{"@context":"https://schema.org","@type":"NewsArticle","generatedAt":"2026-09-22T18:00:52.144Z","headline":"Qwen3.8 27B 量化方案基准实测：Q4_K_M 表现稳定，1 位接近随机水平","description":"作者实测 Qwen3.8 27B 各档 Unsloth 量化（GGUF）在 GPQA Diamond、IFBench 和 Terminal-Bench 2.1 上的表现。","url":"https://www.aioga.com/news/cmtt04yw104xuro5wqydtcs81/","mainEntityOfPage":"https://www.aioga.com/news/cmtt04yw104xuro5wqydtcs81/","datePublished":"2026-09-08T18:10:01.000Z","dateModified":"2026-09-08T18:10:01.000Z","inLanguage":"zh-CN","publisher":{"@type":"NewsMediaOrganization","name":"Aioga","url":"https://www.aioga.com"},"citation":["https://quesma.com/blog/qwen38-27b-quantizations-benchmarked","https://aihot.news/items/cmtt04yw104xuro5wqydtcs81"],"canonicalUrl":"https://www.aioga.com/news/cmtt04yw104xuro5wqydtcs81/","directAnswer":{"@type":"Answer","text":"Aioga 编辑摘要：作者实测 Qwen3.8 27B 各档 Unsloth 量化（GGUF）在 GPQA Diamond、IFBench 和 Terminal-Bench 2.1 上的表现。 Aioga 将其归入「行业动态」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/cmtt04yw104xuro5wqydtcs81/","dateCreated":"2026-09-08T18:10:01.000Z","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":"quesma.com source article","url":"https://quesma.com/blog/qwen38-27b-quantizations-benchmarked","datePublished":"2026-09-08T18:10:01.000Z","provider":{"@type":"Organization","name":"quesma.com","url":"https://quesma.com/blog/qwen38-27b-quantizations-benchmarked"}},{"@type":"CreativeWork","name":"AIHot archive record","url":"https://aihot.news/items/cmtt04yw104xuro5wqydtcs81","datePublished":"2026-09-08T18:10:01.000Z","provider":{"@type":"Organization","name":"AIHot","url":"https://aihot.news/items/cmtt04yw104xuro5wqydtcs81"}}],"aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","originalPublisher":{"name":"quesma.com","url":"https://quesma.com/blog/qwen38-27b-quantizations-benchmarked"},"geoDeepAnswer":null,"article":{"id":"cmtt04yw104xuro5wqydtcs81","slug":"cmtt04yw104xuro5wqydtcs81","url":"https://www.aioga.com/news/cmtt04yw104xuro5wqydtcs81/","title":"Qwen3.8 27B 量化方案基准实测：Q4_K_M 表现稳定，1 位接近随机水平","title_en":"","summary":"作者实测 Qwen3.8 27B 各档 Unsloth 量化（GGUF）在 GPQA Diamond、IFBench 和 Terminal-Bench 2.1 上的表现。","source":"Hacker News 热门（buzzing.cc 中文翻译）","sourceUrl":"https://quesma.com/blog/qwen38-27b-quantizations-benchmarked","aiHotUrl":"https://aihot.news/items/cmtt04yw104xuro5wqydtcs81","publishedAt":"2026-09-08T18:10:01.000Z","category":"行业动态","score":58,"selected":false,"articleBody":["How much GPU RAM do you actually need to run Qwen3.8 27B without sacrificing quality?","The full BF16 model weighs 55 GB, putting it beyond most consumer hardware. Yet the 17 GB Q4_K_M matches the full model on a popular agentic coding benchmark, Terminal-Bench 2.1. It fits on a 24 GB card such as RTX 4090, still leaving room for about 64k tokens of context.","Compression eventually hits a cliff. At 1 bit, the model performs around random chance on GPQA Diamond, and longer reasoning makes it worse.","Qwen3.8 27B GGUF quantizations available from Unsloth on Hugging Face . So much to choose from! I will check 8-bit Q8_0 (29 GB), 4-bit Q4_K_M (17 GB), 2-bit UD-Q2_K_XL (10.7 GB), and the smallest one possible, 1-bit UD-IQ1_S (6.2 GB).","Previously, I investigated the Qwen3.6 27B model, which was good at generating SVG pelicans even at 12GB：https://quesma.com/blog/qwen-quantization-quality/, and maintained most of its knowledge up to 16GB：https://quesma.com/blog/quantization-hurts-knowledge/. At the same time, in Reddit threads, many complain that all quantizations, even the 8-bit ones, give worse results - with people asking why your local LLM feels dumber than it is：https://forum.level1techs.com/t/why-your-local-llm-feels-dumber-than-it-is/253917. Are these complaints grounded?","Measuring token prediction differences (KL-divergence, top-1 predictions) is easy, but it does not tell us whether the model gets worse at solving tasks. Some noise might be irrelevant for solving tasks, as (say) a quantized model generates an answer of precisely the same quality, paraphrased a bit. In other cases, a single different token might be a logical error, or even abruptly end the output.","So, I focus on directly measuring results on popular benchmarks - GPQA Diamond：https://artificialanalysis.ai/evaluations/gpqa-diamond, instruction-following IFBench：https://github.com/allenai/IFBench, programming Terminal-Bench 2.1：https://www.tbench.ai/leaderboard/terminal-bench/2.1. First, to