Run Qwen 3.8 Flash Next (125B) on consumer hardware (RTX 4090) at 100T/s
https://github.com/Niko1221/Strata📌 家用顯卡能跑 1250 億參數模型?Strata 實測效能曝光
TL;DR:開源工具 Strata 讓一張 12GB 顯卡在本機跑 Qwen3.8-Flash-Next(125B),完全不用上傳雲端。
通常一個千億參數等級的模型,想都別想塞進一張遊戲顯卡。但 Strata 這個開源專案宣稱,只要有一張 12GB 以上的 NVIDIA 或 AMD 顯卡,就能在自己的電腦上跑 Qwen3.8-Flash-Next,聊天、寫程式、讀圖片、接你的 coding agent,而且資料完全不離開本機。
🤔 為什麼千億模型能塞進消費級顯卡
關鍵在於 Qwen3.8-Flash-Next 本身是稀疏(MoE)架構加上大量量化。Strata 提供多種壓縮程度的模型版本(Q2_0、IQ2_XS、IQ3_XXS、IQ3_S 等),壓縮越多檔案越小、跑越快,但相對「聰明程度」會打折。安裝程式會自動偵測你的顯卡型號與記憶體,挑選對應的推理引擎(NVIDIA 與 AMD 走不同引擎路徑),並依照你的 RAM 容量推薦合適的版本。
🧩 怎麼用:一鍵安裝、OpenAI 相容 API
Windows 用戶雙擊 START-HERE.bat,Linux 用戶執行 ./setup.sh,安裝程式會問幾個問題(選哪個模型、多大的 context、要不要讀圖),全部按 Enter 選預設即可。下載約 70GB 模型後自動啟動,瀏覽器會打開 http://127.0.0.1:8080 的 Strata 介面,裡面有 Chat 頁面和即時顯示 GPU/CPU/RAM 用量的 Monitor。
若要接到自己的 App 或 coding agent,把 Base URL 設成 http://127.0.0.1:8080/v1(OpenAI 相容格式)即可,API Key 和模型名稱可以隨便填。Claude Code 之類用 Anthropic API 格式的工具,則改用 http://127.0.0.1:8080/v1/messages。工具也提供 MCP server,讓 AI 助手自己操作安裝、啟動、停止 Strata。
📊 實測速度:RTX 5070 與 RX 9070 XT
社群在兩臺一般遊戲電腦上實測,數字代表「生成速度」(每秒輸出幾個 token)與「讀取速度」(吃進 prompt 的速度):
NVIDIA RTX 5070(12GB VRAM):
| 量化版本 | 生成速度 | 讀取速度 |
|---|---|---|
| Q2_0 | 94 tokens/s | 2,650 tokens/s |
| IQ2_XS | 79 tokens/s | 2,090 tokens/s |
| IQ3_XXS | 62 tokens/s | 1,750 tokens/s |
| IQ3_S | 53 tokens/s | 1,620 tokens/s |
| Coder | 55 tokens/s | 2,180 tokens/s |
AMD RX 9070 XT(16GB VRAM):
| 量化版本 | 生成速度 | 讀取速度 |
|---|---|---|
| Q2_0 | 60 tokens/s | 1,160 tokens/s |
| IQ2_XS | 52 tokens/s | 1,110 tokens/s |
| Coder | 44 tokens/s | 1,420 tokens/s |
官方表示,VRAM 更大的卡會更快,例如 RTX 3090(24GB)「應該」能跑到每秒 100-140 token。60 tokens/s 已經比一般人閱讀速度還快。
⚠️ 限制與取捨
Coder 版本移除了一半的 expert,體積縮到 32GB RAM 就能跑,SWE-bench Verified 分數達到完整模型的 91%,但在程式碼以外的任務(包含中文等 CJK 文字)表現較弱。較舊的顯卡(Tesla P40、GTX 10 系列等)與 Intel Arc 需要社群自行編譯測試,屬實驗性支援;AMD 顯卡在 Windows 上目前還不能讀圖片,需切到 Linux 才能用處理器跑圖片辨識。首次啟動模型載入期間,電腦可能會卡頓 1-3 分鐘,這是正常現象。
🎯 實務啟示
如果你在意資料隱私、不想把 prompt 送上雲端 API,或單純想在本機測試大模型的可行性,Strata 把「裝引擎、選量化版本、接 API」這整套流程打包成一鍵安裝,對於沒有時間研究 llama.cpp 參數的工程師是直接可用的起點。但要清楚量化等級與效能是一條光譜,選版本前先衡量自己真正需要的是速度還是推理品質。
🔗 來源
- 標題:Run Qwen 3.8 Flash Next (125B) on consumer hardware (RTX 4090) at 100T/s
- 作者/機構:snehesht(Hacker News)
- 連結:https://github.com/Niko1221/Strata
#Strata #Qwen3 #LocalLLM #OpenSource #MoE #Quantization #LLMInference #ConsumerGPU #AIEngineering #SelfHosted
原始資料 Hacker News · 收集於 2026-10-04
摘要原文
Strata English · 簡體中文 · 日本語 · Deutsch · Français · Español · Português Run a 125-billion-parameter AI model on your own gaming PC NVIDIA or AMD graphics card (12 GB or more) · Windows or Linux · free and open source A voxel pagoda garden, 1 shot prompt running on an RTX 5070 with Strata (IQ3_S, 128K context) · full video (49 s) Strata runs Qwen3.8-Flash-Next on a normal PC. This is a large, smart AI model that usually needs a server. It chats, writes code, reads pictures and works with your apps and coding agents. Nothing leaves your PC. How fast is it? We measured it on two ordinary gaming PCs. A token is about ¾ of a word. Writes answers: how fast the reply appears in a short chat. 60 tokens per second is faster than you can read. Reads your prompt: how fast it takes in what you send (here a 32K-token document, code or chat history). NVIDIA: RTX 5070 (12 GB), Ryzen 5 7600, 64 GB RAM AMD: RX 9070 XT (16 GB), Ryzen 9 3900X, 47 GB RAM Size Writes answers Reads your prompt Q2_0 94 tokens/s 2,650 tokens/s IQ2_XS 79 tokens/s 2,090 tokens/s IQ3_XXS 62 tokens/s 1,750 tokens/s IQ3_S 53 tokens/s 1,620 