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> For a complete documentation index, see https://docs.nvidia.com/nemo/datadesigner/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/datadesigner/_mcp/server.

# 🏗️ Architecture & Performance

Data Designer is an **orchestration framework** that coordinates synthetic data generation workflows. It is a **client** of LLM inference servers—it does not host models itself.

This guide explains the architecture, execution model, and how to tune performance for your specific use case.

***

## Separation of Concerns

```
┌─────────────────────────────────────┐          ┌─────────────────────────────────────┐
│         Data Designer               │          │       Inference Server(s)           │
│         (Orchestration)             │  HTTP    │       (LLM Hosting)                 │
│                                     │  ─────►  │                                     │
│  • Dataset workflow management      │          │  • Model weights and execution      │
│  • Column dependency resolution     │          │  • GPU allocation and scheduling    │
│  • Batching and parallelism         │          │  • Request queuing                  │
│  • Retry and error handling         │          │  • Token generation                 │
│  • Adaptive concurrency (AIMD)      │          │  • Rate limiting (optional)         │
│  • Data validation and quality      │          │                                     │
└─────────────────────────────────────┘          └─────────────────────────────────────┘
              ▲                                                    ▲
              │                                                    │
        Your workflow                                    Your infrastructure
         configuration                                    (or cloud API)
```

### What Data Designer Does

* **Orchestrates** the generation workflow across multiple columns
* **Resolves dependencies** between columns (DAG-based execution)
* **Batches** work into manageable chunks (`buffer_size`)
* **Parallelizes** LLM calls within batches (`max_parallel_requests`)
* **Adapts to rate limits** automatically via AIMD concurrency control
* **Handles errors** with retries and early shutdown logic
* **Validates** generated data against schemas and constraints

### What Data Designer Does NOT Do

* **Host models**: You must provide LLM endpoints
* **Manage GPUs**: Your inference server handles GPU allocation
* **Scale inference**: You must provision sufficient capacity
* **Impose rate limits**: Your server or API gateway sets rate limits (Data Designer *reacts* to them automatically)

***

## Execution Model

#### Async execution

Data Designer uses the **async engine**, which dispatches work at the cell level and overlaps independent columns. The configuration knobs documented below (`buffer_size`, `max_parallel_requests`, AIMD throttle config, error handling) apply to that engine.

The async engine processes datasets in **row groups**, with **parallel** operations across ready cells and independent columns.

### How It Works

**Step 1: Split into row groups**

Your dataset is divided into row groups of `buffer_size` records. Row groups checkpoint independently so interrupted runs can resume from durable parquet files.

**Step 2: Schedule ready work**

Data Designer builds an execution graph from column dependencies. Cells and full-column tasks whose dependencies are satisfied can run concurrently, while order-dependent columns are protected by scheduler locks.

Example workflow:

```
Row group 1 (100 records)
│
├─► Column 1: category (Sampler)      ──── All 100 values generated
├─► Column 2: prompt (LLM Text)       ──── All 100 values generated
├─► Column 3: response (LLM Text)     ──── All 100 values generated
├─► Column 4: score (Expression)      ──── All 100 values computed
│
└─► Checkpoint row group to disk
    │
    ▼
Row group 2 (100 records)
    ...repeat...
```

**Step 3: Generate cells in parallel**

Within each column, cells are processed **in parallel** up to the configured limit:

| Column Type                         | Parallelism Control                  |
| ----------------------------------- | ------------------------------------ |
| Sampler                             | `non_inference_max_parallel_workers` |
| LLM (Text, Code, Structured, Judge) | `max_parallel_requests`              |
| Expression                          | Sequential (fast, CPU-bound)         |

### Key Concepts

| Concept              | Description                                                                                                       |
| -------------------- | ----------------------------------------------------------------------------------------------------------------- |
| **Row groups**       | Records are split into checkpointable groups of `buffer_size`; multiple row groups can be in flight concurrently. |
| **Dependency graph** | Columns and cells become runnable when their upstream dependencies are complete.                                  |
| **Parallel cells**   | Individual cells are generated in parallel up to the configured limit.                                            |

