{"@context":"https://schema.org","@type":"NewsArticle","generatedAt":"2026-07-23T06:40:50.084Z","headline":"2026年单张24GB GPU可运行的最佳本地LLM对比：Qwen、Gemma、Mistral、DeepSeek","description":"一篇指南对比了六款可在单张24GB GPU上以Q4_K_M量化运行的开放权重模型，包括Qwen3.6、Gemma 4、Mistral Small、gpt-oss-20b和DeepSeek-R1-Distill。每款模型均列出了VRAM占用、许可协议及其最擅长的任务。","url":"https://www.aioga.com/news/cmrskbm1s09wabiwml0zv7kbc/","mainEntityOfPage":"https://www.aioga.com/news/cmrskbm1s09wabiwml0zv7kbc/","datePublished":"2026-07-20T01:18:46.000Z","dateModified":"2026-07-20T01:18:46.000Z","inLanguage":"zh-CN","publisher":{"@type":"NewsMediaOrganization","name":"Aioga","url":"https://www.aioga.com"},"citation":["https://www.marktechpost.com/2026/07/19/best-local-llms-you-can-run-on-a-single-24gb-gpu-in-2026-qwen-gemma-mistral-deepseek-compared","https://aihot.virxact.com/items/cmrskbm1s09wabiwml0zv7kbc"],"canonicalUrl":"https://www.aioga.com/news/cmrskbm1s09wabiwml0zv7kbc/","directAnswer":{"@type":"Answer","text":"Aioga 编辑摘要：一篇指南对比了六款可在单张24GB GPU上以Q4_K_M量化运行的开放权重模型，包括Qwen3.6、Gemma 4、Mistral Small、gpt-oss-20b和DeepSeek-R1-Distill。 Aioga 将其归入「技巧观点」方向，重点关注它对真实使用和行业竞争的影响。","url":"https://www.aioga.com/news/cmrskbm1s09wabiwml0zv7kbc/","dateCreated":"2026-07-20T01:18:46.000Z","author":{"@type":"Organization","@id":"https://www.aioga.com/authors/aioga-editorial/#editorial-team","name":"Aioga Editorial Team","url":"https://www.aioga.com/authors/aioga-editorial/"}},"evidence":[{"@type":"CreativeWork","name":"marktechpost.com source article","url":"https://www.marktechpost.com/2026/07/19/best-local-llms-you-can-run-on-a-single-24gb-gpu-in-2026-qwen-gemma-mistral-deepseek-compared","datePublished":"2026-07-20T01:18:46.000Z","provider":{"@type":"Organization","name":"marktechpost.com","url":"https://www.marktechpost.com/2026/07/19/best-local-llms-you-can-run-on-a-single-24gb-gpu-in-2026-qwen-gemma-mistral-deepseek-compared"}},{"@type":"CreativeWork","name":"AIHot archive record","url":"https://aihot.virxact.com/items/cmrskbm1s09wabiwml0zv7kbc","datePublished":"2026-07-20T01:18:46.000Z","provider":{"@type":"Organization","name":"AIHot","url":"https://aihot.virxact.com/items/cmrskbm1s09wabiwml0zv7kbc"}}],"aggregationSource":"MarkTechPost（RSS）","originalPublisher":{"name":"marktechpost.com","url":"https://www.marktechpost.com/2026/07/19/best-local-llms-you-can-run-on-a-single-24gb-gpu-in-2026-qwen-gemma-mistral-deepseek-compared"},"article":{"id":"cmrskbm1s09wabiwml0zv7kbc","slug":"cmrskbm1s09wabiwml0zv7kbc","url":"https://www.aioga.com/news/cmrskbm1s09wabiwml0zv7kbc/","title":"2026年单张24GB GPU可运行的最佳本地LLM对比：Qwen、Gemma、Mistral、DeepSeek","title_en":"Best Local LLMs You Can Run on a Single 24GB GPU in 2026： Qwen， Gemma， Mistral， DeepSeek Compared","summary":"一篇指南对比了六款可在单张24GB GPU上以Q4_K_M量化运行的开放权重模型，包括Qwen3.6、Gemma 4、Mistral Small、gpt-oss-20b和DeepSeek-R1-Distill。每款模型均列出了VRAM占用、许可协议及其最擅长的任务。","source":"MarkTechPost（RSS）","sourceUrl":"https://www.marktechpost.com/2026/07/19/best-local-llms-you-can-run-on-a-single-24gb-gpu-in-2026-qwen-gemma-mistral-deepseek-compared","aiHotUrl":"https://aihot.virxact.com/items/cmrskbm1s09wabiwml0zv7kbc","publishedAt":"2026-07-20T01:18:46.000Z","category":"技巧观点","score":53,"selected":false,"articleBody":["A single 24GB card is the practical floor for serious local inference. It is enough for genuinely capable models, and small enough to sit on one GPU. An RTX 3090：https://en.wikipedia.org/wiki/GeForce_RTX_30_series or RTX 4090：https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/ both land in this tier. The card you own matters less than the models you pick for it.","The old hobbyist move was to squeeze the biggest 70B quant onto the card. That advice is now outdated. The stronger 2026 strategy uses modern 20B–35B-class models that fit cleanly. These leave room for context, and still respond fast enough for coding, chat, and agents. This guide covers the models that actually fit, why each is worth running, and how 24GB gets spent.","Three