{"id":192,"date":"2026-10-08T18:42:58","date_gmt":"2026-10-08T14:42:58","guid":{"rendered":"https:\/\/demensdeum.com\/blog\/2026\/10\/08\/airllm-experiments\/"},"modified":"2026-10-08T19:11:07","modified_gmt":"2026-10-08T15:11:07","slug":"airllm-experiments","status":"publish","type":"post","link":"https:\/\/demensdeum.com\/blog\/zh\/2026\/10\/08\/airllm-experiments\/","title":{"rendered":"AirLLM\uff1a\u5f31\u663e\u5361\u4e0a\u7684\u5927\u578b\u8bed\u8a00\u6a21\u578b"},"content":{"rendered":"<p>\u901a\u5e38\uff0c\u8fd0\u884c\u5177\u6709 700 \u4ebf\u4e2a\u53c2\u6570\u7684\u5927\u578b\u8bed\u8a00\u6a21\u578b\u9700\u8981\u6570\u5341 GB \u7684\u89c6\u9891\u5185\u5b58\u3002 AirLLM \u66f4\u51b7\u9759\u5730\u5904\u7406\u8fd9\u4e2a\u95ee\u9898\uff0c\u5e76\u63d0\u4f9b\u4e86\u4e00\u79cd\u4e0d\u540c\u7684\u65b9\u5f0f\uff1a\u8fd9\u6837\u7684\u6a21\u578b\u53ef\u4ee5\u5728\u53ea\u6709 4 GB \u5185\u5b58\u7684\u663e\u5361\u4e0a\u8fd0\u884c\uff0c\u5e76\u4e14\u4e0d\u9700\u8981\u91cf\u5316\u3001\u84b8\u998f\u548c\u4fee\u526a\u3002\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u5c06\u5c1d\u8bd5\u4e0d\u614c\u4e0d\u5fd9\u5730\u544a\u8bc9\u60a8 AirLLM \u662f\u5982\u4f55\u5de5\u4f5c\u7684\uff0c\u5982\u4f55\u4f7f\u7528\u5b83\uff0c\u5e76\u4e14\u6211\u5c06\u5206\u4eab\u6211\u7684\u5c0f\u9879\u76ee AirLLM-experiments\uff0c\u8fd9\u4f7f\u5f97\u4f7f\u7528\u8fd9\u4e2a\u5e93\u66f4\u52a0\u65b9\u4fbf\u3002<\/p>\n<h2>\u4ec0\u4e48\u662f AirLLM<\/h2>\n<p>AirLLM \u662f\u7531 Gavin Li \u7f16\u5199\u7684\u5927\u578b\u8bed\u8a00\u6a21\u578b\u63a8\u7406\u5f00\u6e90\u5e93\u3002\u5b83\u7684\u60f3\u6cd5\u7b80\u5355\u800c\u4f18\u96c5\uff1a\u8be5\u5e93\u4e00\u6b21\u4ec5\u5c06\u4e00\u5c42\u52a0\u8f7d\u5230 GPU \u4e0a\uff0c\u800c\u4e0d\u662f\u5c06\u6240\u6709\u6a21\u578b\u6743\u91cd\u4e00\u6b21\u5b58\u50a8\u5728\u89c6\u9891\u5185\u5b58\u4e2d\u3002\u6743\u91cd\u4ee5\u5207\u5206\u6210\u5c42\u7684\u5206\u7247\u7684\u5f62\u5f0f\u5b58\u50a8\u5728\u78c1\u76d8\u4e0a\uff0c\u5728\u751f\u6210\u8fc7\u7a0b\u4e2d\uff0c\u6bcf\u5c42\u4f9d\u6b21\u52a0\u8f7d\u3001\u5904\u7406\u548c\u5378\u8f7d\u3002<\/p>\n<p>\u7531\u6b64\u53ef\u89c1\uff0c\u6240\u9700\u7684\u89c6\u9891\u5185\u5b58\u91cf\u5e76\u4e0d\u53d6\u51b3\u4e8e\u6a21\u578b\u7684\u6574\u4f53\u5927\u5c0f\uff0c\u800c\u662f\u53d6\u51b3\u4e8e\u5176\u4e2d\u4e00\u5c42\u7684\u5927\u5c0f\u3002\u8fd9\u5c31\u662f\u4e3a\u4ec0\u4e48 