### Concurrency Formula

At any moment, the number of concurrent LLM requests is:

```python
concurrent_requests = min(
    buffer_size,                # Records in an active row group
    current_throttle_limit,     # AIMD-managed limit (≤ max_parallel_requests)
    remaining_cells_in_column   # Cells left to generate
)
```

`max_parallel_requests` sets the **ceiling**. The actual limit (`current_throttle_limit`) is managed at runtime by an AIMD (Additive Increase / Multiplicative Decrease) controller that reacts to rate-limit signals from the inference server:

* **During optional startup ramp**: when `rampup_seconds` is greater than 0, a new throttle domain starts at one concurrent request and increases linearly toward `max_parallel_requests` over that duration.
* **On the first 429 in a burst**: the limit is reduced by a configurable factor (default: 25% reduction) and a cooldown is applied. Further 429s from already in-flight requests in the same burst do not reduce the limit again — they release their permits and hold the limit steady.
* **After consecutive successes**: the limit increases by 1 (by default) until it reaches the ceiling or a stabilized rate-limit threshold.

This means Data Designer automatically finds the right concurrency level for your server without manual tuning.

**Example**: With `buffer_size=100` and `max_parallel_requests=32`, Data Designer can send up to 32 requests in parallel. If `rampup_seconds=30`, it starts at one request and climbs linearly toward 32 over 30 seconds. If the server returns 429s, startup ramp stops, concurrency drops automatically (e.g., to 24, then 18), and normal AIMD recovery takes over once the server catches up.

***

## Configuration Parameters

### `buffer_size` (RunConfig)

Controls how many records are processed per row group.

```python
import data_designer.config as dd
from data_designer.interface import DataDesigner

run_config = dd.RunConfig(buffer_size=2000)

designer = DataDesigner()
designer.set_run_config(run_config)
```

| Value                | Memory Usage | Throughput                 | Error Feedback |
| -------------------- | ------------ | -------------------------- | -------------- |
| **Low** (100-500)    | Lower        | May not saturate inference | Fast           |
| **Default** (1000)   | Moderate     | Good for most cases        | Moderate       |
| **High** (2000-5000) | Higher       | Better for deep pipelines  | Slower         |

**When to increase**: High-capacity inference server, single-model workflows, memory not constrained

**When to decrease**: Memory-constrained environments, development/debugging, complex multi-model pipelines

***

## Resuming Interrupted Runs

Long generation jobs can be resumed from checkpoints by passing `resume` to `DataDesigner.create()` or `data-designer create --resume`.

```python
from data_designer.interface import DataDesigner, ResumeMode

designer = DataDesigner()
results = designer.create(
    config_builder,
    num_records=10_000,
    dataset_name="training_data",
    resume=ResumeMode.IF_POSSIBLE,
)
```

Resume modes:

* `ResumeMode.NEVER` (default): always start a fresh generation run. If the dataset directory already exists, Data Designer writes to a timestamped directory.
* `ResumeMode.ALWAYS`: resume the existing dataset directory. Raises if the checkpoint is incompatible or cannot be resumed safely.
* `ResumeMode.IF_POSSIBLE`: resume when the stored config fingerprint matches the current config; otherwise start a fresh timestamped run.

Data Designer stores the run configuration in `metadata.json` (`buffer_size`, `target_num_records`, config fingerprint) and `builder_config.json`. Resume scans completed `batch_*.parquet` row groups and reads parquet metadata for the row count actually persisted. That keeps resume crash-safe even if a run was interrupted between writing a row-group parquet and updating metadata, because the filesystem reflects the durable state even when metadata lags by a step.

Resume has a few important invariants:

* `buffer_size` must match the original run.
* `num_records` must be at least the original target; you may extend a run by requesting more records.
* Row counts must stay stable within a run. Put filtering, expansion, aggregation, or deduplication at workflow boundaries.
* Once `process_after_generation()` has run, the dataset is considered terminal for resume. Re-running with the same target returns the existing dataset; extending requires a fresh run.
* If a run crashed after every row group was written but before `process_after_generation()` could start, resume runs after-generation on the existing on-disk dataset (the parquet files are still clean) and marks it terminal afterwards. A crash *during* `process_after_generation()` still raises — the parquet files may have been partially rewritten and starting fresh is the only safe option.