things consume memory during inference. Getting the split right decides whether a model fits.","The first is model weights . Their size depends on parameter count and quantization. At Q4_K_M, a common home-inference default, each parameter costs roughly 0.58 bytes. A 32B model therefore needs about 18–20GB in weights alone. Mixtral-style Mixture-of-Experts (MoE) models are the common trap here. Every expert stays resident in VRAM even when only a few route per token. So you size MoE memory by total parameters, never active parameters.","The second is the KV cache , which grows with context length. Longer prompts and longer sessions eat more VRAM. The third is runtime overhead from the serving stack . A safe rule of thumb adds roughly 1–2GB for the KV cache and runtime at short context.","Quantization is the lever that makes 24GB workable. Q4_K_M is the standard balance of quality and footprint. Q5_K_M and Q6_K raise quality at a memory cost. Q8_0 and BF16 are usually too large for 30B-class models on a single 24GB card. The interactive tool below lets you switch quantization and see each model’s fit change live.","Every model below carries a permissive license, either Apache 2.0 or MIT. All of them fit a single 24GB card at Q4_K_M with room for context. They are grouped by the job each one does best.","Alibaba’s Qwen3.6-27B：https://huggingface.co/Qwen/Qwen3.6-27B is the strongest single default for the card. It is a dense 27B model released in April 2026 under Apache 2.0. The release focuses on agentic coding, repository-level reasoning, and frontend workflows. At Q4_K_M it needs roughly 16GB, leaving comfortable headroom for context. Full model cards and the changelog sit in the Qwen3.6 GitHub repository：https://github.com/QwenLM/Qwen3.6.","The Qwen3.6-35B-A3B：https://huggingface.co/Qwen/Qwen3.6-35B-A3B is a Mixture-of-Experts model with 35B total parameters and about 3B active per token. It decodes far faster than a dense 35B model because only a fraction of weights fire each step. The memory footprint still tracks total parameters, so plan for roughly 20GB at Q4_K_M. That makes it a tight but valid fit, best when you want speed for general chat and tool use.","Google DeepMind released Gemma 4：https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/ on April 2, 2026, under Apache 2.0. It was the first Gemma generation to ship with a fully open license, per the DeepMind Gemma page：https://deepmind.google/models/gemma/. The family spans edge models, a 12B unified multimodal model, a 26B MoE with 3.8B active, and a larger dense flagship. The 26B MoE is the natural 24GB pick when you need vision input and 140+ language coverage.","Mistral’s Small line targets the everyday assistant workload with low latency. Mistral Small 3.1：https://mistral.ai/news/mistral-small-3-1/ added multimodal input and a longer context window, and Small 3.2 refined instruction following. It is a 24B dense model under Apache 2.0, and the lightest footprint in this list at roughly 14GB at Q4_K_M. That headroom lets you push context further than the heavier 32B options allow. In 2026 Mistral also shipped larger successors, but those sit above the single-card tier.","OpenAI’s gpt-oss-20b：https://openai.com/index/introducing-gpt-oss/ is an open-weight reasoning model under Apache 2.0. It is a Mixture-of-Experts design with 21B total parameters and 3.6B active per token. It ships in a native MXFP4 4-bit format, loading in roughly 14GB with generous headroom. It is strong at structured reasoning and tool use, and weaker on broad world knowledge.","DeepSeek distilled its R1 reasoning traces into smaller dense models. The DeepSeek-R1-Distill-Qwen-32B：https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B is the 32B variant, built on a Qwen2.5 base and released under an MIT license. At Q4_K_M it uses about 18–20GB, which is the tightest fit in this guide. It exposes its chain of thought through visible reasoning tokens, which is