Strata v0.1.37 发布：静默引擎检测、Windows AMD 显存预算与安装修复

Official release notes (https://github.com/Niko1221/Strata/releases/tag/v0.1.37) **A frozen engine no longer hangs the server, Windows AMD counts the desktop's VRAM, and a batch of setup fixes from your reports.** **The server notices a silent engine (#481):** if the engine prints nothing during a request for `engine_silence_s` seconds (default 300, longer while a long prompt is being read; 0 turns it off), or never answers a stop, the server ends the request with an error and the next request restarts the engine. Before, both sides could wait for each other forever. The cause of the reported freeze is still being traced with the reporter. **AMD on Windows (#497, PRs #380 and #377 by BlueKingMuch):** - The engine counts the desktop's VRAM (the WDDM budget), so the expert cache no longer spills into system RAM (an RX 6800: 31 -> 42 tokens/s). `STRATA_WDDM_BUDGET=0` turns it off. - The PCIe probe is timed on the host there (it read impossible values before). **A steadier PCIe probe (#485):** the best of four timed bursts, and the share of missed experts read over PCIe follows the measured speed smoothly below 20 GB/s instead of jumping. Above 20 GB/s nothing changes. **Setup:** - **UD-Q4_K_XL on several GPUs (#498):** with enough RAM (~135 GB), setup offers the layer split; `--gpus` is honoured. A start with `--gpus` on a RAM-budget config no longer fails. - **Mirrors and manual downloads (#495):** `HF_ENDPOINT` is honoured (e.g. `https://hf-mirror.com`), also for the draft layer's download. Setup prints the folder and file names it expects for files you already have. - **`--vram-reserve-mib N` (#493):** keeps N MiB of VRAM free for other programs; documented in INSTALL.md. - **Start errors (#496):** when the engine stops before it is ready, the error shows the end of its log. - **Messages (#505):** the start messages match the mode (mapped, resident, RAM budget). **Checked before the release:** - **The same answers as 0.1.36 on all four quants** (Q2_0, IQ3_XXS, IQ3_S, the Coder), 10/10 each, and the prompt path's internal state at 4K and 20K tokens. - **Speed:** - the 5-pair A/B on Q2_0 and IQ3_S: +0.9 / -0.1 / +0.9 / +0.3%, the same expert slots; - a decode re-check at 32K and 128K (3 + 2 alternating pairs): the same speed. - **Kernel parity tests**, **real use at a 57K-token prompt** (Q2_0, the Coder), **Linux** (WSL) Q2_0 identical 10/10. - **AMD on Windows:** the HIP zip builds. The two PRs are reviewed and Windows-HIP-only, but they are untested on an AMD card here (we have none); BlueKingMuch measured them on an RX 6800. - **Tests:** tools/setup 277, server 172. **Updating:** run `UPDATE.bat` (Linux: `./update.sh`). Setup installs engine 0.1.37. The ready-made Strata engines for Windows, which `START-HERE.bat` fetches by itself: - `strata-windows-x64.zip`: NVIDIA (RTX 20 / 30 / 40 / 50: sm_75, sm_86, sm_89, sm_120 + PTX), CUDA 13.0, needs an NVIDIA driver 580 or newer. Contents: `strata.exe`, `strata-vision.exe` (the optional image encoder), `BUILD.json`. - `strata-windows-x64-hip.zip`: AMD (gfx1100, gfx1101, gfx1102, gfx1200, gfx1201, gfx1030), ROCm 10.2.0a20260930 from AMD's TheRock builds, needs a current AMD driver. Contents: `strata.exe`, `strata-device.exe`, the HIP runtime next to them, `BUILD.json`, `rocm\` (the ROCm libraries and their licenses). --- Strata is free and open source. If it runs well on your PC, a coffee keeps the work on it going: Repository README (current repository documentation, not release changes) Strata 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](https://huggingface.co/Qwen/Qwen3.8-Flash-Next)** - a large, smart AI model that normally needs a server - on a normal PC. It chats, writes code, reads pictures and works with your apps and coding agents, and nothing leaves your PC. ## How fast is it? Measured on two ordinary gaming PCs. "Writes answers" is how fast the reply appears in a short chat; "reads your prompt" is how fast it takes in what you send (a 32K-token document, code or chat history). A token is about ¾ of a word, so 60 tokens per second is faster than you can read. 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 | A card with more VRAM is faster: an RTX 3090 (24 GB) should write roughly 100-140 tokens per second. Long chats, other cards: [speed of each model](docs/MODELS.md#how-fast-is-each-size), [community results](docs/COMMUNITY_BENCHMARKS.md). 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 - with **12 GB of VRAM or more** | | **RAM** | 32 GB or more - how much decides [which model](#which-model-should-i-pick) fits; 64 GB runs every size | | **Disk** | about 80 GB free, on 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 | Everything else is installed for you. Two or three cards can share the model ([multi-GPU](docs/MULTI_GPU.md)). The full list: [docs/INSTALL.md](docs/INSTALL.md#what-you-need). ## Install ### Let your AI set it up Use an AI coding assistant (Claude Code, Cursor, Codex, GitHub Copilot, ...)