tokens/s Coder 55 tokens/s 2,180 tokens/s Size Writes answers Reads your prompt Q2_0 60 tokens/s 1,160 tokens/s IQ2_XS 52 tokens/s 1,110 tokens/s Coder 44 tokens/s 1,420 tokens/s NVIDIA: Q2_0 with engine 0.1.36, the other rows with 0.1.26 (4K answers, 32K prompts). The full tables are in DETAILS.md . A card with more VRAM is faster: an RTX 3090 (24 GB) should write about 100-140 tokens per second. Long chats and other cards: speed of each model , community results . Strata is free. If it runs well on your PC, a coffee keeps the work on it going. What you need Graphics card NVIDIA GeForce RTX 20, 30, 40 or 50 series, or AMD Radeon RX 7900 XT / XTX, RX 7800 XT / 7700 XT, RX 9060 XT, RX 9070 / 9070 XT, Radeon AI PRO R9700 or RX 6800 / 6900 series. It needs 12 GB of VRAM or more . RAM 32 GB or more. Your RAM decides which model fits. 64 GB runs every size. Disk About 80 GB free. Use an SSD if you can: the first start is much faster. System Windows 10 / 11 or Linux, and a current graphics driver from NVIDIA or AMD. The installer sets up everything else. Two or three cards can share the model ( multi-GPU ). Experimental, written and tested by community members on their own machines: Older graphics cards (Tesla P40 / V100, GTX 10, Radeon VII / MI50, RX 6700 XT, RX 5500 XT): Older GPUs . Intel Arc , built from source on Linux: Intel Arc . Older processors without AVX2 : they work, but slowly. Older CPUs . The full list: docs/INSTALL.md . Install Let your AI set it up Do you use an AI coding assistant (Claude Code, Cursor, Codex, GitHub Copilot, ...)? Paste this into it: Set up Strata on this PC for me: https://github.com/Niko1221/Strata - follow docs/AI_SETUP.md in that repository. It checks your graphics card, RAM and disk and picks the model that fits. Then it installs and starts it and tells you how to connect your apps. AI tools can also install, start and stop Strata through its MCP server . Or do it yourself Download Strata and unzip it (or git clone it). Windows: double-click START-HERE.bat . Linux: run ./setup.sh in the Strata folder. The steps are the same for NVIDIA and AMD. The installer finds your card and sets up the right engine for it. It asks you a few questions: which model and which size, how much context (how much text the model keeps in mind), whether it should read pictures. Press Enter each time for the recommended answer. Then it downloads the model (about 70 GB) and starts it. If the download stops, run it again: it continues where it left off. Your browser opens the Strata app at http://127.0.0.1:8080 . While the model starts, your PC can be slow or stop responding for 1-3 minutes (longest the first time). Strata loads 35-55 GB into your RAM and locks part of it for the graphics card. This is normal. Wait, and don't close the window. The window shows what Strata is doing. Next time , run START-HERE.bat (or ./setup.sh ) again. It starts right away and downloads nothing twice. Close its window to stop the model. UPDATE.bat ( ./update.sh ) updates Strata without starting it. Updating, Docker, several cards, where the files go and every option: docs/INSTALL.md . Which model should I pick? The installer recommends one for your RAM. The same model comes in several sizes, compressed more or less. Smaller sizes are faster. Larger sizes are a bit smarter. Your RAM Take Why 32 GB Coder it fits 32 GB, and it is made for code (with a 24 GB card, Q2_0 and IQ2_XS run too) 48 GB IQ2_XS (or Q2_0, the fastest) the larger