70B \u578b\u53f7\u9002\u5408 4 GB\uff0c671B \u4e0a\u7684 DeepSeek-V3 \u9002\u5408\u7ea6 12 GB\uff0c\u800c Qwen3-235B \u9002\u5408\u7ea6 3 GB\u3002\u4f46\u6a21\u578b\u672c\u8eab\u7684\u5927\u5c0f\u4e3b\u8981\u53d7\u78c1\u76d8\u7a7a\u95f4\u7684\u9650\u5236\uff0c\u800c\u4e0d\u662f\u663e\u5361\u7684\u529f\u80fd\u3002<\/p>\n<p>\u8fd9\u4e9b\u5185\u5b58\u8282\u7701\u662f\u4ee5\u901f\u5ea6\u4e3a\u4ee3\u4ef7\u7684\uff1a\u751f\u6210\u6bcf\u4e2a\u4ee4\u724c\u65f6\u90fd\u4f1a\u4ece\u78c1\u76d8\u8bfb\u53d6\u6bcf\u4e2a\u5c42\uff0c\u56e0\u6b64\u8f93\u51fa\u5f88\u6162\uff0c\u6bcf\u4e2a\u4ee4\u724c\u5927\u7ea6\u9700\u8981\u51e0\u79d2\u3002\u56e0\u6b64\uff0cAirLLM \u4e0d\u518d\u662f\u5feb\u901f\u4e92\u52a8\u804a\u5929\uff0c\u800c\u662f\u751a\u81f3\u80fd\u591f\u542f\u52a8\u4e0d\u9002\u5408\u786c\u4ef6\u7684\u6a21\u578b\u3002<\/p>\n<h2>\u5b89\u88c5<\/h2>\n<p>\u8be5\u5e93\u662f\u4f7f\u7528\u5e38\u7528\u547d\u4ee4\u4ece PyPI \u5b89\u88c5\u7684\uff1a<\/p>\n<div class=\"hcb_wrap\">\n<pre class=\"prism undefined-numbers lang-unknown\" data-lang=\"unknown\"><code>pip install airllm\n<\/code><\/pre>\n<\/div>\n<p>\u9996\u6b21\u542f\u52a8\u65f6\uff0c\u6a21\u578b\u5c06\u4ece Hugging Face \u4e0b\u8f7d\u5e76\u5206\u89e3\u4e3a\u56fe\u5c42\u3002\u6b64\u8fc7\u7a0b\u5f88\u6162\u5e76\u4e14\u5360\u7528\u5927\u91cf\u78c1\u76d8\u7a7a\u95f4\uff0c\u56e0\u6b64\u60a8\u5e94\u8be5\u63d0\u524d\u51c6\u5907\u597d\u53ef\u7528\u7a7a\u95f4\u3002\u5982\u679c\u9700\u8981\uff0c\u60a8\u53ef\u4ee5\u542f\u7528 4 \u4f4d\u6216 8 \u4f4d\u538b\u7f29 &#8211; \u7136\u540e\u5c06\u5757\u91cf\u5316\u5e94\u7528\u4e8e\u6743\u91cd\uff0c\u8fd9\u5c06\u4f7f\u4ece\u78c1\u76d8\u52a0\u8f7d\u56fe\u5c42\u7684\u901f\u5ea6\u63d0\u9ad8\u7ea6\u4e09\u500d\uff0c\u5e76\u4e14\u7cbe\u5ea6\u4f1a\u635f\u5931\u5f88\u591a\u3002<\/p>\n<h2>\u57fa\u672c\u793a\u4f8b<\/h2>\n<p>\u60a8\u4f7f\u7528 AirLLM \u7684\u65b9\u5f0f\u4e0e\u4f7f\u7528 Transformer \u4e2d\u7684\u5e38\u89c4\u6a21\u578b\u51e0\u4e4e\u76f8\u540c\u3002\u5728Hugging Face\u4e0a\u8f93\u5165\u4e00\u6b21\u6a21\u578bID\u5c31\u8db3\u591f\u4e86\uff0c\u7136\u540e\u4e00\u5207\u5c31\u4f1a\u5f88\u719f\u6089\u4e86\uff1a<\/p>\n<div class=\"hcb_wrap\">\n<pre class=\"prism undefined-numbers lang-unknown\" data-lang=\"unknown\"><code>from airllm import AutoModel\n\nMAX_LENGTH = 128\nmodel = AutoModel.from_pretrained(\"Qwen\/Qwen3-32B\")\n\ninput_text = ['What is the capital of United States?']