Strata v0.1.30 发布：短提示词提速最高 28%，支持多 GPU 与 AMD RDNA4

Official release notes (https://github.com/Niko1221/Strata/releases/tag/v0.1.30) **Short prompts up to 28% faster (the same answers), multi-GPU and AMD RDNA4 improvements, a server that can give the GPU back, and several opt-in features.** **Faster short prompts.** Prompts of 1K to 4K tokens read all experts from the stream from 1,024-token chunks (0.1.29: 2,048): +17-28% prompt speed on this RTX 5070, with the same output (suggested in the issues). `STRATA_PREFILL_STREAM_MIN` overrides the threshold. **Multi-GPU (#216, @gopinath87607).** In a layer split: - Each card now keeps only its own layers' session state, which saved 0.93 GiB on a later card in the author's 4-GPU test. - Each card lends the prompt path the tail of its own expert cache, instead of every card after the first holding back 1 GiB for the whole session. `--prefill auto` can then pick larger chunks (8,192 tokens instead of 2,048 there). - The Monitor's expert cache counts every card (it showed only the first). - Snapshots and checkpoints follow the split: a card saves and restores only its own layers. Single-GPU runs are unchanged (byte-identical). **AMD RDNA4 (#178, @doplxyz).** The RX 9070 / 9070 XT and the Radeon AI PRO R9700 (gfx1201) are supported beside the RX 7900 series: setup picks ROCm and the settings per card, and the engine checks at startup that it was built for the card it runs on. Validated on doplxyz's machine with both cards. The live server tests pass on each. Two AMD cards also run as one layer split from a hand-written config (setup installs one AMD card). It gives exactly the output of one card when the experts sit in the same place; see `docs/AMD_HIP.md`. **The server can give the GPU back (#208, @bytethecookie).** - `--idle-unload 600` unloads the model after 600 s without requests; the next request loads it again. - `POST /unload` and `POST /load` do it on demand. - `--min-free-vram-mib N` loads only when that much VRAM is free (else 503), and `--before-load "cmd"` runs a command first (e.g. one that unloads another server's model). **Low-RAM mode, resident variant.** When the experts don't fit the RAM but the ones the GPU doesn't hold do, setup now copies exactly those into RAM at start (`--resident-experts`) instead of reading them from the file. The same output. On a cold file cache the prompt went from 150 to 1,197 tok/s and answers were 26% faster. **Opt-in and experimental (off unless you turn them on):** - **Contexts past the trained 262K (#84, @j-luwierski):** `--rope-scaling yarn|linear` and `--rope-scale F`, or setup's `--context` past 262144. Experimental: at 320K, facts planted at 300K were found at 10%, 50% and 90% depth. See `docs/DETAILS.md` for the measured quality. - **Several conversations kept at once (#189, @jeremiahritchey):** `--conversation-cache-mib 8192 --conversation-cache-slots 4` parks up to four conversations in RAM, so switching back doesn't re-read the prompt. Not yet with a layer split. With the option off, the engine is byte-identical to 0.1.29. - **Greedy output independent of drafting (#152, reported by @clapbr):** `STRATA_IQ_MT_MIN=1` in the config's `env` makes the IQ models' CPU experts round the same whatever the verify window held. Costs 1-3% decode on IQ3_S (AVX-512); the default is unchanged. - **Coupled draft sampling:** `STRATA_SPEC_COUPLED=1` samples drafts and the verifier from one random stream. No clear gain in our tests, so it stays off. - **Docker (#96, @djmaze):** `docker build -t strata .` builds the engine in the image. The server is PID 1 and stops cleanly on SIGTERM; KV, GPUs and low-RAM mode are set with environment variables. See the README. - **Pascal cards (#124, @ruibeikaa):** the engine builds and runs on compute capability 6.0 (P100) with exact fallbacks for `__dp4a` and `__nanosleep`. Not in the ready-made engine; compile it yourself. Not tested here (no Pascal card). - **Shared expert arena (#129, @rhgo1749):** Linux: `--shared-expert-arena FILE` in `/dev/shm` lets several engines on one machine share the RAM copy of the experts. **Checked before the release:** - Byte-identical to 0.1.29 with a fixed cache on all four quants on