? Paste this into it: ```text 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, picks the model that fits, installs it, starts it and tells you how to connect your apps. AI tools can also install, start and stop Strata themselves through its [MCP server](docs/MCP_SERVER.md). ### Or do it yourself [Download Strata](https://github.com/Niko1221/Strata/archive/refs/heads/main.zip) and unzip it (or `git clone` it). **Windows:** double-click **`START-HERE.bat`**. **Linux:** run **`./setup.sh`** in the Strata folder. The same steps for NVIDIA and AMD: the installer finds your card and sets up the right engine for it. It asks which model, which size, how much context (how much text it keeps in mind) and whether it should read pictures - press Enter each time for the recommended answer. Then it downloads the model (~70 GB; you can stop and it continues where it left off) and starts it. 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. That's normal - wait, and don't close the > window. The window tells you what it is doing. **Next time**, run `START-HERE.bat` (or `./setup.sh`) again: it starts right away, nothing is downloaded 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](docs/INSTALL.md). ## Which model should I pick? The installer recommends one for your RAM. The same model comes in sizes that are compressed more or less: smaller is faster, larger is 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 4-bit (experimental) | room for the largest sizes with everything else open | - **[Coder](docs/MODELS.md#coder)** - a coding version with half of the experts removed: 91% of the full model's SWE-bench Verified score (by its authors), fits 32 GB of RAM. 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](docs/MODELS.md#swift-15)** - a fine-tune that thinks much shorter before it answers, so you get the answer sooner, at about the same quality. - **[Unsloth UD-Q4_K_XL](docs/MODELS.md#unsloth-ud-q4_k_xl-experimental)** (experimental) - the closest to the full model, but most of it is read from the SSD while it answers: 7-8.5 tokens/s on a 64 GB PC. - **[OrcaRouter's Uncensored IQ3_XXS](docs/MODELS.md#orcarouter-uncensored-iq3_xxs)** - a manual setup, not in the installer's menu. Sizes, downloads and what fits where: [docs/MODELS.md](docs/MODELS.md). You can add another model later with `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:** `http://127.0.0.1:8080` - **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 base URL **`http://127.0.0.1:8080/v1`**, any API key and any model name. 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`). - **Thinking:** choose **off, low, medium or high** in the chat menu or your app's "reasoning effort". Off is fastest; high is best for hard questions. - **Pictures:** say yes to "Images?" in setup, then click **Picture** in the chat, or attach them in your app (AMD cards: on Linux through the processor, not on Windows yet). - **From your phone or another PC:** `START-HERE.bat --setup --host 0.0.0.0 --api-key ` - always with a key. - **Good to know:** it answers one request at a time. The first message of a chat is read in full (about 1 minute per 30,000 tokens); follow-ups start in seconds. More: [where your chats are stored](docs/INSTALL.md#where-things-are-stored), [the API](docs/DETAILS.md#using-it). ## Something went wrong? - **My PC froze the first time Strata started.** Normal while it loads the model: wait, 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 "the engine stopped unexpectedly".** Not 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](docs/TROUBLESHOOTING.md). Still stuck? Open an [issue](https://github.com/Niko1221/Strata/issues) and attach `strata-.log` from the Strata folder. ## How does it work? Models like this one normally run on servers with hundreds of gigabytes of graphics memory. Your graphics card has 12-24 GB. Strata makes it fit by **sharing the work across your whole PC** - like a kitchen, where the things you use all the time stay on the counter and the rest waits in the pantry. - **The model is a team of 24,576 small specialists ("experts"),** and each word needs only 10 of them. - **Your graphics card** keeps the few thousand experts that are asked 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. - **Guess, then check:** a small helper guesses the next few words and the big model checks them all at once, so 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): over 1,000 tokens per second. The longer explanation: [docs/HOW_IT_WORKS.md](docs/HOW_IT_WORKS.md). Every part and its numbers: [the details](docs/DETAILS.md#how-it-works) and the [paper](docs/paper/Strata-Paper.pdf). ## Credits and license The model is [Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next) by the Qwen team, compressed by [ISTA-DASLab](https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF), UkisAI (Swift 1.5) and Unsloth; Strata is built with parts of [llama.cpp / ggml](https://github.com/ggml-org/llama.cpp). All credits: [docs/HOW_IT_WORKS.md](docs/HOW_IT_WORKS.md#credits). Strata is open source under the [MIT License](LICENSE); a few parts and every model carry their own licenses ([which ones](docs/HOW_IT_WORKS.md#license)). ## Support Strata Strata is free and open source. If it is useful to you, you can support its development: