laguna-s-2.1
Laguna-S-2.1 — 118B hybrid-attention MoE (~4B active/token) served on 4x Tenstorrent Blackhole (p150x4, TP=4+EP=4, FABRIC_1D) via vLLM with the tenstorrent/vllm-tt-plugin.
Runs on p150x4 (mesh P150x4) — 8,192-token context, up to 1 concurrent sequences.
Packaged and published with tt-model-manager 0.1.0 (manifest schema 5.1).
Quickstart
tt-model pull mando2222/laguna-s-2.1-v51 --with-weights
tt-model serve mando2222/laguna-s-2.1-v51
pull --with-weights downloads the Docker image and the poolside/Laguna-S-2.1 weights (into your HF cache; they are not in the image). serve starts an OpenAI-compatible server on port 20000 (or the next free port, if that one is busy); the first start compiles kernels for your device, which takes several minutes, and the server is ready when it logs Application startup complete.
Context length
max_model_len is 8192, well below what this model's weights would allow. That is a
serving-stack limit, not a model limit: the TT vLLM plugin allocates a UNIFORM
full-attention KV cache per layer, so Laguna's 36 sliding-window-512 layers are costed as
if they were full-context. Byte-math on the real attention pattern would allow ~1M tokens;
until the plugin can allocate per-layer sliding KV, 8192 is what fits.
Use with your client
Point any OpenAI-compatible client at http://127.0.0.1:20000 once the server is up (or
the port tt-model serve printed). Served model names: poolside/Laguna-S-2.1,
laguna-s-2.1.
Provenance
The exact sources the image was built from — code/ in this repo is byte-identical to the model code inside the image:
| component | built from |
|---|---|
| tt-metal | 2d8292201660bff1ad9caf6cac556d9937f436d4 (dirty tree — the image includes uncommitted changes) |
| vLLM | vllm-0.26.0+empty-cp312-cp312-linux_x86_64.whl — a wheel the author built |
| vllm-tt-plugin | a local checkout — commit not published |
code/ digest |
878a7ef65713a865 (sha256, first 16 hex digits) |
| built | 2026-09-10T13:05:11+00:00 by tt-model 0.1.0 |