\n\ninput_tokens = model.tokenizer(\n    input_text,\n    return_tensors=\"pt\",\n    return_attention_mask=False,\n    truncation=True,\n    max_length=MAX_LENGTH,\n    padding=False)\n\ngeneration_output = model.generate(\n    input_tokens['input_ids'].cuda(),\n    max_new_tokens=20,\n    use_cache=True,\n    return_dict_in_generate=True)\n\nprint(model.tokenizer.decode(generation_output.sequences[0]))\n<\/code><\/pre>\n<\/div>\n<p>AutoModel \u7c7b\u672c\u8eab\u51b3\u5b9a\u6a21\u578b\u7c7b\u578b\uff0c\u56e0\u6b64\u540c\u4e00\u884c\u53ef\u7528\u4e8e Llama\u3001Qwen \u548c DeepSeek\u3002\u5982\u679c\u4f60\u91c7\u7528\u50cf DeepSeek-V3 \u8fd9\u6837\u5177\u6709 671B \u53c2\u6570\u7684\u6a21\u578b\uff0c\u5b83\u4e5f\u5c06\u9002\u5408\u5927\u7ea6 12 GB \u7684\u89c6\u9891\u5185\u5b58 &#8211; \u8fd9\u5728\u5f88\u5927\u7a0b\u5ea6\u4e0a\u4f7f\u8be5\u9879\u76ee\u5177\u6709\u5438\u5f15\u529b\u3002<\/p>\n<h2>\u5728 macOS \u4e0a\u542f\u52a8<\/h2>\n<p>AirLLM \u4e0d\u4ec5\u53ef\u4ee5\u5728 CUDA \u4e0a\u8fd0\u884c\uff0c\u8fd8\u53ef\u4ee5\u5728 Apple Silicon \u4e0a\u8fd0\u884c\u3002\u5728 macOS \u4e0a\uff0c\u5b83\u4f7f\u7528 MLX \u540e\u7aef\uff0c\u56e0\u6b64\u9700\u8981\u5b89\u88c5 mlx \u548c torch\uff0c\u5e76\u4e14\u4ec5\u652f\u6301 Apple \u82af\u7247\u3002\u8fd8\u6709\u4e00\u4e2a\u5c0f\u9650\u5236\uff1aMLX \u5b9e\u73b0\u4ec5\u652f\u6301\u7c7b\u4f3c Llama \u7684\u67b6\u6784\uff0c\u56e0\u6b64 Qwen \u548c\u7c7b\u4f3c\u578b\u53f7\u65e0\u6cd5\u4ee5\u8fd9\u79cd\u65b9\u5f0f\u5728 MacBook \u4e0a\u542f\u52a8\u3002<\/p>\n<h2>\u4f7f\u7528\u793a\u4f8b<\/h2>\n<p>AirLLM \u5b9e\u9a8c\u662f\u4e24\u4e2a\u5c0f\u578b\u4ea4\u4e92\u5f0f\u63a7\u5236\u53f0\u5b9e\u7528\u7a0b\u5e8f\uff0c\u7528\u4e8e\u5728\u672c\u5730\u8fd0\u884c\u5927\u578b\u8bed\u8a00\u6a21\u578b\u3002\u5b83\u4eec\u65e0\u9700\u6bcf\u6b21\u90fd\u7f16\u5199\u76f8\u540c\u7684\u4ee3\u7801\u6765\u52a0\u8f7d\u6a21\u578b\u548c\u7ec4\u7ec7\u804a\u5929\u3002<\/p>\n<p>\u7b2c\u4e00\u4e2a\u5de5\u5177airllm-lib-usage.py\u76f4\u63a5\u5728\u5f53\u524dPython\u8fdb\u7a0b\u4e2d\u8fd0\u884cAirLLM\u5e93\u3002\u5b83\u663e\u793a\u603b\u76ee\u5f55\u4e2d\u6a21\u578b\u7684\u7f16\u53f7\u5217\u8868\uff0c\u5982\u6709\u5fc5\u8981\uff0c\u5b83\u4f1a\u5b89\u88c5\u7f3a\u5c11\u7684\u4f9d\u8d56\u9879\u5e76\u6253\u5f00\u804a\u5929\u4f1a\u8bdd\u3002 