sizes do not fit 64 GB IQ2_XS (recommended), or IQ3_XXS / IQ3_S every size fits; IQ3_S is the best and the slowest 96 GB or more IQ3_S , or Unsloth's UD-IQ4_XS (~4-bit) room for the largest sizes with everything else open Coder : a coding version with half of the experts removed. It reaches 91% of the full model's SWE-bench Verified score (measured by its authors) and fits 32 GB of RAM. It is weaker outside code, including Chinese and other CJK text (#438). For those, take Q2_0, IQ2_XS or IQ3_S, which keep every expert. Swift 1.5 : a fine-tune that thinks for a much shorter time before it answers. You get the answer sooner, at about the same quality. Unsloth UD-IQ4_XS : Unsloth's ~4-bit version, between IQ3_S and UD-Q4_K_XL in quality. A 94 GB download. With less than ~80 GB of RAM, Strata reads part of it from the SSD while it answers, so it is slower there (an NVMe SSD helps). Unsloth UD-Q4_K_XL (experimental): the closest to the full model. But Strata reads most of it from the SSD while it answers, so it writes only 7-8.5 tokens/s on a 64 GB PC. OrcaRouter's Uncensored IQ3_XXS : you set it up by hand. It is not in the installer's menu. Sizes, downloads and what fits where: docs/MODELS.md . To add another model later, run SETUP.bat (Linux: ./setup.sh --setup ). Using it The Strata app's Monitor (left) while a coding agent writes the pagoda garden from the video (right) In the browser: open http://127.0.0.1:8080 . It has Chat , a live Monitor of the model and your GPU/CPU/RAM, and About with the settings and addresses. Your apps and coding agents: add an "OpenAI-compatible" provider with the base URL http://127.0.0.1:8080/v1 . Any API key and any model name work. Apps that use Anthropic's API: http://127.0.0.1:8080/v1/messages (Claude Code: ANTHROPIC_BASE_URL=http://127.0.0.1:8080 ). Codex CLI and other apps that use the OpenAI Responses API: /v1/responses ( setup ). Thinking: choose off, low, medium or high in the chat menu or in your app's "reasoning effort". Off is the fastest. High is best for hard questions. Pictures: say yes to "Images?" in setup. Then click Picture in the chat, or attach pictures in your app. AMD cards read pictures on Linux through the processor; on Windows they can't yet. From your phone or another PC: START-HERE.bat --setup --host 0.0.0.0 --api-key <secret> . Always set a key. One request at a time: by default Strata answers one request, and the others wait. To answer several at once, set "parallel": 2 ( BATCHING.md ). On a 12 GB card this makes each answer slower. Long prompts: Strata reads the first message of a chat in full, about 1 minute per 30,000 tokens. Follow-up messages start in seconds. More: where your chats are stored , the API . Something went wrong? My PC froze the first time Strata started. This is normal while it loads the model. Wait, and don't close the window. Still frozen after 10 minutes? Restart the PC, close other programs and try again, or pick a smaller size. It stopped while downloading or installing. Run START-HERE.bat (or ./setup.sh ) again. It continues where it stopped. It's very slow and the disk light keeps blinking, or it says "the engine stopped unexpectedly". Your PC does not have enough free RAM. Close other programs (browsers use a lot), or pick a smaller size (Q2_0 or IQ2_XS). It says port 8080 is already in use. Strata is already running. Look for its window. (436 points, 233 comments on Hacker News)
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