Niko1221/Strata

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 - 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? · Which model? · Install ·

Using it · Problems? · How it works ·

All the details

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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.

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).

Measured Strata on your own PC? See Community benchmark results for a report template and how to share your results in a pull request.

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.

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 - 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 - 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. It needs an explicit packing conversion and is not an installer menu option.

Unsloth's 4-bit UD-Q4_K_XL (experimental) is the fourth version in setup's menu (--family unsloth): the closest to the full model, but a 111 GB download whose 77 GB of experts do not fit in RAM. Strata keeps your RAM minus 24 GB of them in RAM and reads the rest from the SSD while it answers: 7-8.5 tokens/s on a 64 GB PC with a 12 GB GPU, several times slower than the sizes above, and long prompts are slow. It needs 48 GB of RAM or more, an NVMe SSD and one NVIDIA GPU (no images yet). Details and measurements: UD-Q4_K_XL.

An AMD Radeon RX 7900 XT / XTX, RX 9070 / 9070 XT or Radeon AI PRO R9700 on Linux works too (experimental; the RX 7800 XT / 7700 XT and RX 9060 XT were validated by their owners; the RX 6800 / 6900 series, gfx1030, is community-reported): ./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 (no images yet; several cards with --gpus). Details: AMD HIP.

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), ~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 or the NVIDIA App). Everything else - Python, the engine, the model - is set up for you.

Windows

1. Download this project 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
• 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)

• Images? Whether it should also read pictures
• Experimental speed projection? Off unless you say yes - read what it does 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 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), 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.

• 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 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.

Where things are stored

• Your chats: only in your browser. The Chat tab keeps the conversation, its settings and the API key you typed

in the browser's local storage (strata.* keys) - not on the server and not in the Strata folder. Pictures are not kept, only their names. Another browser or a private window starts empty; clearing the site's data deletes them.

• How the model starts: strata-.json in the Strata folder (context, GPUs, host, API key, ...), written

by setup; next to it run-.bat / .sh, the log strata-.log and, when you use "Use for other apps too", strata-.shared-settings.json.

• The model files (models/, packs/, mtp/, 70-120 GB): in Strata-data next to the Strata folder, or

wherever --data-dir put them.

• Where that data folder is: %APPDATA%\Strata\settings.json on Windows, ~/.config/strata/settings.json on

Linux (details).

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), 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, 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 explain every part and its numbers, and the paper tells the whole story, with the measurements behind it.

Credits

• Model: Qwen3.8-Flash-Next by the Qwen team; compressed versions by

ISTA-DASLab; Swift 1.5 by UkisAI; the experimental UD-Q4_K_XL by Unsloth (its support follows eddoursul/Strata). Their licenses apply to the model files.

• Built with parts of llama.cpp / ggml (MIT). Ideas from

Splash, ninfer and HyperQwen. More in the details.

License

Strata is open source under the MIT 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.