useful for slow, deliberate problems. Ready-made GGUF builds are available from bartowski on Hugging Face：https://huggingface.co/bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF.","The frontier open models of 2026 are large sparse MoE systems. They are quite good in performance and reasoning, but they do not run on one consumer card. GLM-5.2 from Z.ai is a roughly 753B-total MoE. Moonshot’s Kimi K2.7 is around 1T total. DeepSeek shipped V4 as a public preview in April 2026, with a V4-Pro checkpoint near 1.6T parameters. Alibaba’s Qwen3.5-397B and Mistral Large 3 sit in the same server-class range.","Because MoE memory tracks total parameters, all of these need multi-GPU rigs or high-memory unified systems. They are worth knowing as API options. They do not change what runs on a single 24GB card.","Three runtimes cover almost every setup. Ollama：https://ollama.com/ is the simplest path, with automatic quantization selection and an OpenAI-compatible API. llama.cpp：https://github.com/ggml-org/llama.cpp gives fine control over GGUF quantization and offload. vLLM：https://github.com/vllm-project/vllm is the throughput-focused server for heavier concurrent workloads.","Start with one model that matches your main job, not the biggest file you can load. Keep context under control, and let the card do what it does best. Run serious local AI without sending a single token to a cloud endpoint.","Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.","Build an Agentic Event Venue Operator [Full Codes]：https://pxllnk.co/twdn5","Thanks! Our team will contact you soon 🙌"],"articleImages":[{"sourceUrl":"https://www.marktechpost.com/wp-content/uploads/2025/07/a-professional-linkedin-headshot-photogr_0jcmb0R9Sv6nW5XK-zkPHw_uARV5VW1ST6osLNlunoVWg-300x300.png","alt":"","afterParagraph":16,"url":"/media/articles/cmrskbm1s09wabiwml0zv7kbc/84e64b03066de40c.webp"},{"sourceUrl":"https://www.marktechpost.com/wp-content/uploads/2026/07/blog19132-1-8-100x70.png","alt":"Google Releases Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber","afterParagraph":17,"url":"/media/articles/cmrskbm1s09wabiwml0zv7kbc/39896896ab8c8014.webp"}],"mediaStatus":"ok","articleBodyZh":["一张24GB的显卡是进行严肃本地推理的实际起点。它足以运行真正有能力的模型，同时又小到可以放在一块GPU上。RTX 3090：https://en.wikipedia.org/wiki/GeForce_RTX_30_series 或 RTX 4090：https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/ 都属于这一档。你拥有的显卡不如你选择运行的模型重要。","以前爱好者通常的做法是将最大的70B量化模型挤入显卡。这个建议现在已经过时。更强的2026策略是使用现代的20B–35B级模型，这类模型可以整洁地放入显卡。它们留有上下文空间，并且在编程、聊天和代理任务中响应足够快。本指南涵盖了实际可运行的模型、每种模型值得运行的原因，以及24GB显存如何被使用。","推理过程中有三样东西会消耗内存。合理的分配决定了模型是否能适配。","第一是模型权重。其大小取决于参数数量和量化方式。在Q4_K_M量化下，这是一种常用的家庭推理默认设置，每个参数大约占0.58字节。因此，一个32B模型仅权重就需要大约18–20GB。类似Mixtral的专家混合（MoE）模型是这里的常见陷阱。即使每个token只路由几个专家，每个专家也会常驻显存。因此MoE模型的显存需求应按总参数量计算，而非活跃参数量。","第二是KV缓存，它随着上下文长度增长而增加。更长的提示和更长的会话会消耗更多显存。第三是服务堆栈产生的运行时开销。经验法则是在短上下文情况下，KV缓存和运行时大约需要额外1–2GB。","量化是让24GB显存可用的杠杆。Q4_K_M是在质量和显存占用之间的标准平衡。Q5_K_M和Q6_K提升了质量，但会增加显存开销。Q8_0和BF16通常对于单张24GB显卡上的30B级模型来说过大。下面的交互工具允许你切换量化方式，并实时查看每个模型的适配情况。","下面列出的每个模型都使用宽松许可——要么是Apache 2.0，要么是MIT。所有模型在Q4_K_M下都能适配单张24GB显卡，同时保留上下文空间。它们按每个模型最擅长的任务进行分组。","阿里巴巴的 Qwen3.6-27B：https://huggingface.co/Qwen/Qwen3.6-27B 是该显卡最强的单卡默认模型。它是一个密集的 27B 模型，于 2026 年 4 月在 Apache 2.0 下发布。该版本重点关注智能代理编码、仓库级推理和前端工作流。在 Q4_K_M 下大约需要 16GB，为上下文提供了充足的空间。完整的模型卡和更新日志可以在 Qwen3.6 GitHub 仓库中找到：https://github.com/QwenLM/Qwen3.6。","Qwen3.6-35B-A3B：https://huggingface.co/Qwen/Qwen3.6-35B-A3B 是一个专家混合（Mixture-of-Experts）模型，总参数量为 35B，每个标记激活约 3B 参数。它的解码速度远快于密集的 35B 模型，因为每步只有一部分权重被激活。内存占用仍然跟踪总参数量，因此在 Q4_K_M 下大约需要 20GB。这使得它紧凑但可用，最适合用于一般聊天和工具使用时追求速度。","谷歌 DeepMind 发布了 Gemma 4：https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/ 于 2026 年 4 月 2 日，在 Apache 2.0 下发布。它是首个完全以开放许可发布的 Gemma 世代，根据 DeepMind Gemma 页面：https://deepmind.google/models/gemma/。该系列涵盖边缘模型、12B 统一多模态模型、26B MoE（每次激活 3.8B）以及更大的密集旗舰模型。当需要视觉输入和 140+ 语言覆盖时，26B MoE 是自然选择，约需 24GB。","Mistral 的 Small 系列针对日常助手工作负载和低延迟进行优化。Mistral Small 3.1：https://mistral.ai/news/mistral-small-3-1/ 增加了多模态输入和更长的上下文窗口，Small 3.2 改进了指令遵循能力。它是一个密集 24B 模型，在 Apache 2.0 下发布，是此列表中占用内存最小的，大约在 Q4_K_M 下占用 14GB。这个空间让你可以推送更长的上下文，比重量级的 32B 模型更灵活。2026 年，Mistral 也发布了更大型号，但那些属于单卡层级之上。","OpenAI 的 gpt-oss-20b：https://openai.com/index/introducing-gpt-oss/ 是一个开权重推理模型，在 Apache 2.0 下发布。它是一个专家混合设计，总参数为 21B，每个标记激活约 3.6B 参数。它以原生 MXFP4 4-bit 格式发布，加载时大约占用 14GB，有足够冗余空间。它在结构化推理和工具使用方面表现强劲，而在广泛的世界知识方面表现稍弱。","DeepSeek 将其 R1 推理轨迹精炼成更小的密集模型。DeepSeek-R1-Distill-Qwen-32B：https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B 是 32B 版本，基于 Qwen2.5 构建，并在 MIT 许可证下发布。在 Q4_K_M 下，它大约使用 18–20GB，是本指南中最紧凑的配置。它通过可见推理令牌展示思维链，这对处理缓慢、需要深思的问题很有用。bartowski 在 Hugging Face 上提供了现成的 GGUF 构建：https://huggingface.co/bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF。","2026 年的前沿开放模型是大型稀疏 MoE 系统。它们在性能和推理上都相当出色，但不能在单张消费级显卡上运行。Z.ai 的 GLM-5.2 约为 753B 总参数 MoE。Moonshot 的 Kimi K2.7 总参数约为 1T。DeepSeek 于 2026 年 4 月发布 V4 公共预览版，V4-Pro 检查点参数接近 1.6T。阿里巴巴的 Qwen3.5-397B 和 Mistral Large 3 处于同一服务器级别范围。","由于 MoE 内存跟踪总参数，这些模型都需要多 GPU 配置或高内存统一系统。了解它们作为 API 选项是有价值的，但它们不会改变单张 24GB 显卡上的运行情况。","三种运行时涵盖几乎所有配置。Ollama：https://ollama.com/ 是最简单的路径，具有自动量化选择和 OpenAI 兼容 API。llama.cpp：https://github.com/ggml-org/llama.cpp 提供对 GGUF 量化和卸载的精细控制。vLLM：https://github.com/vllm-project/vllm 是面向更大并发工作负载的吞吐量优化服务器。","从一个与您主要任务匹配的模型开始，而不是试图加载最大的文件。保持上下文可控，让显卡发挥最佳性能。进行严肃的本地 AI 运行，而无需将任何令牌发送到云端。","Michal Sutter 是一名数据科学专业人士，持有帕多瓦大学数据科学硕士学位。凭借在统计分析、机器学习和数据工程方面的坚实基础，Michal 擅长将复杂的数据集转化为可执行的洞察。","构建 Agentic Event Venue Operator [完整代码]：https://pxllnk.co/twdn5","谢谢！我们的团队会尽快与您联系 🙌"],"translationStatus":"translated","bodyOrigin":"source-page","editorial":{"summary":"Aioga 编辑摘要：一篇指南对比了六款可在单张24GB GPU上以Q4_K_M量化运行的开放权重模型，包括Qwen3.6、Gemma 4、Mistral Small、gpt-oss-20b和DeepSeek-R1-Distill。 Aioga 将其归入「技巧观点」方向，重点关注它对真实使用和行业竞争的影响。","background":"背景分析：实践类内容的价值在于是否能被复现、是否有明确边界，以及它能否转化为稳定的开发或工作流方法。","viewpoint":"Aioga 判断：这条动态更适合作为行业观察信号，当前信息足以建立线索，但不足以推导长期结论。","implications":"影响分析：对相关团队而言，短期应先核对来源、可用范围和实际成本，再判断是否值得接入或跟进。","nextStep":"后续观察：继续观察示例是否可复现、工具版本变化、社区反馈和实际成本。","evidenceRefs":["title","summary","articleBody"],"confidence":"medium","status":"published","aiGenerated":false,"autoApproved":true,"generatedBy":"rule-safe-fallback","generatedAt":"2026-07-23T06:49:19.120Z","sourceHash":"ea8efdcaffa6cfd7","validation":{"passed":true,"mode":"rule-safe-fallback","checks":["schema","length","source-attribution","no-html"]}},"tags":["技巧观点","MarkTechPost（RSS）"],"translations":{"zh-CN":{"title":"2026年单张24GB GPU可运行的最佳本地LLM对比：Qwen、Gemma、Mistral、DeepSeek","summary":"一篇指南对比了六款可在单张24GB GPU上以Q4_K_M量化运行的开放权重模型，包括Qwen3.6、Gemma 4、Mistral Small、gpt-oss-20b和DeepSeek-R1-Distill。每款模型均列出了VRAM占用、许可协议及其最擅长的任务。","category":"技巧观点","source":"marktechpost.com","aggregationSource":"MarkTechPost（RSS）","pageTitle":"2026年单张24GB GPU可运行的最佳本地LLM对比：Qwen、Gemma、Mistral、DeepSeek - Aioga AI资讯","description":"一篇指南对比了六款可在单张24GB GPU上以Q4_K_M量化运行的开放权重模型，包括Qwen3.6、Gemma 4、Mistral Small、gpt-oss-20b和DeepSeek-R1-Distill。每款模型均列出了VRAM占用、许可协议及其最擅长的任务。","url":"https://www.aioga.com/news/cmrskbm1s09wabiwml0zv7kbc/"},"en":{"title":"Comparison of the Best Local LLMs That Can Run on a Single 24GB GPU in 2026: Qwen, Gemma, Mistral, DeepSeek","summary":"A guide compared six open-weight models that can run with Q4_K_M quantization on a single 24GB GPU, including Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b, and DeepSeek-R1-Distill. For each model, VRAM usage, licensing, and their best-suited tasks are listed.","category":"Insights","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"Comparison of the Best Local LLMs That Can Run on a Single 24GB GPU in 2026: Qwen, Gemma, Mistral, DeepSeek - Aioga AI News","description":"A guide compared six open-weight models that can run with Q4_K_M quantization on a single 24GB GPU, including Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b, and DeepSeek-R1-Distill....","url":"https://www.aioga.com/en/news/cmrskbm1s09wabiwml0zv7kbc/","contentTranslated":true,"sourceHash":"29532ec91467d7b4","translatedAt":"2026-07-23T03:42:13.275Z"},"ja":{"title":"2026年単枚24GB GPUで動作可能な最高のローカルLLM比較：Qwen、Gemma、Mistral、DeepSeek","summary":"あるガイドでは、24GB GPUの単一カード上でQ4_K_M量子化で動作可能な6つのオープンウェイトモデルを比較しています。