this PC (Q2_0, IQ3_XXS, IQ3_S, the Coder), including the prompt path's internal state, with the same decode speed. - Real use at a 64K context on all five models (Q2_0, IQ2_XS, IQ3_XXS, IQ3_S, the Coder), through the server: - a fact found in a 57,566-token document; - the follow-up turn reusing the whole prompt; - a tool call; - a cancelled long prompt followed by a new request; - a sampled answer. None of them restarted the engine or ran low on VRAM. Prompt speed and free VRAM were the same as 0.1.29. - The conversation cache (#189): with it off, the same output as 0.1.29 up to 128K; with it on, the reuse, memory-pressure and admission tests pass. - Rope scaling (#84): the parity tests, and needles at 300K in a 320K context. - The server's tests (80) and the CPU expert parity test (0 failures; with `STRATA_IQ_MT_MIN=1` no row differs between one token and a group). - Linux (WSL, RTX 5070): Q2_0 byte-identical to 0.1.29 with a fixed cache (10/10), and the default settings pass. - AMD (RX 9070 XT and R9700): the HIP build with its tests (45 of 47; the 2 others need a model fixture or an AVX-512 CPU), the live server test on each card, and the two-card layer split. **Updating:** get the latest files (`git pull`, or download and unzip anywhere), then run `START-HERE.bat` (Linux: `./setup.sh`). Setup installs engine 0.1.30. The ready-made Strata engine for Windows (RTX 20 / 30 / 40 / 50: sm_75, sm_86, sm_89, sm_120 + PTX), CUDA 13.0. You don't need to download this yourself: `START-HERE.bat` fetches it (and NVIDIA's cuBLAS from pip), so no compiler or CUDA Toolkit is needed, only an NVIDIA driver 580 or newer. Contents: `strata.exe` (the engine), `strata-vision.exe` (the optional image encoder), `BUILD.json`. Repository README (current repository documentation, not release changes) Strata Run a 125-billion-parameter AI model on a normal gaming PC one NVIDIA card (12-24 GB) + 64 GB of RAM · Windows or Linux · one click to install 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 your own PC. It writes its answers at **60-95 tokens per second** (a token is about ¾ of a word): faster than you can read. - **Free and open source.** > **Jump to:** [How fast?](#how-fast-is-it) · [Which model?](#which-model-should-i-pick) · [Install](#install) · > [Using it](#using-it) · [Problems?](#something-went-wrong) · [How it works](#how-does-it-work) · > [All the details](docs/DETAILS.md) --- ## How fast is it? Measured on an RTX 5070 (12 GB), a Ryzen 5 7600 and 64 GB of RAM: | Size | Writes answers (short chat) | Writes answers (128K context) | Reads your prompt | | --- | ---: | ---: | ---: | | **Q2_0** | 93 tokens/s | 74 tokens/s | 2,170 tokens/s | | **IQ2_XS** | 79 tokens/s | 63 tokens/s | 2,090 tokens/s | | **IQ3_XXS** | 62 tokens/s | 49 tokens/s | 1,750 tokens/s | | **IQ3_S** | 53 tokens/s | 46 tokens/s | 1,620 tokens/s | | **Coder** (IQ1_M) | 55 tokens/s | 43 tokens/s | 2,180 tokens/s | - **Writes answers** = how fast the reply appears (tokens per second). - **Reads your prompt** = how fast it takes in what you send (long documents, code, chat history), measured on a 32K-token prompt; a 4K prompt reads at 910-1,580 tokens/s. A 32K prompt takes about 15 seconds with Q2_0. A card with more VRAM is faster, because more of the model fits on the GPU: an RTX 3090 (24 GB) should do roughly 100-140 tokens per second. All measurements, long-context numbers and estimates for other cards are in the [details](docs/DETAILS.md#speed-measured). Every PC is different: `START-HERE.bat --calibrate` measures a few engine settings on yours and keeps the fastest (about 5-10 minutes; on the PC above it made the Coder 7% faster). **Two or three NVIDIA cards?