MLX\u8fd0\u884c\u65f6\u5728macOS\u4e0a\u4f7f\u7528\uff0ctorch\u5728\u5176\u4ed6\u5e73\u53f0\u4e0a\u4f7f\u7528\u3002<\/p>\n<p>\u7b2c\u4e8c\u4e2a\u5de5\u5177\u662fairllm-openai-server-client.py\uff0c\u53ef\u5e2e\u52a9\u60a8\u4f7f\u7528airllm-openai-server\u670d\u52a1\u5668\uff0c\u8be5\u670d\u52a1\u5668\u5728Docker\u4e2d\u8fd0\u884c\u5e76\u63d0\u4f9b\u4e0eOpenAI\u517c\u5bb9\u7684API\u3002\u5f53\u60a8\u7b2c\u4e00\u6b21\u542f\u52a8\u5b83\u65f6\uff0c\u8be5\u5b9e\u7528\u7a0b\u5e8f\u4f1a\u8981\u6c42\u8bbe\u7f6e\u3001\u6536\u96c6\u56fe\u50cf\u3001\u62fe\u53d6\u5bb9\u5668\u5e76\u5411\u6b63\u5728\u8fd0\u884c\u7684\u670d\u52a1\u5668\u6253\u5f00\u6d41\u5f0f\u804a\u5929\u3002\u5f53\u91cd\u590d\u542f\u52a8\u65f6\uff0c\u5b83\u4f1a\u627e\u5230\u4e00\u4e2a\u73b0\u6709\u7684\u5bb9\u5668\u5e76\u7b80\u5355\u5730\u91cd\u7528\u5b83\uff0c\u5e76\u5c06\u6a21\u578b\u7f13\u5b58\u5b58\u50a8\u5728\u5355\u72ec\u7684 Docker \u5377\u4e2d\uff0c\u4ee5\u514d\u518d\u6b21\u4e0b\u8f7d\u6a21\u578b\u3002<\/p>\n<p>\u53ef\u7528\u6a21\u578b\u7684\u5217\u8868\u5305\u542b\u5728\u516c\u5171\u6a21\u5757 model_catalog.py \u4e2d\uff0c\u56e0\u6b64\u8fd9\u4e24\u4e2a\u5de5\u5177\u90fd\u53ef\u4ee5\u4f7f\u7528\u540c\u4e00\u7ec4\u6a21\u578b\u3002\u804a\u5929\u547d\u4ee4\u4e5f\u5f88\u5e38\u89c1\uff1a\/help\u3001\/clear\u3001\/exit\u3002\u6e90\u4ee3\u7801\u548c\u8bf4\u660e\u4f4d\u4e8e\u5b58\u50a8\u5e93\u4e2d\uff1a<\/p>\n<p><a href=\"https:\/\/github.com\/zefir1990\/AirLLM-experiments\" rel=\"noopener\" target=\"_blank\">https:\/\/github.com\/zefir1990\/AirLLM-experiments<\/a><\/p>\n<h2>\u9700\u8981\u6ce8\u610f\u4ec0\u4e48<\/h2>\n<p>\u7b2c\u4e00\u4e2a\u54cd\u5e94\u53ef\u80fd\u9700\u8981\u51e0\u5206\u949f\u7684\u65f6\u95f4\u6765\u51c6\u5907\uff1a\u9996\u5148\uff0cAirLLM \u4e0b\u8f7d\u6a21\u578b\u5e76\u5c06\u5176\u5206\u5c42\uff0c\u7136\u540e\u624d\u5f00\u59cb\u751f\u6210\u3002\u6b64\u5916\uff0c\u901f\u5ea6\u4ecd\u7136\u5f88\u4f4e\u2014\u2014\u8fd9\u662f\u4e3a\u4e86\u8282\u7701\u5185\u5b58\u800c\u6545\u610f\u505a\u51fa\u7684\u59a5\u534f\u3002\u5bf9\u4e8e\u50cf meta-llama \u8fd9\u6837\u7684\u95e8\u63a7\u6a21\u578b\uff0c\u60a8\u9700\u8981\u63a5\u53d7 Hugging Face \u4e0a\u7684\u6761\u6b3e\u5e76\u4f20\u9012\u8bbf\u95ee\u4ee4\u724c\u3002\u6700\u597d\u4e0d\u8981\u5220\u9664\u4e0b\u8f7d\u7684\u6a21\u578b\u548c\u5206\u7247\u7684\u7f13\u5b58\uff0c\u5426\u5219\u6240\u6709\u5185\u5bb9\u90fd\u5fc5\u987b\u91cd\u65b0\u4e0b\u8f7d\u3002<\/p>\n<h2>\u94fe\u63a5<\/h2>\n<p><a