replicate official results of the full model BF16 , and then to see how quantization affects results.","I burned around $3,000 on Modal：https://modal.com/ GPUs when I ran models with llama.cpp：https://github.com/ggml-org/llama.cpp using a build from 16 August 2026 as earlier builds do not work for this model：https://www.paulsprogrammingnotes.com/2026/08/running-qwen-3-8-27b-16gb.html. I could have run it on my own laptop, in principle, but (unlike pelican-generation), these are time-consuming benchmarks.","Note that I use F16 KV-cache regardless of model quantization, weighing around 2.3 GB per 32k tokens.","I used Unsloth quantizations：https://huggingface.co/unsloth/Qwen3.8-27B-GGUF: v2 for the 2-, 4-, and 8-bit models, and v3：https://unsloth.ai/docs/basics/dynamic-3.0-ggufs for the 1-bit models. Unsloth replaced the v2 files on 19 August 2026：https://huggingface.co/unsloth/Qwen3.8-27B-GGUF/discussions/74, so the exact files used for most tests are no longer available.","In short, if you go with a 4-bit quantization Q4_K_M (17GB), you won’t notice a difference on these benchmarks. At the same time, the effort setting matters a lot (note that the default is xhigh ) - and it is a tricky choice, as it can overthink：https://simonwillison.net/2026/Aug/16/qwen-38-27b/.","The easiest ones are one-shot tests: in this case, graduate-level science GPQA Diamond and instruction-following IFBench. I run each at three reasoning efforts: low , medium , and the default xhigh .","First and foremost, I was happy I replicated the official results. Running benchmarks is hard; there are many hidden settings or assumptions that can change the results drastically. Here, on the first go, results were as reported by Qwen.","Second, besides noise (bars are Wilson 95% confidence intervals：https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Wilson_score_interval, very conservative for run-to-run noise), there is little difference down to 4-bit; only the 2-bit scores a bit lower.","At the same time, thinking level changed the score dractically. The best results, for xhigh , needed around 8k reasoning tokens.","Here, to my great surprise, there is no change between models, down to a decent 2-bit one, weighing less than 11 GB. Yet, context is even lower, around 4k tokens.","How does it work for programming? Terminal-Bench 2.1 is a standard agentic benchmark, with 89 tasks. Here I use 3h timeout, xhigh effort. I reserve 98k context.","Not only does my measurement of BF16 replicate the stated result, but, to my surprise, Q4_K_M does as well. I accidentally skipped running Q8_0 ; yet, in this case, I can safely interpolate between 4-bit and the full model’s values. Running it would be both costly and unnecessary (and would exceed an informal blog post’s budget). Only at 2-bit UD-Q2_K_XL things break a bit. A noticeable fall, but still the level of Opus 4.7 or Gemini 3.1 Pro：https://www.tbench.ai/leaderboard/terminal-bench/2.1?lf=%7B%22agent_display%22:%7B%22kind%22:%22categorical%22,%22values%22:%5B%22Terminus+2%22%5D%7D%7D. Again, far from frontier, but also - far from useless.","Results are one thing, but what about the process? Do smaller models need more turns, tokens or time to get the result?","On the same solved tasks, UD-Q2_K_XL takes as many turns as BF16 but writes about a quarter more tokens. The number of turns stay roughly the same.","Quality drops off a cliff at 1-bit. As with knowledge：https://quesma.com/blog/quantization-hurts-knowledge/, quantization damage is nonlinear: first there is no measurable change, then a small decline, and finally a collapse.","While 2-bit quantizations work to some extent, even the best 1-bit model is useless for these benchmarks:","As you may see, the scores are around the random guessing level, with the smallest model being below that threshold. And longer reasoning makes it worse: at xhigh , scores drop below low , as the model more often reasons until the token budget runs out and returns an empty answer. Sure, Unsloth boasts that:","We also made some smaller UD-1bit quants with UD-IQ1_S being 6.2GB (without MTP) which retain around 72% top-1% accuracy yet being 89% smaller.","But in this case, these remaining 28% matter a lot. And this matches another user’s experience, vide Qwen3.8 27b 1bit brain damage quant on r/LocalLLaMA：https://www.reddit.com/r/LocalLLaMA/comments/1vtr3h0/ladies_and_gentlemen_i_present_to_you_qwen38_27b/.","Running