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  "text": "千萬3.827B就是開源模型的Deep Seek時刻。它性能好、體積小、個人也能部署,實現很不錯的效果。\n最近幾天我研究了各種本地部署的方案,過程都有發在知識星球NewType裡面。那本期視頻我來做一個總結。\n咱們從一個基礎問題開始。在本地跑開源大模型需要什麼樣的配置?\n重點看兩個指標。\n第一,容量。也就是先看統一內存或者GPU顯存的容量。模型運行的時候,需要把權重裝進GPU顯存或者統一內存。\n如果裝不下,要麼無法運行,要麼就只能把部分的權重放進系統內存,通過CPU或者PCIe讀取。那這麼一來,速度就會大幅下降。\n用大白話來說就是,內存或者顯存容量決定了你能跑多大尺寸、多高精度的模型。\n舉個例子,一個27B的模型如果使用BF16的精度,那麼光是權重就需要大約50GB。\n量化到8bit之後,可以降到30GB左右。\n而量化到4bit之後,只需要17-20GB。\n除了權重之外,系統本身推理框架上下紋,它們都需要占有內存。\n所以4bit的模型,你光有17GB還不夠。大概得要20GB才能勉強能跑。\n第二,帶框。\n把模型裝進內存之後,第二個關鍵指標就是內存帶框。\n容量決定了你能不能跑,而帶框決定模型生成有多快。\n注意,內存容量大,並不意味著帶框就高。\n比如英偉達的DGX Spark,它有128GB,比RTX Pro 6000的就是6GB還大。\n看起來很大很划算對吧?\n但是它的內存帶框只有273GB,遠小於6000的1792。\n這麼低的帶框意味著,DGX Spark雖然能裝下很大的模型,\n但是跑大型Dance模型的時候,生成速度不會太快。\n它更適合MOE模型,因為MOE的總參數雖然很大,\n但每次生成Token的時候,它只激活其中的一小部分。\n你看,我們星球裡就有一個小夥伴,買了兩台,用來跑Deep Seek V4 Flash,速度有30多,夠用了。\n容量和帶框是理解本地推理最重要的兩個指標,但它們不是全部。\n你實際的性能還會受到GPU的數量力、模型架構和推理框架的影響。\n但是對於普通人來說,先抓住這兩個核心指標完全沒有問題。\nOK,理解了原理之後,那選擇就容易多了。\n咱們有三個選擇。\n第一,Mac。\n蘋果芯片用的是統一記憶,也就是CPU和GPU共享一個記憶池。\n因此,買Mac的話,可以用相對較低的價格獲得更高的記憶。\n但是,它的不足之處在於,帶框沒有那麼高,只有5090和6000的一半甚至更低。\n所以,即使Mac擁有同樣甚至更多的記憶,在運行Dance模型的時候,速度不會太快。\n而這個是需要咱們取捨的地方。\n第二,RTX5090。\n5090擁有3.4G顯存和1792GB每秒的記憶體帶框,非常適合千萬3.827B這樣的模型。\n所以,如果你的目標只是本地運行千萬3.827B的話,那一張5090通常會比Mac Studio更加合適。\n如果需要多Agent并發的話,可以上兩張5090。\n注意啊,5090沒有NV-Link。\n你非要跨卡通信的話,要走PCIE,效率不會像一塊原生的64G顯卡那麼高。\n所以,插兩張卡的目的,是讓每張卡都運行一個完整的模型。\n第一張卡用來處理一部分的Agent。\n第二張卡處理另一部分的Agent。\n它特別適合短或者中上下文,高并發多Agent任務的情況。\n5090雖然夠快,但它容量只有32G。\n你跑27B的模型沒有問題。\n如果要運行70B的模型超長上下文,或者同時常駐多個模型,\n那這樣的顯存就不夠了。\n這是它的不足之處。\n第三,RTX PRO 6000。\n這是最完整的本地AI方案。\n這張卡擁有96G顯存和1792GB每秒的帶寬。\n同時具備大容量和高帶寬。\n意味著它特別適合長上下文多模型常駐和生產及Agent服務。\n而且RTX PRO 6000還支持ECC和專業驅動,更適合長期運行。\n但是,它有一個很大的不足。\n太貴了。\n你去收下價格就知道了。\n剛才我介紹的三種方案,價格跨度非常大。\n從幾萬到十幾二十萬都有。\n所以,沒有一種硬件在所有維度都最好。\n你只能做取捨。\n大家目前用什麼樣的配置跑本地模型,效果如何?\n歡迎在評論區留言。\nOK,以上就是本期內容。\n想了解AI想奪回個體主權,\n想找到自從盜合的人就來我們Newtype社群。\n那咱們下期見。\n請問一下,請問一下。\n請問一下,請問一下。\n請問一下,請問一下。",
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      "text": "千萬3.827B就是開源模型的Deep Seek時刻。它性能好、體積小、個人也能部署,實現很不錯的效果。",
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      "text": "最近幾天我研究了各種本地部署的方案,過程都有發在知識星球NewType裡面。那本期視頻我來做一個總結。",
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      "text": "咱們從一個基礎問題開始。在本地跑開源大模型需要什麼樣的配置?",
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      "text": "重點看兩個指標。",
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      "text": "第一,容量。也就是先看統一內存或者GPU顯存的容量。模型運行的時候,需要把權重裝進GPU顯存或者統一內存。",
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      "text": "用大白話來說就是,內存或者顯存容量決定了你能跑多大尺寸、多高精度的模型。",
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      "text": "舉個例子,一個27B的模型如果使用BF16的精度,那麼光是權重就需要大約50GB。",
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      "text": "量化到8bit之後,可以降到30GB左右。",
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      "text": "而量化到4bit之後,只需要17-20GB。",
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      "text": "除了權重之外,系統本身推理框架上下紋,它們都需要占有內存。",
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      "text": "所以4bit的模型,你光有17GB還不夠。大概得要20GB才能勉強能跑。",
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      "text": "第二,帶框。",
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      "text": "把模型裝進內存之後,第二個關鍵指標就是內存帶框。",
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      "text": "容量決定了你能不能跑,而帶框決定模型生成有多快。",
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      "text": "比如英偉達的DGX Spark,它有128GB,比RTX Pro 6000的就是6GB還大。",
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    {
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      "text": "看起來很大很划算對吧?",
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      "text": "但是它的內存帶框只有273GB,遠小於6000的1792。",
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      "text": "這麼低的帶框意味著,DGX Spark雖然能裝下很大的模型,",
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      "text": "但是跑大型Dance模型的時候,生成速度不會太快。",
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      "text": "它更適合MOE模型,因為MOE的總參數雖然很大,",
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      "text": "但每次生成Token的時候,它只激活其中的一小部分。",
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      "text": "你看,我們星球裡就有一個小夥伴,買了兩台,用來跑Deep Seek V4 Flash,速度有30多,夠用了。",
