在消费级硬件(RTX 4090)上以 100T/s 的速度运行 Qwen 3.8 Flash Next(125B)。
Run Qwen 3.8 Flash Next (125B) on consumer hardware (RTX 4090) at 100T/s

原始链接: https://github.com/Niko1221/Strata

Strata 是一个免费的开源系统,可在一台游戏电脑上完整运行号称拥有 1250 亿参数的 Qwen3.8-Flash-Next 模型。它支持 Windows 或 Linux,兼容 NVIDIA 或 AMD 显卡;硬件要求包括至少 12 GB 显存、32 GB 以上内存,以及大约 80 GB 的可用 SSD 存储空间。通过让 GPU、CPU、内存和 SSD 协同处理模型任务,它可以在本地提供服务器级 AI 性能,且无需将数据发送到其他地方。 根据硬件配置、量化方式和模型大小,基准测试速度约为每秒 44~94 个输出 token。较小的量化版本速度更快,而质量更高的版本需要更多内存。编程专用版本可装入 32 GB 内存,但在中文及其他 CJK 文本上的表现相对较差。 安装过程很简单:下载 Strata,在 Windows 上运行 `START-HERE.bat`,或在 Linux 上运行 `./setup.sh`;选择推荐选项,然后等待约 70 GB 的模型下载完成。本地网页应用将在 `127.0.0.1:8080` 打开,提供聊天、系统监控、图像支持,以及兼容 OpenAI 和 Anthropic 的 API,可供编程代理和其他应用使用。 首次启动时,系统可能会将 35~55 GB 数据加载到内存中,导致电脑暂时卡顿。它支持多块 GPU、远程访问、API 密钥和并行请求。

Hacker News 上的讨论聚焦于 Strata,这是一款专用推理引擎,代码主要由 AI 生成。它可以在消费级硬件上运行 Qwen3.8 Flash Next——一个拥有 1250 亿参数、其中约 60 亿参数处于激活状态的 MoE 模型。提交者称,在配备 128GB 系统内存的 RTX 4090 上,速度约为每秒 124 个 token;其他用户则报告称,在 RTX 3080、3090、5090 系统、Radeon 硬件以及 Mac 上都能获得可用的速度。 对于模型质量,意见存在明显分歧许多人认为,Flash Next 在编程方面比 Qwen3.8 27B 更快、能力更强,有时甚至接近较早期的 Sonnet 或 Opus 模型。IQ3 和 IQ4 量化被广泛认为是可以使用的,这得益于专家缓存和激进的卸载策略。也有人报告称,该模型会出现幻觉、工具调用失效、视觉能力较差、长上下文错误、并发能力弱等问题,而且从 Q2、Q1 或专家剪枝版本升级后,性能会显著下降。 讨论还质疑,如果缺乏固定的准确率基准、合适的评测框架以及量化细节,仅凭宣传的速度是否真的有意义。据称,安装过程大约需要一个小时,冷启动时间约为 15 分钟。尽管存在怀疑,评论者普遍认为,专用引擎正在推动私人化、低成本的本地 AI 发展;不过,关于安全性、可维护性,以及 AI 生成的代码能否通过严格的人工审查,争论仍在继续。
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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 that Strata's model wrote, running in the browser
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.

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 RAMAMD: 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.

Buy Me A Coffee
Strata is free. If it runs well on your PC, a coffee keeps the work on it going.

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.

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.

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).

The Strata app's Monitor tab next to a coding agent
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.

  • 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.

More problems and their fixes: docs/TROUBLESHOOTING.md. Still stuck? Open an issue and attach strata-<model>.log from the Strata folder. Found a security problem? Report it privately: SECURITY.md.

Models like this one usually run on servers with hundreds of gigabytes of graphics memory. Your graphics card has 12-24 GB. Strata makes the model fit by sharing the work across your whole PC. Think of a kitchen: the things you use all the time stay on the counter, and the rest waits in the pantry.

The model's 24,576 experts: the busiest on the graphics card, all of them in RAM, a lookup table on the SSD

  • The model is a team of 24,576 small specialists ("experts"). Each word needs only 10 of them.
  • Your graphics card keeps the few thousand experts that are used most often. Your RAM holds all of them, and your processor works on the rest at the same time. Your SSD holds a big lookup table.

A small helper guesses the next words; the big model checks them all at once and keeps the right ones

  • Guess, then check: a small helper guesses the next few words. The big model checks them all at once. You get the same answer, 1.6-1.8x sooner.
  • Long texts are read in big pieces (up to 8,192 tokens at a time), at over 1,000 tokens per second.

The longer explanation: docs/HOW_IT_WORKS.md. Every part and its numbers: the details and the paper.

The model is Qwen3.8-Flash-Next by the Qwen team. It was compressed by ISTA-DASLab, UkisAI (Swift 1.5) and Unsloth. Strata uses parts of llama.cpp / ggml. All credits: docs/HOW_IT_WORKS.md. Strata is open source under the MIT License. A few parts and every model have their own licenses (which ones).

Strata is free and open source. If it is useful to you, you can support its development:

Buy Me A Coffee

联系我们 contact @ memedata.com