比較対象には、Qwen3.6、Gemma 4、Mistral Small、gpt-oss-20b、DeepSeek-R1-Distillが含まれます。各モデルについては、VRAM使用量、ライセンス契約、および最も得意とするタスクが記載されています。","category":"ヒントと視点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"2026年単枚24GB GPUで動作可能な最高のローカルLLM比較：Qwen、Gemma、Mistral、DeepSeek - Aioga AIニュース","description":"あるガイドでは、24GB GPUの単一カード上でQ4_K_M量子化で動作可能な6つのオープンウェイトモデルを比較しています。比較対象には、Qwen3.6、Gemma 4、Mistral Small、gpt-oss-20b、DeepSeek-R1-Distillが含まれます。各モデルについては、VRAM使用量、ライセンス契約、および最も得意とするタスクが記載さ...","url":"https://www.aioga.com/ja/news/cmrskbm1s09wabiwml0zv7kbc/","contentTranslated":true,"sourceHash":"29532ec91467d7b4","translatedAt":"2026-07-23T03:42:24.801Z"},"ko":{"title":"2026년 단일 24GB GPU에서 실행할 수 있는 최고의 로컬 LLM 비교: Qwen, Gemma, Mistral, DeepSeek","summary":"한 가이드에서는 단일 24GB GPU에서 Q4_K_M 양자화를 사용하여 실행할 수 있는 여섯 가지 공개 가중치 모델을 비교했으며, 여기에는 Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b, DeepSeek-R1-Distill가 포함됩니다. 각 모델마다 VRAM 사용량, 라이선스 및 가장 적합한 작업이 나열되어 있습니다.","category":"인사이트","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"2026년 단일 24GB GPU에서 실행할 수 있는 최고의 로컬 LLM 비교: Qwen, Gemma, Mistral, DeepSeek - Aioga AI 뉴스","description":"한 가이드에서는 단일 24GB GPU에서 Q4_K_M 양자화를 사용하여 실행할 수 있는 여섯 가지 공개 가중치 모델을 비교했으며, 여기에는 Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b, DeepSeek-R1-Distill가 포함됩니다. 각 모델마다 VRAM 사용량, 라이선스 및 가장 적...","url":"https://www.aioga.com/ko/news/cmrskbm1s09wabiwml0zv7kbc/","contentTranslated":true,"sourceHash":"29532ec91467d7b4","translatedAt":"2026-07-23T03:43:18.125Z"},"es":{"title":"Comparación de los mejores LLM locales que se pueden ejecutar con una GPU de 24 GB en 2026: Qwen, Gemma, Mistral, DeepSeek","summary":"Una guía comparó seis modelos de pesos abiertos que pueden ejecutarse en una sola GPU de 24 GB con cuantización Q4_K_M, incluyendo Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b y DeepSeek-R1-Distill. Para cada modelo se enumeran el uso de VRAM, la licencia y las tareas en las que es más competente.","category":"Ideas","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"Comparación de los mejores LLM locales que se pueden ejecutar con una GPU de 24 GB en 2026: Qwen, Gemma, Mistral, DeepSeek - Aioga Noticias de IA","description":"Una guía comparó seis modelos de pesos abiertos que pueden ejecutarse en una sola GPU de 24 GB con cuantización Q4_K_M, incluyendo Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b y De...","url":"https://www.aioga.com/es/news/cmrskbm1s09wabiwml0zv7kbc/","contentTranslated":true,"sourceHash":"29532ec91467d7b4","translatedAt":"2026-07-23T03:43:06.610Z"},"fr":{"title":"Comparatif des meilleurs LLM locaux pouvant fonctionner sur un GPU unique de 24 Go en 2026 : Qwen, Gemma, Mistral, DeepSeek","summary":"Un guide a comparé six modèles à poids ouverts pouvant fonctionner sur un GPU unique de 24 Go en quantification Q4_K_M, y compris Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b et DeepSeek-R1-Distill. Chaque modèle indique l'utilisation de la VRAM, la licence et les tâches pour lesquelles il est le plus performant.","category":"Analyses","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"Comparatif des meilleurs LLM locaux pouvant fonctionner sur un GPU unique de 24 Go en 2026 : Qwen, Gemma, Mistral, DeepSeek - Aioga Actualités IA","description":"Un guide a comparé six modèles à poids ouverts pouvant fonctionner sur un GPU unique de 24 Go en quantification Q4_K_M, y compris Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b et De...","url":"https://www.aioga.com/fr/news/cmrskbm1s09wabiwml0zv7kbc/","contentTranslated":true,"sourceHash":"29532ec91467d7b4","translatedAt":"2026-07-23T03:44:03.136Z"},"de":{"title":"Vergleich der besten lokal ausführbaren LLMs auf einer einzelnen 24GB GPU im Jahr 2026: Qwen, Gemma, Mistral, DeepSeek","summary":"Ein Leitfaden verglich sechs Open-Weight-Modelle, die auf einer einzelnen 24GB-GPU mit Q4_K_M-Quantisierung laufen können, darunter Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b und DeepSeek-R1-Distill. Für jedes Modell wurden der VRAM-Verbrauch, die Lizenzvereinbarung und die Aufgaben, in denen es am besten ist, aufgeführt.