these benchmarks isn’t cheap. Running benchmarks via API is costly, as I know from my previous benchmarks：https://quesma.com/benchmarks/. Running on rented GPU is much costlier.","I used Modal, as it is easy to run it from the CLI, including from agents. Other setups may have different pricing. Obviously, this calculation changes if you have your own devices.","It takes some testing to find the optimal way to run models. Usually, instead of using Multi-Token Prediction (MTP), which works well for a single stream, I use a few parallel streams. The key constraint is whether the GPU has enough memory for both the model and the required KV caches.","I used NVIDIA L40S (the same Ada Lovelace chip as the RTX 4090 but twice as much memory: 48 GB), H100 (80 GB) and H200 (141 GB). I would like to share costs to give you a ballpark estimate if you want to run benchmarks yourself.","For comparison, DeepSeek V4 Flash 0731：https://openrouter.ai/deepseek/deepseek-v4-flash-0731#providers, a 284B model, costs around $0.1/Mtok for output from the cheapest providers on OpenRouter. I am not sure how much of this difference comes from the efficiency of running models at scale, pricing strategy, or popularity.","If you run experiments locally, usually pick the best model that fits in your GPU memory together with the required context. For most tasks Unsloth’s Q4_K_M should be good enough, without any noticeable difference; for some simpler tasks UD-Q2_K_XL should be more than fine. Since people report that KV-caches are more susceptible to quantization, I may test it as well.","But in general, I believe that quantization should be embraced, rather than feared.","And what is your experience? Join the discussion on r/LocalLLaMA：https://www.reddit.com/r/LocalLLaMA/comments/1vz3ieu/benchmarking_qwen38_27b_quantizations_4bit_holds/, Hacker News：https://news.ycombinator.com/item?id=49611128, or LinkedIn：https://www.linkedin.com/feed/update/urn:li:activity:7498431004932743168/.","August 2026 on Baba Is Bench: Gemini 3.7 Flash, Grok 4.6 and DeepSeek V4 Pro 0813 each beat their predecessor while costing 3-20x less. For open-weight GLM-5.3 and Qwen3.8 progress is gradual.","A buyer’s guide to OpenAI Codex for individuals, teams, and enterprises. Includes comparisons with Claude Code.","We tested Qwen3.6 27B quantizations by Unsloth on Hugging Face, with pelicans on bikes, gears, Terminal-Bench 2.1, and AIME-120.","Understand what your coding agents actually do."],"articleImages":[{"sourceUrl":"https://quesma.com/_astro/hf_unsloth_qwen38_27b_choices.OSlYkgGR_2lHFg.webp","alt":"Qwen3.8 27B GGUF quantizations available from Unsloth on Hugging Face","afterParagraph":2,"url":"/media/articles/cmtt04yw104xuro5wqydtcs81/db542224fbe72df3.webp"},{"sourceUrl":"https://quesma.com/_astro/thumbnail.CU6fIcVH_bvV49.webp","alt":"Gemini 3.7 Flash, Grok 4.6, GLM-5.3 and DeepSeek V4 Pro joined the frontier","afterParagraph":32,"url":"/media/articles/cmtt04yw104xuro5wqydtcs81/ca8ab2528e586352.webp"},{"sourceUrl":"https://quesma.com/_astro/thumbnail.tG46lv8n_ru3uY.webp","alt":"OpenAI Codex pricing: the $270 PR a $200/month sub covers daily","afterParagraph":33,"url":"/media/articles/cmtt04yw104xuro5wqydtcs81/0e354ae3f3e52b11.webp"},{"sourceUrl":"https://quesma.com/_astro/thumbnail.DDdnibQ3_2jYG0b.webp","alt":"Do Qwen3.6 27B quantizations break the pelican?","afterParagraph":34,"url":"/media/articles/cmtt04yw104xuro5wqydtcs81/7fcee34daadcf718.webp"}],"mediaStatus":"ok","articleBodyZh":["实际上运行 Qwen3.8 27B 而不牺牲质量需要多少 GPU 内存？","完整的 BF16 模型占用 55 GB，这超出了大多数消费者硬件的承受范围。然而，17 GB 的 Q4_K_M 在一个流行的代理编程基准测试 Terminal-Bench 2.1 上与完整模型表现相同。它可以放入 24 GB 的显卡如 RTX 4090，同时仍能容纳大约 64k 的上下文 token。","压缩最终会达到极限。在 1 bit 时，模型在 GPQA Diamond 上的表现接近随机机会，而更长的推理会使情况更糟。","Qwen3.8 27B 的 GGUF 量化模型可从 Hugging Face 上的 Unsloth 获取。选择非常多！我将检查 8-bit Q8_0（29 GB）、4-bit Q4_K_M（17 GB）、2-bit UD-Q2_K_XL（10.7 GB）以及可能的最小模型 1-bit UD-IQ1_S（6.2 GB）。","此前，我研究了 Qwen3.6 27B 模型，即使在 12GB 下也能很好地生成 SVG 形式的鹈鹕图像：https://quesma.com/blog/qwen-quantization-quality/，且在 16GB 时保留了大部分知识：https://quesma.com/blog/quantization-hurts-knowledge/。同时，在 Reddit 讨论中，很多人抱怨所有量化模型，即使是 8-bit，也会产生更差的结果——有人问为什么本地 LLM 感觉比实际要笨：https://forum.level1techs.com/t/why-your-local-llm-feels-dumber-than-it-is/253917。这些抱怨有依据吗？","衡量 token 预测的差异（KL 散度、top-1 预测）很容易，但它并不能告诉我们模型在解决任务时是否变差。有些噪声对解决任务可能无关紧要，比如一个量化模型生成的答案质量完全相同，只是措辞稍有不同。