The `DatasetCreationResults` returned by a resume invocation reflects the full dataset on disk for anything that reads the artifact directory (`load_dataset`, `count_records`, `load_analysis`, `export`, `push_to_hub`). Per-run observability — `task_traces`, model-usage logs, and telemetry events emitted during the call — is scoped to the resume invocation only; the original run's in-memory traces are not persisted across process boundaries.

Only resume datasets from trusted artifact directories. Resume reads local `metadata.json`, `builder_config.json`, and parquet files to determine checkpoint state.

***

### `max_parallel_requests` (InferenceParams)

Sets the **maximum** concurrent LLM API calls **per model**. This is the ceiling that the AIMD throttle controller can ramp up to — the actual concurrency at runtime may be lower if the server signals rate limits.

```python
import data_designer.config as dd

model = dd.ModelConfig(
    alias="my-model",
    model="nvidia/nemotron-3-nano-30b-a3b",
    provider="nvidia",
    inference_parameters=dd.ChatCompletionInferenceParams(
        max_parallel_requests=8,
    ),
)
```

**Default**: 4

**When to increase**: Your inference backend has high throughput capacity, you're using a cloud API with generous rate limits, or you're running vLLM/TensorRT-LLM with multiple GPUs. With AIMD, setting an aggressively high value is safer than before — the system will self-correct downward if the server can't keep up, and salvage rounds can reclaim transiently failed rows.

**When to decrease**: You want to cap resource usage to a known safe level, or you want more predictable/debuggable execution.

Finding the optimal value
The right value depends on your inference stack and model. Self-hosted vLLM servers can often handle values as high as 256, 512, or even 1024 depending on your hardware.

With AIMD, a practical approach is to set `max_parallel_requests` to the **upper bound** you're comfortable with and let the throttle controller find the sustainable level automatically. If you see frequent 429 → recovery cycles in the logs, your ceiling is above the server's true capacity but the system is handling it. If you never see any throttle activity, you may have room to increase the ceiling further.

**Benchmark approach**: Run a small dataset (e.g., 100 records) with increasing `max_parallel_requests` values (4 → 8 → 16 → 32 → ...) and measure generation time. Stop increasing when the runtime stops decreasing—that's when your inference server is saturated.

***

### `non_inference_max_parallel_workers` (RunConfig)

Controls thread pool size for non-LLM operations (samplers, expressions, validators).

```python
run_config = dd.RunConfig(non_inference_max_parallel_workers=8)
designer.set_run_config(run_config)
```

**Default**: 4

**When to increase**: Many CPU-bound columns (complex expressions, heavy sampling)

***

### Adaptive Throttling (RunConfig)

Data Designer uses an AIMD (Additive Increase / Multiplicative Decrease) controller to automatically adjust concurrency per model based on rate-limit feedback from the inference server. The defaults work well for most workloads. Override them via `ThrottleConfig` only when you understand the trade-offs.

```python
import data_designer.config as dd
from data_designer.interface import DataDesigner

run_config = dd.RunConfig(
    throttle=dd.ThrottleConfig(
        reduce_factor=0.75,       # Multiply limit by this on a 429 (default: 0.75)
        additive_increase=1,      # Add this many slots after success_window successes (default: 1)
        success_window=25,        # Consecutive successes before increasing (default: 25)
        cooldown_seconds=2.0,     # Pause after a 429 when no Retry-After header (default: 2.0)
        ceiling_overshoot=0.10,   # Probe 10% above observed server limit (default: 0.10)
        rampup_seconds=0.0,       # Optional startup ramp duration; 0 disables it (default: 0.0)
    ),
)

designer = DataDesigner()
designer.set_run_config(run_config)
```