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"Vergleich der besten lokal ausführbaren LLMs auf einer einzelnen 24GB GPU im Jahr 2026: Qwen, Gemma, Mistral, DeepSeek - Aioga KI-News","description":"Ein Leitfaden verglich sechs Open-Weight-Modelle, die auf einer einzelnen 24GB-GPU mit Q4_K_M-Quantisierung laufen können, darunter Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b und...","url":"https://www.aioga.com/de/news/cmrskbm1s09wabiwml0zv7kbc/","contentTranslated":true,"sourceHash":"29532ec91467d7b4","translatedAt":"2026-07-23T03:44:01.391Z"},"pt-BR":{"title":"Comparação dos melhores LLMs locais que podem ser executados em uma GPU de 24GB em 2026: Qwen, Gemma, Mistral, DeepSeek","summary":"Um guia comparou seis modelos de pesos abertos que podem ser executados em uma única GPU de 24 GB com quantização Q4_K_M, incluindo Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b e DeepSeek-R1-Distill. Para cada modelo, foram listados o uso de VRAM, a licença e as tarefas em que se destacam.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"Comparação dos melhores LLMs locais que podem ser executados em uma GPU de 24GB em 2026: Qwen, Gemma, Mistral, DeepSeek - Aioga Notícias de IA","description":"Um guia comparou seis modelos de pesos abertos que podem ser executados em uma única GPU de 24 GB com quantização Q4_K_M, incluindo Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b e D...","url":"https://www.aioga.com/pt-BR/news/cmrskbm1s09wabiwml0zv7kbc/","contentTranslated":true,"sourceHash":"29532ec91467d7b4","translatedAt":"2026-07-23T03:44:50.263Z"},"ru":{"title":"Сравнение лучших локальных LLM, работающих на одной 24GB GPU в 2026 году: Qwen, Gemma, Mistral, DeepSeek","summary":"Одна статья-сравнение рассмотрела шесть моделей с открытыми весами, которые можно запускать на одной видеокарте с 24 ГБ памяти с квантизацией Q4_K_M, включая Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b и DeepSeek-R1-Distill. Для каждой модели указано использование VRAM, лицензионное соглашение и задачи, в которых она наиболее эффективна.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"Сравнение лучших локальных LLM, работающих на одной 24GB GPU в 2026 году: Qwen, Gemma, Mistral, DeepSeek - Aioga Новости ИИ","description":"Одна статья-сравнение рассмотрела шесть моделей с открытыми весами, которые можно запускать на одной видеокарте с 24 ГБ памяти с квантизацией Q4_K_M, включая Qwen3.6, Gemma 4, Mist...","url":"https://www.aioga.com/ru/news/cmrskbm1s09wabiwml0zv7kbc/","contentTranslated":true,"sourceHash":"29532ec91467d7b4","translatedAt":"2026-07-23T03:44:53.080Z"},"ar":{"title":"مقارنة أفضل نماذج LLM المحلية التي يمكن تشغيلها على بطاقة GPU واحدة بسعة 24GB في عام 2026: Qwen، Gemma، Mistral، DeepSeek","summary":"دليل واحد قارن بين ستة نماذج ذات أوزان مفتوحة يمكن تشغيلها بالكمية Q4_K_M على بطاقة رسومات واحدة بسعة 24 جيجابايت، بما في ذلك Qwen3.6 وGemma 4 وMistral Small وgpt-oss-20b وDeepSeek-R1-Distill. تم سرد استخدام VRAM ورخصة كل نموذج والمهمة التي يجيدها كل نموذج.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"مقارنة أفضل نماذج LLM المحلية التي يمكن تشغيلها على بطاقة GPU واحدة بسعة 24GB في عام 2026: Qwen، Gemma، Mistral، DeepSeek - Aioga أخبار الذكاء الاصطناعي","description":"دليل واحد قارن بين ستة نماذج ذات أوزان مفتوحة يمكن تشغيلها بالكمية Q4_K_M على بطاقة رسومات واحدة بسعة 24 جيجابايت، بما في ذلك Qwen3.6 وGemma 4 وMistral Small وgpt-oss-20b وDeepSeek...","url":"https://www.aioga.com/ar/news/cmrskbm1s09wabiwml0zv7kbc/","contentTranslated":true,"sourceHash":"29532ec91467d7b4","translatedAt":"2026-07-23T03:45:38.737Z"},"hi":{"title":"2026 में एकल 24GB GPU पर चलने वाले सर्वश्रेष्ठ स्थानीय LLM की तुलना: Qwen, Gemma, Mistral, DeepSeek","summary":"एक