{"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":"2026-06-10T05:05:19","modified_gmt":"2026-06-10T05:05:19","slug":"open-source-llm-leaderboard-hardware-2026","status":"publish","type":"post","link":"https:\/\/convly.ai\/pt\/open-source-llm-leaderboard-hardware-2026\/","title":{"rendered":"Open-Source LLM Leaderboard 2026: Hardware Needed to Run Each Top Model"},"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>Principais conclus\u00f5es<\/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<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_84 counter-flat ez-toc-counter ez-toc-container-direction\">\n<label for=\"ez-toc-cssicon-toggle-item-6a38af1e5f1fd\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Alternar<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #000000;color:#000000\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #000000;color:#000000\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a38af1e5f1fd\"  aria-label=\"Alternar\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/convly.ai\/pt\/open-source-llm-leaderboard-hardware-2026\/#The_2026_leaderboard\" >The 2026 leaderboard<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/pt\/open-source-llm-leaderboard-hardware-2026\/#Hardware_needed_per_model_Q4_K_M_8_K_context\" >Hardware needed per model (Q4_K_M, 8 K context)<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/pt\/open-source-llm-leaderboard-hardware-2026\/#What_to_actually_run_by_use_case\" >What to actually run, by use case<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/pt\/open-source-llm-leaderboard-hardware-2026\/#Quantization_tradeoffs\" >Quantization tradeoffs<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/pt\/open-source-llm-leaderboard-hardware-2026\/#Pros_and_cons_open_vs_closed_in_2026\" >Pros and cons (open vs closed in 2026)<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/pt\/open-source-llm-leaderboard-hardware-2026\/#The_software_lever_your_inference_engine_changes_the_answer\" >The software lever: your inference engine changes the answer<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/convly.ai\/pt\/open-source-llm-leaderboard-hardware-2026\/#FAQ\" >Perguntas frequentes<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/convly.ai\/pt\/open-source-llm-leaderboard-hardware-2026\/#Bottom_line\" >Conclus\u00e3o<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/convly.ai\/pt\/open-source-llm-leaderboard-hardware-2026\/#Related_articles\" >Artigos relacionados<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"The_2026_leaderboard\"><\/span>The 2026 leaderboard<span class=\"ez-toc-section-end\"><\/span><\/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>Modelo<\/th>\n<th>Params<\/th>\n<th>Composite<\/th>\n<th>Lan\u00e7ado<\/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><span class=\"ez-toc-section\" id=\"Hardware_needed_per_model_Q4_K_M_8_K_context\"><\/span>Hardware needed per model (Q4_K_M, 8 K context)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Modelo<\/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=\"\/pt\/vram-requirements-every-major-llm-2026\/\">VRAM cheat sheet<\/a>.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"What_to_actually_run_by_use_case\"><\/span>What to actually run, by use case<span class=\"ez-toc-section-end\"><\/span><\/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><span class=\"ez-toc-section\" id=\"Quantization_tradeoffs\"><\/span>Quantization tradeoffs<span class=\"ez-toc-section-end\"><\/span><\/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=\"\/pt\/vram-requirements-every-major-llm-2026\/\">VRAM Requirements for Every Major LLM<\/a>.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Pros_and_cons_open_vs_closed_in_2026\"><\/span>Pros and cons (open vs closed in 2026)<span class=\"ez-toc-section-end\"><\/span><\/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>Limita\u00e7\u00f5es<\/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<p><!--ai-enriched--><\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_software_lever_your_inference_engine_changes_the_answer\"><\/span>The software lever: your inference engine changes the answer<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The leaderboard above assumes you fit a model entirely in VRAM and run it. In practice, the <strong>inference engine<\/strong> you choose can swing real-world throughput by an order of magnitude on the <em>mesmo<\/em> hardware, and one technique can let a model run on a GPU that the table says is far too small. Picking hardware without picking the runtime is half a decision.<\/p>\n<p>Two camps matter for self-hosters. <strong>vLLM<\/strong> (and similar throughput engines like SGLang) are built for concurrency: their continuous-batching scheduler keeps the GPU saturated, so a single card serving many simultaneous requests can deliver several times the aggregate tokens per second of a naive setup. If you are building an app, an internal API, or anything multi-user, this is the camp to be in. <strong>llama.cpp<\/strong> (and the front-ends built on it, Ollama and LM Studio) optimizes for a single user and maximum flexibility: it runs on almost anything, handles GGUF quants, and \u2014 crucially \u2014 can spill parts of a model to system RAM. On Apple Silicon, the MLX runtime fills the same single-user role and squeezes the most out of unified memory.