在其他情况下，一个不同的 token 可能是逻辑错误，甚至会突然结束输出。","因此，我专注于直接在流行基准上测量结果——GPQA Diamond：https://artificialanalysis.ai/evaluations/gpqa-diamond，跟随指令的 IFBench：https://github.com/allenai/IFBench，编程 Terminal-Bench 2.1：https://www.tbench.ai/leaderboard/terminal-bench/2.1。首先，复现完整模型 BF16 的官方结果，然后观察量化对结果的影响。","我在 Modal：https://modal.com/ 的 GPU 上烧了大约 3,000 美元，当时我使用 llama.cpp：https://github.com/ggml-org/llama.cpp 运行模型，使用的是 2026 年 8 月 16 日的构建版本，因为早期的构建版本不适用于此模型：https://www.paulsprogrammingnotes.com/2026/08/running-qwen-3-8-27b-16gb.html。原则上我也可以在自己的笔记本电脑上运行，但（不像 pelican-generation）这些是耗时的基准测试。","请注意，无论模型量化如何，我都使用 F16 KV-cache，每 32k 个 token 大约占 2.3 GB。","我使用了 Unsloth 量化：https://huggingface.co/unsloth/Qwen3.8-27B-GGUF：v2 用于 2、4 和 8 位模型，v3：https://unsloth.ai/docs/basics/dynamic-3.0-ggufs 用于 1 位模型。Unsloth 于 2026 年 8 月 19 日替换了 v2 文件：https://huggingface.co/unsloth/Qwen3.8-27B-GGUF/discussions/74，因此大多数测试中使用的确切文件不再可用。","总之，如果你选择 4 位量化 Q4_K_M（17GB），你在这些基准测试中不会注意到区别。同时，努力设置非常重要（注意默认值是 xhigh）——这是一个棘手的选择，因为它可能导致过度思考：https://simonwillison.net/2026/Aug/16/qwen-38-27b/。","最简单的是一次性测试：在这种情况下，是研究生级科学 GPQA Diamond 和指令执行 IFBench。我在三个推理努力等级下运行每个测试：low、medium 和默认的 xhigh。","首先，我很高兴我复制了官方结果。运行基准测试很难；有许多隐藏的设置或假设会极大地改变结果。在这里，第一次运行的结果与 Qwen 报告的一致。","其次，除了噪声（柱状图为 Wilson 95% 置信区间：https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Wilson_score_interval，对运行间噪声非常保守），即使量化到 4 位，差异也很小；只有 2 位的分数稍低一些。","同时，思考等级极大地改变了分数。对于 xhigh，最佳结果需要大约 8k 推理 token。","在这里，让我非常惊讶的是，即使是减到 2 位的不错模型，模型间也没有变化，其权重不到 11 GB。然而，上下文甚至更低，大约 4k token。","编程方面它是如何工作的？Terminal-Bench 2.1 是一个标准的智能代理基准，包含 89 个任务。在这里，我使用 3 小时超时，极高努力。保留了 98k 上下文。","不仅我对 BF16 的测量复现了声明的结果，令我惊讶的是，Q4_K_M 也复现了。我不小心跳过了运行 Q8_0；然而在这种情况下，我可以安全地在 4 位与完整模型的数值之间插值。运行它既昂贵又不必要（而且会超出非正式博客文章的预算）。只有在 2 位 UD-Q2_K_XL 时情况会有些崩溃。明显下降，但仍在 Opus 4.7 或 Gemini 3.1 Pro 的水平：https://www.tbench.ai/leaderboard/terminal-bench/2.1?lf=%7B%22agent_display%22:%7B%22kind%22:%22categorical%22,%22values%22:%5B%22Terminus+2%22%5D%7D%7D。再次说明，远未到前沿，但也——远非无用。","结果是一方面，但过程呢？较小的模型是否需要更多回合、更多 token 或更多时间来得到结果？","在相同已解决的任务上，UD-Q2_K_XL 所需回合与 BF16 相同，但生成的 token 数多出约四分之一。回合数大致保持不变。","质量在 1 位时骤降。与知识类似：https://quesma.com/blog/quantization-hurts-knowledge/，量化的损害是非线性的：起初没有可测量的变化，然后小幅下降，最终崩溃。","虽然 2 位量化在某种程度上有效，但即使是最好的 1 位模型，对这些基准测试也是无用的：","正如你所见，分数接近随机猜测水平，最小的模型甚至低于该阈值。更长的推理会使情况更糟：在极高设置下，分数低于低设置，因为模型更频繁地推理，直到 token 预算耗尽并返回空答案。当然，Unsloth 自豪地宣称：","我们也制作了一些较小的 UD-1bit 量化模型，其中 UD-IQ1_S 为 6.2GB（不含 MTP），在保持约 72% top-1% 准确率的同时，体积缩小了 89%。","但在这种情况下，这剩余的 28% 非常重要。这也符合另一位用户的经验，参见 Qwen3.8 27b 1bit 脑损量化 在 r/LocalLLaMA：https://www.reddit.com/r/LocalLLaMA/comments/1vtr3h0/ladies_and_gentlemen_i_present_to_you_qwen38_27b/.","运行这些基准测试并不便宜。通过 API 运行基准测试成本很高，这一点我从之前的基准测试中就知道：https://quesma.com/benchmarks/。在租用的 GPU 上运行成本更高。","我使用了 Modal，因为它很容易从 CLI 运行，包括通过代理运行。其他设置可能有不同的定价。显然，如果你有自己的设备，这个计算会有所不同。","找到运行模型的最佳方式需要一些测试。通常，我不是使用适用于单流的多令牌预测（MTP），而是使用几个并行流。关键限制是 GPU 是否有足够的内存容纳模型和所需的 KV 缓存。","我使用了 NVIDIA L40S（与 RTX 4090 相同的 Ada Lovelace 芯片，但内存翻倍：48 GB）、H100（80 GB）和 H200（141 GB）。我想分享成本信息，给你一个大致的估计，如果你想自己运行基准测试的话。","相比之下，DeepSeek V4 Flash 0731：https://openrouter.ai/deepseek/deepseek-v4-flash-0731#providers，一款 284B 模型，最便宜的 OpenRouter 提供商输出成本约为 $0.1/Mtok。我不确定这些差异中有多少来自于大规模运行模型的效率、定价策略或受欢迎程度。","如果你在本地运行实验，通常选择最适合 GPU 内存并包含所需上下文的最佳模型。对于大多数任务，Unsloth 的 Q4_K_M 应该足够好，没有明显差异；对于一些简单任务，UD-Q2_K_XL 完全可以胜任。由于有人报告 KV 缓存更容易受到量化影响，我可能也会测试这一点。","但总体而言，我认为量化应该被接受，而不是害怕。","那么你的经验如何？加入讨论：r/LocalLLaMA：https://www.reddit.com/r/LocalLLaMA/comments/1vz3ieu/benchmarking_qwen38_27b_quantizations_4bit_holds/，Hacker News：https://news.ycombinator.com/item?id=49611128，或 LinkedIn：https://www.linkedin.com/feed/update/urn:li:activity:7498431004932743168/。","2026 年 8 月，在 Baba Is Bench 上：Gemini 3.7 Flash、Grok 4.6 和 DeepSeek V4 Pro 0813 均击败了其前辈，同时成本降低 3-20 倍。对于开源权重的 GLM-5.3 和 Qwen3.8，进展相对缓慢。","个人、团队和企业购买 OpenAI Codex 的指南。包含与 Claude Code 的比较。","我们在 Hugging Face 上测试了 Unsloth 对 Qwen3.6 27B 的量化，使用了骑自行车的鹈鹕、齿轮、Terminal-Bench 2.1 和 AIME-120。","了解你的编码代理实际在做什么。"],"translationStatus":"translated","bodyOrigin":"source-page","editorial":{"summary":"Aioga 编辑摘要：作者实测 Qwen3.8 27B 各档 Unsloth 量化（GGUF）在 GPQA Diamond、IFBench 和 Terminal-Bench 2.1 上的表现。 Aioga 将其归入「行业动态」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：公司与行业类动态需要放在竞争格局、商业化路径、资本信号和监管环境中观察，单条公告不能代表最终结果。","viewpoint":"Aioga 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上的表现。","url":"https://www.aioga.com/news/cmtt04yw104xuro5wqydtcs81/","articleBody":["实际上运行 Qwen3.8 27B 而不牺牲质量需要多少 GPU 内存？","完整的 BF16 模型占用 55 GB，这超出了大多数消费者硬件的承受范围。然而，17 GB 的 Q4_K_M 在一个流行的代理编程基准测试 Terminal-Bench 2.1 上与完整模型表现相同。