गाइडने छह ओपन-वेट मॉडल की तुलना की जो एक ही 24GB GPU पर Q4_K_M क्वांटाइज़ेशन के साथ चल सकते हैं, जिनमें Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b और DeepSeek-R1-Distill शामिल हैं। प्रत्येक मॉडल के VRAM उपयोग, लाइसेंसिंग और इसकी सबसे अच्छी क्षमताओं वाले कार्य सूचीबद्ध किए गए हैं।","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"2026 में एकल 24GB GPU पर चलने वाले सर्वश्रेष्ठ स्थानीय LLM की तुलना: Qwen, Gemma, Mistral, DeepSeek - Aioga AI समाचार","description":"एक गाइडने छह ओपन-वेट मॉडल की तुलना की जो एक ही 24GB GPU पर Q4_K_M क्वांटाइज़ेशन के साथ चल सकते हैं, जिनमें Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b और DeepSeek-R1-Distill शामिल...","url":"https://www.aioga.com/hi/news/cmrskbm1s09wabiwml0zv7kbc/","contentTranslated":true,"sourceHash":"29532ec91467d7b4","translatedAt":"2026-07-23T03:45:43.749Z"},"it":{"title":"Confronto dei migliori LLM locali eseguibili con una singola GPU da 24 GB nel 2026: Qwen, Gemma, Mistral, DeepSeek","summary":"Una guida ha confrontato sei modelli a peso aperto che possono essere eseguiti con quantizzazione Q4_K_M su una singola GPU da 24 GB, inclusi Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b e DeepSeek-R1-Distill. Per ciascun modello sono stati indicati l'utilizzo della VRAM, la licenza e i compiti in cui eccelle di più.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"Confronto dei migliori LLM locali eseguibili con una singola GPU da 24 GB nel 2026: Qwen, Gemma, Mistral, DeepSeek - Aioga Notizie IA","description":"Una guida ha confrontato sei modelli a peso aperto che possono essere eseguiti con quantizzazione Q4_K_M su una singola GPU da 24 GB, inclusi Qwen3.6, Gemma 4, Mistral Small, gpt-o...","url":"https://www.aioga.com/it/news/cmrskbm1s09wabiwml0zv7kbc/","contentTranslated":true,"sourceHash":"29532ec91467d7b4","translatedAt":"2026-07-23T03:46:26.489Z"},"nl":{"title":"Vergelijking van de beste lokale LLM's die kunnen draaien op een enkele 24GB GPU in 2026: Qwen, Gemma, Mistral, DeepSeek","summary":"Een gids vergelijkte zes open-weights modellen die op een enkele 24GB GPU kunnen draaien met Q4_K_M-quantisatie, waaronder Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b en DeepSeek-R1-Distill. Voor elk model worden het VRAM-gebruik, de licentieovereenkomst en de taken waarin het het beste is, vermeld.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"Vergelijking van de beste lokale LLM's die kunnen draaien op een enkele 24GB GPU in 2026: Qwen, Gemma, Mistral, DeepSeek - Aioga AI-nieuws","description":"Een gids vergelijkte zes open-weights modellen die op een enkele 24GB GPU kunnen draaien met Q4_K_M-quantisatie, waaronder Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b en DeepSeek-...","url":"https://www.aioga.com/nl/news/cmrskbm1s09wabiwml0zv7kbc/","contentTranslated":true,"sourceHash":"29532ec91467d7b4","translatedAt":"2026-07-23T03:46:32.048Z"},"tr":{"title":"2026'da tek 24GB GPU ile çalıştırılabilecek en iyi yerel LLM karşılaştırması: Qwen, Gemma, Mistral, DeepSeek","summary":"Bir rehber, tek bir 24GB GPU'da Q4_K_M miktarına göre çalıştırılabilen altı açık ağırlıklı modeli karşılaştırdı; bunlar arasında Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b ve DeepSeek-R1-Distill bulunuyor. Her model için VRAM kullanımı, lisans anlaşması ve en iyi olduğu görevler listelendi.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"2026'da tek 24GB GPU ile çalıştırılabilecek en iyi yerel LLM karşılaştırması: Qwen, Gemma, Mistral, DeepSeek - Aioga AI Haberleri","description":"Bir rehber, tek bir 24GB GPU'da Q4_K_M miktarına göre çalıştırılabilen altı açık ağırlıklı modeli karşılaştırdı; bunlar arasında Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b ve Dee...","url":"https://www.aioga.com/tr/news/cmrskbm1s09wabiwml0zv7kbc/","contentTranslated":true,"sourceHash":"29532ec91467d7b4","translatedAt":"2026-07-23T03:47:20.064Z"},"vi":{"title":"So sánh các LLM bản địa tốt nhất chạy trên GPU 24GB đơn lẻ vào năm 2026: Qwen, Gemma, Mistral, DeepSeek","summary":"Một hướng dẫn đã so sánh sáu mô hình trọng số mở có thể chạy trên GPU 24GB đơn với lượng lượng tử hóa Q4_K_M, bao gồm Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b và DeepSeek-R1-Distill. Mỗi mô hình đều liệt kê việc sử dụng VRAM, giấy phép và các nhiệm vụ mà nó có khả năng thực hiện tốt nhất.