** Just run `START-HERE.bat`: it lists your cards, says which ones Strata can use, and asks whether to share the model across them (recommended when two can). An install made on one card asks once at its next start. Or choose yourself: `START-HERE.bat --gpus 0,2` (both, remembered), `--gpus all`, or `--gpu 0` (one card, this start only). Each card keeps the experts of its own layers, and prompts flow through the cards in a pipeline: on an RTX 5080 + RTX 3090 prompts were read 18-20% faster than on the 5080 alone, decoding on par. Every card must be an RTX 20 series or newer with 8 GB or more. See [docs/MULTI_GPU.md](docs/MULTI_GPU.md). ## Which model should I pick? **The size** (the same model, compressed more or less): | Model | RAM+VRAM Requirements | Speed | Quality | | --- | ---: | --- | --- | | **Q2_0** | 37.6 GB | fastest | good | | **IQ2_XS** | 39.2 GB | fast | better (**recommended**) | | **IQ3_XXS** | 47.0 GB | slower | great | | **IQ3_S** | 54.8 GB | slowest | best: matches the full model on the published tests (original model only) | **Will it fit?** Shard 1 is the part of the model that gets loaded when it starts: its experts go into your **RAM**, the rest onto your graphics card (the second shard, a 29 GB lookup table, stays on the SSD). So it fits when your **RAM is at least shard 1 + about 10 GB** for Windows and your other programs. With 64 GB of RAM every size fits (IQ3_S with little else open); with 48 GB, Q2_0 and IQ2_XS. A bigger graphics card makes it faster, but it doesn't lower the RAM needed. **The version:** - **Qwen3.8-Flash-Next** - the original. - **[Coder](https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF)** - ISTA-DASLab's coding version: half of the experts removed, keeping the ones that code, tool use and images need (91% of the full model's SWE-bench Verified score, 99% of LiveCodeBench, by its authors). One size (IQ1_M: its experts stored like IQ3_S): shard 1 is **29.6 GB**, so it fits a PC with **32 GB of RAM**, runs 262K context on 64 GB, and reads long prompts the fastest of all. Weaker outside coding. - **[Swift 1.5](https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF)** - a fine-tune by UkisAI that thinks much shorter before answering, so you get the answer sooner, with about the same quality. Same speed per token, and about the same RAM as the same size of the original (no IQ3_S). Its own license applies (see its page). Not sure? Take **IQ2_XS** - or the **Coder** if you mainly write code, or have 32-48 GB of RAM. You can add another one later with `SETUP.bat` (the same as `START-HERE.bat --setup`; on Linux `./setup.sh --setup`). For **OrcaRouter's Flash-Next Uncensored IQ3_XXS**, see the [manual compatibility setup](docs/ORCA.md). It needs an explicit packing conversion and is not an installer menu option. An **AMD Radeon RX 7900 XT / XTX, RX 9070 / 9070 XT or Radeon AI PRO R9700 on Linux** works too (experimental): `./setup.sh --backend hip`, chosen by itself on a PC with no NVIDIA card Strata can use. It installs ROCm without sudo and compiles the engine (one GPU, no images yet). Details: [AMD HIP](docs/AMD_HIP.md). ## Install **You need:** an NVIDIA RTX 20, 30, 40 or 50 card with 12 GB of VRAM or more (RTX 20 since 0.1.27), enough RAM for the size you pick (above; a big GPU makes up for less RAM - the [low-RAM mode](docs/DETAILS.md)), ~80 GB of free disk space (an SSD makes the first start much faster), and Windows 10/11 or Linux. The only thing you install yourself is a current **NVIDIA driver** ([nvidia.com/drivers](https://www.nvidia.com/drivers) or the NVIDIA App). Everything else - Python, the engine, the model - is set up for you. **Windows** 1. [Download this project](https://github.com/Niko1221/Strata/archive/refs/heads/main.zip) and unzip it (or `git clone` it). 2. Double-click **`START-HERE.bat`**. 3. Answer a few questions - or just press Enter each time for the recommended choice: - **Which model and size?** The original or Swift 1.5, and Q2_0, IQ2_XS, IQ3_XXS or IQ3_S - see [above](#which-model-should-i-pick) - **How much context?** How much text it can keep in mind at once (it suggests one for your card). 384K and 512K (experimental) extend the model past its trained 262K by rope scaling - the setup turns it on itself (yarn and a covering factor; `--rope-scaling`/`--rope-scale` override) ([details](docs/DETAILS.md)) - **Images?** Whether it should also read pictures - **Experimental speed projection?** Off unless you say yes - [read what it does](docs/DETAILS.md#experimental-speed-projection-experimental-off-by-default) first Then it downloads everything (the model is ~70 GB, so the first time takes a while - you can stop and it picks up where it left off) and **starts the model**. 