它可以放入 24 GB 的显卡如 RTX 4090，同时仍能容纳大约 64k 的上下文 token。","压缩最终会达到极限。在 1 bit 时，模型在 GPQA Diamond 上的表现接近随机机会，而更长的推理会使情况更糟。","Qwen3.8 27B 的 GGUF 量化模型可从 Hugging Face 上的 Unsloth 获取。选择非常多！我将检查 8-bit Q8_0（29 GB）、4-bit Q4_K_M（17 GB）、2-bit UD-Q2_K_XL（10.7 GB）以及可能的最小模型 1-bit UD-IQ1_S（6.2 GB）。","此前，我研究了 Qwen3.6 27B 模型，即使在 12GB 下也能很好地生成 SVG 形式的鹈鹕图像：https://quesma.com/blog/qwen-quantization-quality/，且在 16GB 时保留了大部分知识：https://quesma.com/blog/quantization-hurts-knowledge/。同时，在 Reddit 讨论中，很多人抱怨所有量化模型，即使是 8-bit，也会产生更差的结果——有人问为什么本地 LLM 感觉比实际要笨：https://forum.level1techs.com/t/why-your-local-llm-feels-dumber-than-it-is/253917。这些抱怨有依据吗？","衡量 token 预测的差异（KL 散度、top-1 预测）很容易，但它并不能告诉我们模型在解决任务时是否变差。有些噪声对解决任务可能无关紧要，比如一个量化模型生成的答案质量完全相同，只是措辞稍有不同。在其他情况下，一个不同的 token 可能是逻辑错误，甚至会突然结束输出。","因此，我专注于直接在流行基准上测量结果——GPQA Diamond：https://artificialanalysis.ai/evaluations/gpqa-diamond，跟随指令的 IFBench：https://github.com/allenai/IFBench，编程 Terminal-Bench 2.1：https://www.tbench.ai/leaderboard/terminal-bench/2.1。首先，复现完整模型 BF16 的官方结果，然后观察量化对结果的影响。","我在 Modal：https://modal.com/ 的 GPU 上烧了大约 3,000 美元，当时我使用 llama.cpp：https://github.com/ggml-org/llama.cpp 运行模型，使用的是 2026 年 8 月 16 日的构建版本，因为早期的构建版本不适用于此模型：https://www.paulsprogrammingnotes.com/2026/08/running-qwen-3-8-27b-16gb.html。原则上我也可以在自己的笔记本电脑上运行，但（不像 pelican-generation）这些是耗时的基准测试。","请注意，无论模型量化如何，我都使用 F16 KV-cache，每 32k 个 token 大约占 2.3 GB。","我使用了 Unsloth 量化：https://huggingface.co/unsloth/Qwen3.8-27B-GGUF：v2 用于 2、4 和 8 位模型，v3：https://unsloth.ai/docs/basics/dynamic-3.0-ggufs 用于 1 位模型。Unsloth 于 2026 年 8 月 19 日替换了 v2 文件：https://huggingface.co/unsloth/Qwen3.8-27B-GGUF/discussions/74，因此大多数测试中使用的确切文件不再可用。","总之，如果你选择 4 位量化 Q4_K_M（17GB），你在这些基准测试中不会注意到区别。同时，努力设置非常重要（注意默认值是 xhigh）——这是一个棘手的选择，因为它可能导致过度思考：https://simonwillison.net/2026/Aug/16/qwen-38-27b/。","最简单的是一次性测试：在这种情况下，是研究生级科学 GPQA Diamond 和指令执行 IFBench。我在三个推理努力等级下运行每个测试：low、medium 和默认的 xhigh。","首先，我很高兴我复制了官方结果。运行基准测试很难；有许多隐藏的设置或假设会极大地改变结果。在这里，第一次运行的结果与 Qwen 报告的一致。","其次，除了噪声（柱状图为 Wilson 95% 置信区间：https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Wilson_score_interval，对运行间噪声非常保守），即使量化到 4 位，差异也很小；只有 2 位的分数稍低一些。","同时，思考等级极大地改变了分数。对于 xhigh，最佳结果需要大约 8k 推理 token。","在这里，让我非常惊讶的是，即使是减到 2 位的不错模型，模型间也没有变化，其权重不到 11 GB。然而，上下文甚至更低，大约 4k token。","编程方面它是如何工作的？Terminal-Bench 2.1 是一个标准的智能代理基准，包含 89 个任务。在这里，我使用 3 小时超时，极高努力。保留了 98k 上下文。","不仅我对 BF16 的测量复现了声明的结果，令我惊讶的是，Q4_K_M 也复现了。我不小心跳过了运行 Q8_0；然而在这种情况下，我可以安全地在 4 位与完整模型的数值之间插值。运行它既昂贵又不必要（而且会超出非正式博客文章的预算）。只有在 2 位 UD-Q2_K_XL 时情况会有些崩溃。明显下降，但仍在 Opus 4.7 或 Gemini 3.1 Pro 的水平：https://www.tbench.ai/leaderboard/terminal-bench/2.1?lf=%7B%22agent_display%22:%7B%22kind%22:%22categorical%22,%22values%22:%5B%22Terminus+2%22%5D%7D%7D。再次说明，远未到前沿，但也——远非无用。","结果是一方面，但过程呢？较小的模型是否需要更多回合、更多 token 或更多时间来得到结果？","在相同已解决的任务上，UD-Q2_K_XL 所需回合与 BF16 相同，但生成的 token 数多出约四分之一。回合数大致保持不变。","质量在 1 位时骤降。与知识类似：https://quesma.com/blog/quantization-hurts-knowledge/，量化的损害是非线性的：起初没有可测量的变化，然后小幅下降，最终崩溃。","虽然 2 位量化在某种程度上有效，但即使是最好的 1 位模型，对这些基准测试也是无用的：","正如你所见，分数接近随机猜测水平，最小的模型甚至低于该阈值。更长的推理会使情况更糟：在极高设置下，分数低于低设置，因为模型更频繁地推理，直到 token 预算耗尽并返回空答案。当然，Unsloth 自豪地宣称：","我们也制作了一些较小的 UD-1bit 量化模型，其中 UD-IQ1_S 为 6.2GB（不含 MTP），在保持约 72% top-1% 准确率的同时，体积缩小了 89%。","但在这种情况下，这剩余的 28% 非常重要。这也符合另一位用户的经验，参见 Qwen3.8 27b 1bit 脑损量化 在 r/LocalLLaMA：https://www.reddit.com/r/LocalLLaMA/comments/1vtr3h0/ladies_and_gentlemen_i_present_to_you_qwen38_27b/.","运行这些基准测试并不便宜。通过 API 运行基准测试成本很高，这一点我从之前的基准测试中就知道：https://quesma.com/benchmarks/。在租用的 GPU 上运行成本更高。","我使用了 Modal，因为它很容易从 CLI 运行，包括通过代理运行。其他设置可能有不同的定价。显然，如果你有自己的设备，这个计算会有所不同。","找到运行模型的最佳方式需要一些测试。通常，我不是使用适用于单流的多令牌预测（MTP），而是使用几个并行流。关键限制是 GPU 是否有足够的内存容纳模型和所需的 KV 缓存。","我使用了 NVIDIA L40S（与 RTX 4090 相同的 Ada Lovelace 芯片，但内存翻倍：48 GB）、H100（80 GB）和 H200（141 GB）。我想分享成本信息，给你一个大致的估计，如果你想自己运行基准测试的话。","相比之下，DeepSeek V4 Flash 0731：https://openrouter.ai/deepseek/deepseek-v4-flash-0731#providers，一款 284B 模型，最便宜的 OpenRouter 提供商输出成本约为 $0.1/Mtok。我不确定这些差异中有多少来自于大规模运行模型的效率、定价策略或受欢迎程度。","如果你在本地运行实验，通常选择最适合 GPU 内存并包含所需上下文的最佳模型。对于大多数任务，Unsloth 的 Q4_K_M 应该足够好，没有明显差异；对于一些简单任务，UD-Q2_K_XL 完全可以胜任。由于有人报告 KV 缓存更容易受到量化影响，我可能也会测试这一点。","但总体而言，我认为量化应该被接受，而不是害怕。","那么你的经验如何？加入讨论：r/LocalLLaMA：https://www.reddit.com/r/LocalLLaMA/comments/1vz3ieu/benchmarking_qwen38_27b_quantizations_4bit_holds/，Hacker News：https://news.ycombinator.com/item?id=49611128，或 LinkedIn：https://www.linkedin.com/feed/update/urn:li:activity:7498431004932743168/。","2026 年 8 月，在 Baba Is Bench 上：Gemini 3.7 Flash、Grok 4.6 和 DeepSeek V4 Pro 0813 均击败了其前辈，同时成本降低 3-20 倍。