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"So sánh các LLM bản địa tốt nhất chạy trên GPU 24GB đơn lẻ vào năm 2026: Qwen, Gemma, Mistral, DeepSeek - Tin tức AI Aioga","description":"Một hướng dẫn đã so sánh sáu mô hình trọng số mở có thể chạy trên GPU 24GB đơn với lượng lượng tử hóa Q4_K_M, bao gồm Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b và DeepSeek-R1-Di...","url":"https://www.aioga.com/vi/news/cmrskbm1s09wabiwml0zv7kbc/","contentTranslated":true,"sourceHash":"29532ec91467d7b4","translatedAt":"2026-07-23T03:47:23.217Z"},"id":{"title":"Perbandingan LLM lokal terbaik yang dapat dijalankan dengan GPU 24GB tunggal pada tahun 2026: Qwen, Gemma, Mistral, DeepSeek","summary":"Sebuah panduan membandingkan enam model berat terbuka yang dapat dijalankan pada GPU 24GB tunggal dengan kuantisasi Q4_K_M, termasuk Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b, dan DeepSeek-R1-Distill. Setiap model mencantumkan penggunaan VRAM, lisensi, serta tugas yang paling dikuasainya.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"Perbandingan LLM lokal terbaik yang dapat dijalankan dengan GPU 24GB tunggal pada tahun 2026: Qwen, Gemma, Mistral, DeepSeek - Berita AI Aioga","description":"Sebuah panduan membandingkan enam model berat terbuka yang dapat dijalankan pada GPU 24GB tunggal dengan kuantisasi Q4_K_M, termasuk Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b, d...","url":"https://www.aioga.com/id/news/cmrskbm1s09wabiwml0zv7kbc/","contentTranslated":true,"sourceHash":"29532ec91467d7b4","translatedAt":"2026-07-23T03:48:03.649Z"},"th":{"title":"เปรียบเทียบ LLM ท้องถิ่นที่ดีที่สุดที่สามารถรันด้วย GPU 24GB เดี่ยวในปี 2026: Qwen, Gemma, Mistral, DeepSeek","summary":"คู่มือฉบับหนึ่งได้เปรียบเทียบโมเดลน้ำหนักเปิดหกรุ่น ซึ่งสามารถรันด้วยการคูณเชิงควอนตัม Q4_K_M บน GPU ขนาด 24GB ต่อหนึ่งการ์ดได้ รวมถึง Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b และ DeepSeek-R1-Distill แต่ละโมเดลมีการระบุการใช้งาน VRAM, ใบอนุญาต และงานที่ถนัดที่สุด.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"เปรียบเทียบ LLM ท้องถิ่นที่ดีที่สุดที่สามารถรันด้วย GPU 24GB เดี่ยวในปี 2026: Qwen, Gemma, Mistral, DeepSeek - ข่าว AI Aioga","description":"คู่มือฉบับหนึ่งได้เปรียบเทียบโมเดลน้ำหนักเปิดหกรุ่น ซึ่งสามารถรันด้วยการคูณเชิงควอนตัม Q4_K_M บน GPU ขนาด 24GB ต่อหนึ่งการ์ดได้ รวมถึง Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b...","url":"https://www.aioga.com/th/news/cmrskbm1s09wabiwml0zv7kbc/","contentTranslated":true,"sourceHash":"29532ec91467d7b4","translatedAt":"2026-07-23T03:48:16.560Z"},"pl":{"title":"Porównanie najlepszych lokalnych LLM działających na pojedynczym GPU 24 GB w 2026 roku: Qwen, Gemma, Mistral, DeepSeek","summary":"Przewodnik porównuje sześć modeli z otwartym dostępem do wag, które można uruchomić na pojedynczym GPU 24 GB z kwantyzacją Q4_K_M, w tym Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b i DeepSeek-R1-Distill. Dla każdego modelu podano zużycie VRAM, licencję oraz jego najlepiej obsługiwane zadania.","category":"技巧观点","source":"MarkTechPost（RSS）","aggregationSource":"MarkTechPost（RSS）","pageTitle":"Porównanie najlepszych lokalnych LLM działających na pojedynczym GPU 24 GB w 2026 roku: Qwen, Gemma, Mistral, DeepSeek - Aioga Wiadomości AI","description":"Przewodnik porównuje sześć modeli z otwartym dostępem do wag, które można uruchomić na pojedynczym GPU 24 GB z kwantyzacją Q4_K_M, w tym Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20...","url":"https://www.aioga.com/pl/news/cmrskbm1s09wabiwml0zv7kbc/","contentTranslated":true,"sourceHash":"29532ec91467d7b4","translatedAt":"2026-07-23T03:49:03.646Z"}}}}