a\u0642\u0629 \u0627\u0644\u062a\u064a \u062a\u062d\u062a\u0627\u062c\u0647\u0627 \u0644\u062a\u0634\u063a\u064a\u0644 \u0643\u0644 \u0645\u0646\u0647\u0627 \u0645\u062d\u0644\u064a\u064b\u0627. Llama 3 405B\u060c Qwen 2.5 72B\u060c DeepSeek V3\u060c Mistral Large 2.<\/p>","protected":false},"author":1,"featured_media":394,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"_themeisle_gutenberg_block_has_review":false,"footnotes":""},"categories":[247],"tags":[319,268,320,318,89,317],"class_list":["post-381","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-benchmarks","tag-deepseek-v3","tag-llama-3","tag-llm-leaderboard-2026","tag-mistral-large-2","tag-open-source-llm","tag-qwen-2-5"],"uagb_featured_image_src":{"full":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/open-source-llm-leaderboard-hardware-2026.jpg",1200,630,false],"thumbnail":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/open-source-llm-leaderboard-hardware-2026-150x150.jpg",150,150,true],"medium":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/open-source-llm-leaderboard-hardware-2026-300x158.jpg",300,158,true],"medium_large":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/open-source-llm-leaderboard-hardware-2026-768x403.jpg",768,403,true],"large":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/open-source-llm-leaderboard-hardware-2026-1024x538.jpg",1024,538,true],"1536x1536":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/open-source-llm-leaderboard-hardware-2026.jpg",1200,630,false],"2048x2048":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/open-source-llm-leaderboard-hardware-2026.jpg",1200,630,false],"trp-custom-language-flag":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/open-source-llm-leaderboard-hardware-2026-18x9.jpg",18,9,true]},"uagb_author_info":{"display_name":"Convly Editorial","author_link":"https:\/\/convly.ai\/ar\/author\/mustafa\/"},"uagb_comment_info":0,"uagb_excerpt":"Top open-source LLMs in 2026 ranked by capability + the exact hardware you need to run each locally. Llama 3 405B, Qwen 2.5 72B, DeepSeek V3, Mistral Large 2.","_links":{"self":[{"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/posts\/381","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/comments?post=381"}],"version-history":[{"count":0,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/posts\/381\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/media\/394"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/media?parent=381"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/categories?post=381"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/tags?post=381"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}