对于开源权重的 GLM-5.3 和 Qwen3.8，进展相对缓慢。","个人、团队和企业购买 OpenAI Codex 的指南。包含与 Claude Code 的比较。","我们在 Hugging Face 上测试了 Unsloth 对 Qwen3.6 27B 的量化，使用了骑自行车的鹈鹕、齿轮、Terminal-Bench 2.1 和 AIME-120。","了解你的编码代理实际在做什么。"]},"en":{"title":"Qwen3.8 27B quantization scheme benchmark actual measurement: Q4_K_M performance is stable, 1 bit is close to random level","summary":"The author conducted actual tests on the performance of Qwen3.8 27B at various Unsloth quantization levels (GGUF) on GPQA Diamond, IFBench, and Terminal-Bench 2.1.","category":"Industry","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Qwen3.8 27B quantization scheme benchmark actual measurement: Q4_K_M performance is stable, 1 bit is close to random level - Aioga AI News","description":"The author conducted actual tests on the performance of Qwen3.8 27B at various Unsloth quantization levels (GGUF) on GPQA Diamond, IFBench, and Terminal-Bench 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Terminal-Bench 2.1에서 실측한 성능.","category":"업계 동향","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Qwen3.8 27B 양자화 계획 기준 실제 측정: Q4_K_M 성능 안정적, 1자리 근접 무작위 수준 - Aioga AI 뉴스","description":"작성자가 Qwen3.8 27B 각 단계 Unsloth 양자화(GGUF)를 GPQA Diamond, IFBench 및 Terminal-Bench 2.1에서 실측한 성능.","url":"https://www.aioga.com/ko/news/cmtt04yw104xuro5wqydtcs81/","contentTranslated":true,"sourceHash":"6134357c1bfba7ee","translatedAt":"2026-09-14T21:19:27.649Z"},"es":{"title":"Prueba de referencia del esquema de cuantificación Qwen3.8 27B: Q4_K_M muestra un rendimiento estable, 1 dígito cercano al nivel aleatorio","summary":"El autor probó empíricamente el rendimiento de Qwen3.8 27B con varias cuantificaciones de Unsloth (GGUF) en GPQA Diamond, IFBench y Terminal-Bench 2.1.","category":"Industria","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Prueba de referencia del esquema de cuantificación Qwen3.8 27B: Q4_K_M muestra un rendimiento estable, 1 dígito cercano al nivel aleatorio - Aioga Noticias de IA","description":"El autor probó empíricamente el rendimiento de Qwen3.8 27B con varias cuantificaciones de Unsloth (GGUF) en GPQA Diamond, IFBench y Terminal-Bench 2.1.","url":"https://www.aioga.com/es/news/cmtt04yw104xuro5wqydtcs81/","contentTranslated":true,"sourceHash":"6134357c1bfba7ee","translatedAt":"2026-09-14T21:19:17.733Z"},"fr":{"title":"Mesure réelle du plan de quantification de Qwen3.8 27B : la performance de Q4_K_M est stable, 1 niveau proche du niveau aléatoire","summary":"L'auteur a testé les performances de Qwen3.8 27B avec différentes quantifications Unsloth (GGUF) sur GPQA Diamond, IFBench et Terminal-Bench 2.1.","category":"Industrie","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Mesure réelle du plan de quantification de Qwen3.8 27B : la performance de Q4_K_M est stable, 1 niveau proche du niveau aléatoire - Aioga Actualités IA","description":"L'auteur a testé les performances de Qwen3.8 27B avec différentes quantifications Unsloth (GGUF) sur GPQA Diamond, IFBench et Terminal-Bench 2.1.","url":"https://www.aioga.com/fr/news/cmtt04yw104xuro5wqydtcs81/","contentTranslated":true,"sourceHash":"6134357c1bfba7ee","translatedAt":"2026-09-14T21:20:12.027Z"},"de":{"title":"Qwen3.8 27B Quantisierungsplan Benchmark-Messung: Q4_K_M zeigt stabile Leistung, 1 Stelle nahe dem Zufallsniveau","summary":"Der Autor hat die Leistung der Qwen3.8 27B Unsloth-Quantisierung (GGUF) in allen Stufen auf GPQA Diamond, IFBench und Terminal-Bench 2.1 praktisch getestet.","category":"行业动态","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Qwen3.8 27B Quantisierungsplan Benchmark-Messung: Q4_K_M zeigt stabile Leistung, 1 Stelle nahe dem Zufallsniveau - Aioga KI-News","description":"Der Autor hat die Leistung der Qwen3.8 27B Unsloth-Quantisierung (GGUF) in allen Stufen auf GPQA Diamond, IFBench und Terminal-Bench 2.1 praktisch getestet.","url":"https://www.aioga.com/de/news/cmtt04yw104xuro5wqydtcs81/","contentTranslated":true,"sourceHash":"6134357c1bfba7ee","translatedAt":"2026-09-14T21:20:12.603Z"},"pt-BR":{"title":"Qwen3.8 27B Medição de referência do esquema de quantização: Q4_K_M apresenta desempenho estável, 1 dígito próximo ao nível aleatório","summary":"O autor testou na prática o desempenho das quantizações Unsloth (GGUF) de Qwen3.8 27B em diferentes níveis no GPQA Diamond, IFBench e Terminal-Bench 2.1.","category":"行业动态","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Qwen3.8 27B Medição de referência do esquema de