href=\"https:\/\/github.com\/lyogavin\/airllm\" rel=\"noopener\" target=\"_blank\">https:\/\/github.com\/lyogavin\/airllm<\/a><br \/>\n<a href=\"https:\/\/github.com\/mkamranr\/airllm-openai-server\" rel=\"noopener\" target=\"_blank\">https:\/\/github.com\/mkamranr\/airllm-openai-server<\/a><br \/>\n<a href=\"https:\/\/github.com\/zefir1990\/AirLLM-experiments\" rel=\"noopener\" target=\"_blank\">https:\/\/github.com\/zefir1990\/AirLLM-experiments<\/a><br \/>\n<a href=\"https:\/\/pypi.org\/project\/airllm\/\" rel=\"noopener\" target=\"_blank\">https:\/\/pypi.org\/project\/airllm\/<\/a><br \/>\n<a href=\"https:\/\/huggingface.co\/\" rel=\"noopener\" target=\"_blank\">https:\/\/huggingface.co\/<\/a><\/p>\n<h2>\u6765\u6e90<\/h2>\n<p><a href=\"https:\/\/github.com\/lyogavin\/airllm\" rel=\"noopener\" target=\"_blank\">https:\/\/github.com\/lyogavin\/airllm<\/a><br \/>\n<a href=\"https:\/\/github.com\/zefir1990\/AirLLM-experiments\" rel=\"noopener\" target=\"_blank\">https:\/\/github.com\/zefir1990\/AirLLM-experiments<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u901a\u5e38\uff0c\u8fd0\u884c\u5177\u6709 700 \u4ebf\u4e2a\u53c2\u6570\u7684\u5927\u578b\u8bed\u8a00\u6a21\u578b\u9700\u8981\u6570\u5341 GB \u7684\u89c6\u9891\u5185\u5b58\u3002 AirLLM \u66f4\u51b7\u9759\u5730\u5904\u7406\u8fd9\u4e2a\u95ee\u9898\uff0c\u5e76\u63d0\u4f9b\u4e86\u4e00\u79cd\u4e0d\u540c\u7684\u65b9\u5f0f\uff1a\u8fd9\u6837\u7684\u6a21\u578b\u53ef\u4ee5\u5728\u53ea\u6709 4 GB \u5185\u5b58\u7684\u663e\u5361\u4e0a\u8fd0\u884c\uff0c\u5e76\u4e14\u4e0d\u9700\u8981\u91cf\u5316\u3001\u84b8\u998f\u548c\u4fee\u526a\u3002\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u5c06\u5c1d\u8bd5\u4e0d\u614c\u4e0d\u5fd9\u5730\u544a\u8bc9\u60a8 AirLLM \u662f\u5982\u4f55\u5de5\u4f5c\u7684\uff0c\u5982\u4f55\u4f7f\u7528\u5b83\uff0c\u5e76\u4e14\u6211\u5c06\u5206\u4eab\u6211\u7684\u5c0f\u9879\u76ee AirLLM-experiments\uff0c\u8fd9\u4f7f\u5f97\u4f7f\u7528\u8fd9\u4e2a\u5e93\u66f4\u52a0\u65b9\u4fbf\u3002 \u4ec0\u4e48\u662f AirLLM AirLLM \u662f\u7531 Gavin Li \u7f16\u5199\u7684\u5927\u578b\u8bed\u8a00\u6a21\u578b\u63a8\u7406\u5f00\u6e90\u5e93\u3002\u5b83\u7684\u60f3\u6cd5\u7b80\u5355\u800c\u4f18\u96c5\uff1a\u8be5\u5e93\u4e00\u6b21\u4ec5\u5c06\u4e00\u5c42\u52a0\u8f7d\u5230 GPU 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