<\/p>\n<p>That spill ability is what makes the biggest models reachable. Mixture-of-experts models such as DeepSeek V3 carry a huge total parameter count but activate only a small slice per token. llama.cpp&#8217;s <strong>expert-offload<\/strong> flag (<code>--n-cpu-moe<\/code>) keeps the always-active layers on the GPU and pushes the rarely-touched experts into RAM. The upshot: a 24 GB card paired with a lot of fast system memory can <em>run<\/em> a frontier MoE model the VRAM table says it has no business running.<\/p>\n<p>The honest caveat is speed. Offloading trades capacity for latency. Depending on the quant level and your memory bandwidth, expect anywhere from low single-digit tokens per second on aggressive setups to the mid-teens \u2014 firmly in the &#8220;technically runs&#8221; zone, not the &#8220;snappy chat&#8221; zone. The lever is real, but it is a way to access a model you otherwise couldn&#8217;t, not a free upgrade.<\/p>\n<ul>\n<li><strong>Building for multiple users?<\/strong> Choose vLLM or SGLang and size VRAM to fit the model fully.<\/li>\n<li><strong>Single user, want the biggest model on modest hardware?<\/strong> Use llama.cpp with MoE offload and pour your budget into RAM and memory bandwidth, not just the GPU.<\/li>\n<li><strong>On a Mac?<\/strong> Prefer MLX or Ollama; unified memory already does most of the &#8220;offload&#8221; job for you.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"FAQ\"><\/span>Perguntas frequentes<span class=\"ez-toc-section-end\"><\/span><\/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<h3>Is one big GPU or two smaller GPUs better for running these models?<\/h3>\n<p>For pure inference, one card with enough VRAM to hold the model is simpler and avoids the overhead of splitting layers across devices. Two cards make sense when the goal is more total VRAM than any single affordable GPU offers \u2014 for example pairing two 24 GB cards to host a model that won&#8217;t fit in one. The trade-offs are real: a second GPU adds power draw, heat, PCIe-bandwidth bottlenecks between cards, and more finicky configuration. If a single card can fit your target model at a quant you&#8217;re happy with, buy the single card.<\/p>\n<h3>How much does electricity cost to run a local LLM 24\/7?<\/h3>\n<p>Idle and light-use power is modest, but a high-end GPU under sustained load can pull a few hundred watts, and that adds up if the machine is always on. The practical move is to keep the rig asleep or the model unloaded when idle, and only spin up under real demand \u2014 most local runtimes load and unload models on request. For occasional personal use the running cost is minor; for a model serving traffic around the clock, factor electricity into your total cost of ownership alongside the hardware price.<\/p>\n<h3>Is it even worth running these models locally when the hosted APIs are so cheap?<\/h3>\n<p>It depends on why you&#8217;re self-hosting. If your only goal is the lowest cost per token, the hosted APIs for these same open models are hard to beat and require zero hardware. Local hosting wins when you need data to never leave your machine, want guaranteed availability with no rate limits or per-token billing, or are doing high-volume batch work where owned hardware amortizes. For most casual users, the API is the rational choice; for privacy-driven, offline, or heavy-throughput use cases, local pays off.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Bottom_line\"><\/span>Conclus\u00e3o<span class=\"ez-toc-section-end\"><\/span><\/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=\"\/pt\/best-gpus-for-local-llms-2026\/\">guia das melhores GPUs para LLMs locais<\/a>. For the laptop side, our <a href=\"\/pt\/best-laptops-for-machine-learning-2026\/\">best laptops for ML 2026<\/a> covers the portable options.<\/p>\n<p><!--related-block--><\/p>\n<div class=\"convly-related\">\n<h2><span class=\"ez-toc-section\" id=\"Related_articles\"><\/span>Artigos relacionados<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><a href=\"https:\/\/convly.ai\/pt\/vram-requirements-every-major-llm-2026\/\">VRAM Requirements for Every Major LLM in 2026 (Quantization Cheat Sheet)<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/pt\/how-to-run-llama-3-locally-on-snapdragon-8-gen-4\/\">How to Run Llama 3 Locally on Snapdragon 8 Gen 4 (Step-by-Step, 2026)<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/pt\/claude-5-new-ai-models-june-2026\/\">Existe um Claude 5? Claude Fable 5 e todos os principais modelos de IA de junho de 2026<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>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.<\/p>","protected":false},"author":1,"featured_media":394,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"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":""}},"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"],"_links":{"self":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/posts\/381","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/comments?post=381"}],"version-history":[{"count":2,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/posts\/381\/revisions"}],"predecessor-version":[{"id":994,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/posts\/381\/revisions\/994"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/media\/394"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/media?parent=381"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/categories?post=381"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/tags?post=381"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}