quantização: Q4_K_M apresenta desempenho estável, 1 dígito próximo ao nível aleatório - Aioga Notícias de IA","description":"O autor testou na prática o desempenho das quantizações Unsloth (GGUF) de Qwen3.8 27B em diferentes níveis no GPQA Diamond, IFBench e Terminal-Bench 2.1.","url":"https://www.aioga.com/pt-BR/news/cmtt04yw104xuro5wqydtcs81/","contentTranslated":true,"sourceHash":"6134357c1bfba7ee","translatedAt":"2026-09-14T21:20:55.370Z"},"ru":{"title":"Фактическое измерение эталонной схемы квантизации Qwen3.8 27B: Q4_K_M показывает стабильную работу, 1 бит близок к случайному уровню","summary":"Автор провел практическое тестирование производительности Qwen3.8 27B с различными уровнями квантования Unsloth (GGUF) на GPQA Diamond, IFBench и Terminal-Bench 2.1.","category":"行业动态","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Фактическое измерение эталонной схемы квантизации Qwen3.8 27B: Q4_K_M показывает стабильную работу, 1 бит близок к случайному уровню - Aioga Новости ИИ","description":"Автор провел практическое тестирование производительности Qwen3.8 27B с различными уровнями квантования Unsloth (GGUF) на GPQA Diamond, IFBench и Terminal-Bench 2.1.","url":"https://www.aioga.com/ru/news/cmtt04yw104xuro5wqydtcs81/","contentTranslated":true,"sourceHash":"6134357c1bfba7ee","translatedAt":"2026-09-14T21:21:01.967Z"},"ar":{"title":"Qwen3.8 27B برنامج القياس المرجعي الكمي: أداء Q4_K_M مستقر، مستوى واحد قريب من المستوى العشوائي","summary":"قام المؤلف باختبار أداء نماذج Qwen3.8 27B المحسوبة بمستوى Unsloth (GGUF) على GPQA Diamond و IFBench و Terminal-Bench 2.1.","category":"行业动态","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Qwen3.8 27B برنامج القياس المرجعي الكمي: أداء Q4_K_M مستقر، مستوى واحد قريب من المستوى العشوائي - Aioga أخبار الذكاء الاصطناعي","description":"قام المؤلف باختبار أداء نماذج Qwen3.8 27B المحسوبة بمستوى Unsloth (GGUF) على GPQA Diamond و IFBench و Terminal-Bench 2.1.","url":"https://www.aioga.com/ar/news/cmtt04yw104xuro5wqydtcs81/","contentTranslated":true,"sourceHash":"6134357c1bfba7ee","translatedAt":"2026-09-14T21:21:52.376Z"},"hi":{"title":"Qwen3.8 27B क्वांटाइज़ेशन योजना मानक वास्तविक माप: Q4_K_M प्रदर्शन स्थिर, 1 अंक लगभग यादृच्छिक स्तर के करीब","summary":"लेखक ने Qwen3.8 27B के सभी Unsloth क्वांटाइजेशन (GGUF) का GPQA Diamond, IFBench और Terminal-Bench 2.1 पर प्रदर्शन वास्तव में मापा।","category":"行业动态","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Qwen3.8 27B क्वांटाइज़ेशन योजना मानक वास्तविक माप: Q4_K_M प्रदर्शन स्थिर, 1 अंक लगभग यादृच्छिक स्तर के करीब - Aioga AI समाचार","description":"लेखक ने Qwen3.8 27B के सभी Unsloth क्वांटाइजेशन (GGUF) का GPQA Diamond, IFBench और Terminal-Bench 2.1 पर प्रदर्शन वास्तव में मापा।","url":"https://www.aioga.com/hi/news/cmtt04yw104xuro5wqydtcs81/","contentTranslated":true,"sourceHash":"6134357c1bfba7ee","translatedAt":"2026-09-14T21:21:50.493Z"},"it":{"title":"Misurazione del benchmark del piano di quantizzazione Qwen3.8 27B: Q4_K_M mostra prestazioni stabili, 1 cifra vicino al livello casuale","summary":"L'autore ha testato le prestazioni di Qwen3.8 27B in tutte le versioni di quantizzazione Unsloth (GGUF) su GPQA Diamond, IFBench e Terminal-Bench 2.1.","category":"行业动态","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Misurazione del benchmark del piano di quantizzazione Qwen3.8 27B: Q4_K_M mostra prestazioni stabili, 1 cifra vicino al livello casuale - Aioga Notizie IA","description":"L'autore ha testato le prestazioni di Qwen3.8 27B in tutte le versioni di quantizzazione Unsloth (GGUF) su GPQA Diamond, IFBench e Terminal-Bench 2.1.","url":"https://www.aioga.com/it/news/cmtt04yw104xuro5wqydtcs81/","contentTranslated":true,"sourceHash":"6134357c1bfba7ee","translatedAt":"2026-09-14T21:22:41.948Z"},"nl":{"title":"Qwen3.8 27B kwantisering schema benchmark metingen: Q4_K_M presteert stabiel, 1 bit dicht bij willekeurig niveau","summary":"De auteur heeft de prestaties van Qwen3.8 27B in verschillende Unsloth-quantisaties (GGUF) getest op GPQA Diamond, IFBench en Terminal-Bench 2.1.","category":"行业动态","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Qwen3.8 27B kwantisering schema benchmark metingen: Q4_K_M presteert stabiel, 1 bit dicht bij willekeurig niveau - Aioga AI-nieuws","description":"De auteur heeft de prestaties van Qwen3.8 27B in verschillende Unsloth-quantisaties (GGUF) getest op GPQA Diamond, IFBench en Terminal-Bench 