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**, just double-click `START-HERE.bat` again: it starts right away, nothing is downloaded twice. Close its window to stop the model. **Updating:** download the new version and unzip it anywhere (or `git pull`), then run `START-HERE.bat` in it. The model files are kept in a `Strata-data` folder next to your Strata folder, so a new copy finds them and sets itself up the same way - nothing big is downloaded again. **Linux:** run `./setup.sh` - same questions, same result. **Docker (Linux):** the same idea, in a container. 1. Host: Docker with the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) and a driver **580 or newer** (CUDA 13.0). 2. Build (this compiles the engine into the image, so the container never compiles): `docker build -t strata .` `docker build -t strata --build-arg CUDA_ARCHITECTURES=89 .` builds for one card only (faster). The default covers RTX 30 (86), RTX 40 (89), RTX 50 (120) and A-series (80); a card outside that set needs a rebuild with its own arch. Add `--build-arg BUILD_VISION=0` to skip the image encoder. 3. Run (the first start downloads the ~70 GB model, then starts; later starts go straight to serving): `docker run --rm --gpus all -p 8080:8080 --ulimit memlock=-1 -v strata-data:/data strata` The setup choices are env vars: `-e MODEL=IQ2_XS -e FAMILY=qwen -e CONTEXT=32768 -e VISION=no` (or `MODEL=Q2_0|IQ3_XXS|IQ3_S`, `FAMILY=swift|coder`; the defaults above are the recommended ones). `-e VISION=cpu` keeps the image encoder on the CPU. `-e KV=int8|q4_0|k8v4` picks the KV cache precision; `k8v4` is INT8 K with 4-bit V and keeps its KV in VRAM from 64K up. Only the model files, the prepared pack, the MTP layer and the install config live in the `strata-data` volume; the engine is part of the image. Switching between models already on the volume needs no setup pass: `-e MODEL=Q2_0 -e FAMILY=coder` picks that model's config. Add `-e REINSTALL=1` only to change settings for a model already set up (context, vision, KV, host, api_key, LOW_RAM), since those are recorded in its config. Strata loads 32-62 GB into RAM. `--gpus all` on a host with two usable cards takes both: the layer split is setup's recommended default ([docs/MULTI_GPU.md](docs/MULTI_GPU.md)), and a volume set up for one card switches to the pair on its first start there. Pin one card with `-e GPU=0`, or name them with `-e GPUS=0,2` and where the later card's layers start with `-e LAYER_SPLIT=18`. A memory limit needs `-e LOW_RAM=on`, which maps the model's experts from the pack instead of keeping them in RAM: setup.py measures the host's RAM, not the container's limit, so it cannot see a cap. LOW_RAM runs on one card. The server listens on `0.0.0.0:8080` by default; set `-e API_KEY=` before exposing the port to a network. The image has a `HEALTHCHECK` on `/health`, so `docker ps` shows the container healthy once the model is loaded, and `GET /v1/status` says what it is running. ## 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` - the Strata app (it opens by itself when the model starts): **Chat**, a live **Monitor** of the model and your GPU/CPU/RAM, and **About** with the settings and addresses. - **Chat in the terminal:** `.venv\Scripts\python chat.py` - **Your apps and coding agents:** add it as 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`. - **Thinking:** the model thinks before it answers. Choose **off, low, medium or high** - in the chat page menu, with `/think low` in `chat.py`, or with your app's "reasoning effort" setting. Off is fastest; high is best for hard questions. - **Pictures:** in the chat page click **Picture**; in `chat.py` type `/image `; in apps just attach them. - **From your phone or another PC:** `START-HERE.bat --setup --host 0.0.0.0 --api-key `, then open the address the server window prints; see the [details](docs/DETAILS.md#using-it). - **Experimental speed projection (off by default):** an experimental control vector that setup can turn on; it changes how the model answers - read [what it does](docs/DETAILS.md#experimental-speed-projection-experimental-off-by-default) first. **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); after that it keeps the conversation and reads only what is new, so follow-ups start in seconds. ## Something went wrong? **My PC froze, or got very slow, the first time Strata started.