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      "text": "容量和帶框是理解本地推理最重要的兩個指標,但它們不是全部。",
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      "text": "你實際的性能還會受到GPU的數量力、模型架構和推理框架的影響。",
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      "text": "但是對於普通人來說,先抓住這兩個核心指標完全沒有問題。",
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      "text": "OK,理解了原理之後,那選擇就容易多了。",
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      "text": "咱們有三個選擇。",
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      "text": "第一,Mac。",
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      "text": "蘋果芯片用的是統一記憶,也就是CPU和GPU共享一個記憶池。",
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      "text": "因此,買Mac的話,可以用相對較低的價格獲得更高的記憶。",
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      "text": "但是,它的不足之處在於,帶框沒有那麼高,只有5090和6000的一半甚至更低。",
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      "text": "所以,即使Mac擁有同樣甚至更多的記憶,在運行Dance模型的時候,速度不會太快。",
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      "text": "而這個是需要咱們取捨的地方。",
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      "text": "第二,RTX5090。",
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      "text": "5090擁有3.4G顯存和1792GB每秒的記憶體帶框,非常適合千萬3.827B這樣的模型。",
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      "text": "所以,如果你的目標只是本地運行千萬3.827B的話,那一張5090通常會比Mac Studio更加合適。",
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      "text": "如果需要多Agent并發的話,可以上兩張5090。",
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      "text": "注意啊,5090沒有NV-Link。",
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      "text": "你非要跨卡通信的話,要走PCIE,效率不會像一塊原生的64G顯卡那麼高。",
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      "text": "所以,插兩張卡的目的,是讓每張卡都運行一個完整的模型。",
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      "text": "第一張卡用來處理一部分的Agent。",
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      "text": "第二張卡處理另一部分的Agent。",
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      "text": "它特別適合短或者中上下文,高并發多Agent任務的情況。",
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      "text": "你跑27B的模型沒有問題。",
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      "text": "如果要運行70B的模型超長上下文,或者同時常駐多個模型,",
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      "text": "那這樣的顯存就不夠了。",
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      "text": "這是它的不足之處。",
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      "text": "第三,RTX PRO 6000。",
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      "text": "這是最完整的本地AI方案。",
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      "text": "這張卡擁有96G顯存和1792GB每秒的帶寬。",
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      "text": "意味著它特別適合長上下文多模型常駐和生產及Agent服務。",
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      "text": "太貴了。",
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      "text": "你去收下價格就知道了。",
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      "text": "剛才我介紹的三種方案,價格跨度非常大。",
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      "text": "從幾萬到十幾二十萬都有。",
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      "text": "所以,沒有一種硬件在所有維度都最好。",
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      "text": "你只能做取捨。",
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      "text": "大家目前用什麼樣的配置跑本地模型,效果如何?",
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    {
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      "text": "歡迎在評論區留言。",
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      "text": "OK,以上就是本期內容。",
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      "text": "想了解AI想奪回個體主權,",
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      "text": "想找到自從盜合的人就來我們Newtype社群。",
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      "text": "那咱們下期見。",
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      "text": "請問一下,請問一下。",
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      "text": "請問一下,請問一下。",
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      "text": "請問一下,請問一下。",
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