2.1.","url":"https://www.aioga.com/nl/news/cmtt04yw104xuro5wqydtcs81/","contentTranslated":true,"sourceHash":"6134357c1bfba7ee","translatedAt":"2026-09-14T21:22:36.205Z"},"tr":{"title":"Qwen3.8 27B nicelleştirme planı temel gerçek ölçüm: Q4_K_M performansı istikrarlı, 1 seviye rastgele seviyeye yakın","summary":"Yazar, Qwen3.8 27B'in tüm Unsloth (GGUF) seviyelerinin GPQA Diamond, IFBench ve Terminal-Bench 2.1 üzerindeki performansını gerçek testlerle ölçtü.","category":"行业动态","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Qwen3.8 27B nicelleştirme planı temel gerçek ölçüm: Q4_K_M performansı istikrarlı, 1 seviye rastgele seviyeye yakın - Aioga AI Haberleri","description":"Yazar, Qwen3.8 27B'in tüm Unsloth (GGUF) seviyelerinin GPQA Diamond, IFBench ve Terminal-Bench 2.1 üzerindeki performansını gerçek testlerle ölçtü.","url":"https://www.aioga.com/tr/news/cmtt04yw104xuro5wqydtcs81/","contentTranslated":true,"sourceHash":"6134357c1bfba7ee","translatedAt":"2026-09-14T21:23:33.160Z"},"vi":{"title":"Đo lường thực tế của kế hoạch lượng hóa Qwen3.8 27B: Q4_K_M hoạt động ổn định, 1 vị trí gần mức ngẫu nhiên","summary":"Tác giả đã thực nghiệm hiệu suất của Qwen3.8 27B được lượng tử hóa Unsloth (GGUF) ở các cấp độ khác nhau trên GPQA Diamond, IFBench và Terminal-Bench 2.1.","category":"行业动态","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Đo lường thực tế của kế hoạch lượng hóa Qwen3.8 27B: Q4_K_M hoạt động ổn định, 1 vị trí gần mức ngẫu nhiên - Tin tức AI Aioga","description":"Tác giả đã thực nghiệm hiệu suất của Qwen3.8 27B được lượng tử hóa Unsloth (GGUF) ở các cấp độ khác nhau trên GPQA Diamond, IFBench và Terminal-Bench 2.1.","url":"https://www.aioga.com/vi/news/cmtt04yw104xuro5wqydtcs81/","contentTranslated":true,"sourceHash":"6134357c1bfba7ee","translatedAt":"2026-09-14T21:23:29.338Z"},"id":{"title":"Qwen3.8 27B Rencana Kuantisasi Benchmark Pengukuran Aktual: Q4_K_M menunjukkan kinerja yang stabil, 1 berada dekat dengan tingkat acak","summary":"Penulis menguji secara langsung kinerja Qwen3.8 27B dengan berbagai tingkat kuantisasi Unsloth (GGUF) pada GPQA Diamond, IFBench, dan Terminal-Bench 2.1.","category":"行业动态","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Qwen3.8 27B Rencana Kuantisasi Benchmark Pengukuran Aktual: Q4_K_M menunjukkan kinerja yang stabil, 1 berada dekat dengan tingkat acak - Berita AI Aioga","description":"Penulis menguji secara langsung kinerja Qwen3.8 27B dengan berbagai tingkat kuantisasi Unsloth (GGUF) pada GPQA Diamond, IFBench, dan Terminal-Bench 2.1.","url":"https://www.aioga.com/id/news/cmtt04yw104xuro5wqydtcs81/","contentTranslated":true,"sourceHash":"6134357c1bfba7ee","translatedAt":"2026-09-14T21:24:20.365Z"},"th":{"title":"Qwen3.8 27B แผนการวัดปริมาณมาตรฐานการทดสอบจริง: Q4_K_M มีความเสถียรในการแสดงผล, 1 ตำแหน่งใกล้กับระดับสุ่ม","summary":"ผู้เขียนได้ทดสอบการวัดปริมาณ Unsloth (GGUF) ของ Qwen3.8 27B ในแต่ละระดับบน GPQA Diamond, IFBench และ Terminal-Bench 2.1","category":"行业动态","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Qwen3.8 27B แผนการวัดปริมาณมาตรฐานการทดสอบจริง: Q4_K_M มีความเสถียรในการแสดงผล, 1 ตำแหน่งใกล้กับระดับสุ่ม - ข่าว AI Aioga","description":"ผู้เขียนได้ทดสอบการวัดปริมาณ Unsloth (GGUF) ของ Qwen3.8 27B ในแต่ละระดับบน GPQA Diamond, IFBench และ Terminal-Bench 2.1","url":"https://www.aioga.com/th/news/cmtt04yw104xuro5wqydtcs81/","contentTranslated":true,"sourceHash":"6134357c1bfba7ee","translatedAt":"2026-09-14T21:24:29.323Z"},"pl":{"title":"Qwen3.8 27B Pomiar referencyjny programu kwantyzacji: Q4_K_M wykazuje stabilne działanie, 1 znak bliski poziomowi losowemu","summary":"Autor przeprowadził rzeczywiste testy Qwen3.8 27B z różnymi poziomami kwantyzacji Unsloth (GGUF) w benchmarkach GPQA Diamond, IFBench i Terminal-Bench 2.1.","category":"行业动态","source":"Hacker News 热门（buzzing.cc 中文翻译）","aggregationSource":"Hacker News 热门（buzzing.cc 中文翻译）","pageTitle":"Qwen3.8 27B Pomiar referencyjny programu kwantyzacji: Q4_K_M wykazuje stabilne działanie, 1 znak bliski poziomowi losowemu - Aioga Wiadomości AI","description":"Autor przeprowadził rzeczywiste testy Qwen3.8 27B z różnymi poziomami kwantyzacji Unsloth (GGUF) w benchmarkach GPQA Diamond, IFBench i Terminal-Bench 2.1.","url":"https://www.aioga.com/pl/news/cmtt04yw104xuro5wqydtcs81/","contentTranslated":true,"sourceHash":"6134357c1bfba7ee","translatedAt":"2026-09-14T21:25:24.006Z"}},"evidenceTier":"verified-news","reviewStatus":"automated-ingest","indexable":true,"editorialCover":""}}