** That's normal while it starts, most of all the first time. Strata loads 35-55 GB into your RAM, locks part of it for the graphics card, and works out how much of the model fits on your GPU. The mouse can freeze for a few minutes. **Wait, and don't close the window.** The next starts are much faster. Still frozen after 10 minutes? Restart the PC, close other programs (browsers use a lot of RAM) and try again. If it keeps happening, pick a smaller size (Q2_0 or IQ2_XS). **It stopped while downloading or installing.** Run `START-HERE.bat` again. It continues where it stopped. **It says the NVIDIA driver is too old.** Update it (NVIDIA App or [nvidia.com/drivers](https://www.nvidia.com/drivers)), restart the PC, and run `START-HERE.bat` again. **It says port 8080 is already in use.** Strata is already running. Look for its window. **It's very slow and the disk light keeps blinking.** Your PC is out of free RAM. Close other programs, or pick a smaller size (Q2_0 or IQ2_XS). **An answer stopped with "the engine stopped unexpectedly".** Usually not enough RAM (on Linux the system then stops the engine). Just send your message again: Strata starts the engine by itself. If it keeps happening, close other programs or pick a smaller size. **It says the prompt exceeds the context.** The conversation is longer than the context you chose. Start a new chat, or run `SETUP.bat` and pick more context. **Still stuck?** Look in the [full troubleshooting table](docs/DETAILS.md#troubleshooting), or open an issue 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** - the same idea as 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 it writes needs only 10 of them. So it doesn't have to have all of them on the graphics card at once. - **Your graphics card** does the part of the work needed for every word, and keeps the few thousand experts that are asked most often. It keeps learning which ones those are while you use it. - **Your RAM** holds every expert. When a word needs one the card doesn't have, **your processor** works on it - at the same time as the graphics card, so neither waits for the other. - **Your SSD** holds a big lookup table; the model only reads a few small rows of it per word. - **Guess, then check.** A small, fast helper built into the model guesses the next few words, and the big model checks all the guesses in one go. It keeps the ones it agrees with and writes the next word itself - so one step often produces several words. The helper only guesses - the big model decides every word - so you get the same quality answer, 1.6-1.8x sooner. - **Long texts are read in big pieces** (up to 8,192 tokens - pieces of words - at a time), which is why a long document or code base is read at over 1,000 tokens per second. Want the full picture? The [details](docs/DETAILS.md#how-it-works) explain every part and its numbers, and the [paper](docs/paper/Strata-Paper.pdf) tells the whole story, with the measurements behind it. ## Credits - Model: [Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next) by the Qwen team; compressed versions by [ISTA-DASLab](https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF); [Swift 1.5](https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF) by UkisAI. Their licenses apply to the model files. - Built with parts of [llama.cpp / ggml](https://github.com/ggml-org/llama.cpp) (MIT). Ideas from [Splash](https://github.com/incoai/splash), [ninfer](https://github.com/Neroued/ninfer) and [HyperQwen](https://github.com/syv-ai/HyperQwen). More in the [details](docs/DETAILS.md#credits-and-licenses). ## License Strata is open source under the [MIT License](LICENSE). A few parts carry their own licenses: `third_party/ggml` (MIT, llama.cpp / ggml), the web app's font (SIL Open Font License 1.1) and the experimental speed projection's vector in `data/experimental-speed-projection` (Qwen Community License 1.0, from the model's activations). The models are not part of this repository; each model's own license applies to its files.