# 02 — Training & product

Primary: [blog extract](../reference/blog/introducing-laguna-s-2-1.md) · [models page](https://poolside.ai/models) · Hub card.

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## Pretrain

| Fact | Value | Source |
|---|---|---|
| Tokens | **30T** | models page |
| Start | **2026-05-22** | blog |
| Hardware | **4096× NVIDIA H200** | blog |
| Timeline | under **nine weeks** to launch | blog |
| Data lineage | **same pretrain data as XS 2.1** | blog |
| Diff vs XS | scale + training-code fixes + small recipe — not new corpus | blog |

## Post-train

Two stages: **SFT** (bootstraps capabilities, partly synthetic) then **RL** on tasks the model cannot yet solve at high pass rate.

| Axis | Claim |
|---|---|
| RL precision | **FP8** (first Laguna) |
| Environments | ~**409k** agentic + non-agentic |
| Terminal subset | ~**83k** |
| SWE subset | ~**168k** |
| SWE commit tasks | ~**38k** across ~**17k** repos |
| New for S 2.1 | agentic repository install (deps + green tests) |

### Training-loop changes (blog)

1. More generous rollout budgets (timeouts, tokens/turn, turns/task)  
2. New sandbox service (background processes, selective network block, artifact cache)  
3. **Multi-harness rollouts** — same prompts across scaffolds to reduce single-harness overfitting  
4. FP8 RL  

### Behavioral bet

> not necessarily add more intelligence, but improve … more verification, less taking things for granted, not declaring victory early, and being more persistent.

## Product surface

| Surface | Note |
|---|---|
| Hub | `poolside/Laguna-S-2.1` OpenMDW-1.1; BF16/FP8/INT4/NVFP4/GGUF/MLX |
| DFlash | `poolside/Laguna-S-2.1-DFlash` speculative draft |
| API | OpenAI-compatible `https://inference.poolside.ai/v1` · `poolside/laguna-s-2.1` |
| Agent | `pool` harness; ACP clients; trajectories.poolside.ai |
| Hosted | OpenRouter (free 256K + paid 1M), Vercel AI Gateway, Baseten |
| Chat | chat.poolside.ai (no login required) |
| Local | DGX Spark class for quantized; multi-GPU for full BF16 (~236 GB) |
| Fine-tune | NVIDIA NeMo AutoModel · Prime Intellect Prime Lab |

### OpenRouter pricing (blog, dedicated 1M)

$0.10 in · $0.20 out · $0.01 cache-read per 1M tokens.

### Base model

Pre-post-training weights: request **models@poolside.ai** (not on Hub by default).

## Serve (Hub card)

| Engine | Parsers |
|---|---|
| **vLLM** | `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1` |
| **SGLang** | `--reasoning-parser poolside_v1 --tool-call-parser poolside_v1` |
| **TRT-LLM** | `--tool_parser poolside_v1 --reasoning_parser laguna` (note flag names) |
| **llama.cpp** | poolside fork branch `laguna`; GGUF + DFlash |

Thinking: `chat_template_kwargs.enable_thinking`. In pool: `/thought-level`.

Full commands: [03-serve-and-thinking](../code-study/03-serve-and-thinking.md).