> This page is for version 2.0.0 (Latest) (default).
> For other versions, use one of these documentation indexes:
> - 2.0.0 (Latest) (default): https://docs.nvidia.com/nim/alpamayo/2.0.0/llms.txt
> - 1.0.0: https://docs.nvidia.com/nim/alpamayo/1.0.0/llms.txt

> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nim/alpamayo/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nim/alpamayo/_mcp/server.

## Release 2.0.0

This is the first release of the Alpamayo 2 Super NIM: container tag and `GET /v1/version` `release` `1.0.0`. It is documented by documentation version 2.0.0, the version shown in this site's version selector, which also retains the Alpamayo 1.5 pages.
For prerelease containers, also record the image tag or digest because release candidates can report the same NIM version.

### Capabilities

* **Driving-scene inference:** trajectory generation, visual question answering, meta-action prediction, auto-labeling, and visual grounding through `POST /v1/infer`. See [Inference Tasks](/nim/alpamayo/tasks).
* **Client APIs:** HTTP for all five tasks, OpenAI-compatible trajectory and VQA routes, and gRPC `Predict` for trajectory generation. See [API Reference](/nim/alpamayo/api-reference).
* **Deployment profiles:** BF16 and ModelOpt FP8, with optional DFlash2 speculative decoding, on supported H100 and H200 GPUs. See [Support Matrix](/nim/alpamayo/support-matrix).
* **Trajectory control:** multiple trajectory samples, explicit sampling seeds, and optional navigation guidance. See [Sampling Control](/nim/alpamayo/sampling-params) and [Navigation-conditioned trajectory](/nim/alpamayo/tasks#navigation-conditioned-trajectory).
* **Deployment tools:** bundled scenes, payload builder, HTTP and gRPC clients, agent skills, custom checkpoint loading, and offline deployment instructions. Start with [Quickstart Guide](/nim/alpamayo/quickstart-guide).
* **Operations:** health checks, active configuration discovery through `GET /v1/models`, and Prometheus metrics for requests, queues, and speculative decoding. See [Observability](/nim/alpamayo/observability).

### Compatibility and limitations

All inference responses are returned in full; streaming is not supported. Each container uses one GPU and processes requests one at a time; startup rejects more than one visible GPU. BYOC accepts unquantized BF16 and ModelOpt FP8 checkpoints and rejects other checkpoint quantizations at startup. Grounding responses carry `bbox_space` (`grid_0_1000`), and `GET /v1/models` reports `action_expert_max_batch_size`.
The served model ID is `nvidia/alpamayo-2-super`; requests that name the earlier ID `nvidia/Alpamayo2-Super` are still accepted. `GET /v1/models` also reports `root`, the directory of the selected profile's `checksums.blake3` model-file manifest. For current limitations and operational guidance, see [Known Issues and Limitations](/nim/alpamayo/known-issues).

### Migrating from Alpamayo 1.5

Update the model ID to `nvidia/alpamayo-2-super` and use the Alpamayo 2 Super examples. Images, camera layouts, and precision profiles differ from 1.5.

Alpamayo 2 Super uses vLLM-Omni as its serving backend. Read the active backend from `GET /v1/models` and use the Super-specific configuration and warmup guidance. Compatible HTTP envelopes do not imply identical runtime behavior.

* Send `task` with `trajectory`, `vqa`, `meta_action`, `auto_labeling`, or `grounding`, and use `task="vqa"` for questions. The legacy `mode` field remains accepted: `trajectory` selects trajectory generation, `answer` selects VQA, and `both` returns combined output. The [API reference](/nim/alpamayo/api-reference) maps every task to its `mode` value.
* The default driving layout has six cameras with four frames each (24 images), plus 16 ego-history poses. Trajectory accepts an explicitly declared subset of those cameras, including the four-camera layout; meta-action and auto-labeling retain the complete profile. See [Camera order and sample files](/nim/alpamayo/api-reference#camera-order-and-sample-files).
* Multi-image VQA without `camera_order` reads 24 images in the same six-camera driving order as trajectory and the bundled samples. Declare `camera_order` for other layouts, including `rear_tele`.
* `POST /v1/infer` also accepts the OpenAI-style `model` (served ID or empty) and `stream` (`false`) fields, and responses add `finish_reason`, `time_step` and `warnings`. See [API Reference](/nim/alpamayo/api-reference).
* Select `bf16` or `fp8`, or an opt-in DFlash2 variant. There is no `w4a16` profile. See [Support Matrix](/nim/alpamayo/support-matrix) for GPU requirements.
* Omit `max_tokens` from trajectory-producing requests; including it returns HTTP 422. The NIM manages the trajectory generation budget.
* Use the HTTP status code and `status` field for readiness checks. The readiness `object` string is now `Alpamayo model readiness check`.

For the previous generation's capabilities and deployment instructions, see [Alpamayo 1.5 Documentation](/nim/alpamayo/1.0.0/overview).