| Parameter           | Default | Effect                                                                                                                                                                  |
| ------------------- | ------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `reduce_factor`     | 0.75    | How aggressively to cut concurrency on a 429. Lower = more aggressive.                                                                                                  |
| `additive_increase` | 1       | Slots added per recovery step. Higher = faster ramp-up, but riskier.                                                                                                    |
| `success_window`    | 25      | Consecutive successes required before each increase step.                                                                                                               |
| `cooldown_seconds`  | 2.0     | Pause duration after a 429 (used when the server doesn't send `Retry-After`).                                                                                           |
| `ceiling_overshoot` | 0.10    | Fraction above the observed rate-limit ceiling the controller is allowed to probe.                                                                                      |
| `rampup_seconds`    | 0.0     | Optional startup ramp duration. When greater than 0, domains start at one concurrent request and linearly climb to the configured ceiling unless a 429 aborts the ramp. |

How it works in practice
When a model endpoint returns HTTP 429, the controller reduces the concurrency limit for that model and pauses briefly. After enough consecutive successes, it begins ramping back up. If the server rate-limits again, the controller records that level as a ceiling and stabilizes just below it, with a small overshoot band to detect when the server can handle more load.

You can observe this in the logs — look for messages like `concurrency reduced from X → Y` and `concurrency increased from X → Y`.

***

### Error Handling (RunConfig)

Control retry behavior and early shutdown for failed generations.

```python
run_config = dd.RunConfig(
    max_conversation_restarts=5,           # Full conversation restarts (default: 5)
    max_conversation_correction_steps=0,   # In-conversation corrections (default: 0)
    disable_early_shutdown=False,          # Enable early shutdown (default)
    shutdown_error_rate=0.5,               # Shut down if >50% errors
    shutdown_error_window=10,              # Min tasks before error monitoring
)
designer.set_run_config(run_config)
```

**When to adjust**:

* **Strict schemas**: Increase `max_conversation_restarts` to 7, add `max_conversation_correction_steps=2`
* **Debugging**: Set `disable_early_shutdown=True` to see all errors
* **Simple text**: Reduce `max_conversation_restarts` to 3

***

## Async Engine

The async engine is the execution path. It dispatches work at the cell level rather than the column level, so independent columns overlap in time and per-(provider, model) AIMD pools tune themselves independently. See the [Async All the Way Down](/dev-notes/async-all-the-way-down) dev note for the full architecture.

### Per-model timeouts drive every deadline

The `inference_parameters.timeout` field on a `ModelConfig` sets the per-request HTTP timeout. The same value also drives the sync→async bridge that custom columns use when they call `model.generate()`. There is no separate queue-wait deadline — waits scale with provider speed and AIMD's adaptive concurrency. Slow self-hosted endpoints (e.g. large models on a single GPU) only need this one knob raised:

```python
import data_designer.config as dd

config_builder.add_model_config(
    dd.ModelConfig(
        alias="slow-model",
        model="my/slow-model",
        provider="my-provider",
        inference_parameters=dd.ChatCompletionInferenceParams(
            timeout=600,
        ),
    )
)
```

### Run outcomes

A run can finish with fewer records than requested when non-retryable errors drop rows. Inspect `len(result.load_dataset())` to detect.

If the rate of non-retryable errors crosses `RunConfig.shutdown_error_rate`, generation stops early and raises `DataDesignerEarlyShutdownError` (a subclass of `DataDesignerGenerationError`). Catch it separately when a typed retry path is appropriate:

```python
from data_designer.interface.errors import DataDesignerEarlyShutdownError

try:
    result = dd_instance.create(config_builder, num_records=1000)
except DataDesignerEarlyShutdownError:
    # e.g. retry against a different model alias
    ...
```

## Common Problems

| Problem                            | Symptom                                           | Solution                                                                                                                                                                                                                          |
| ---------------------------------- | ------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Low throughput**                 | Low GPU utilization                               | Increase `max_parallel_requests` and/or `buffer_size`. If the throttle has self-reduced due to earlier 429s (check logs for "concurrency reduced" messages), the server may need more capacity or you can wait for AIMD recovery. |
| **Frequent 429 → recovery cycles** | Logs show repeated concurrency drops and ramp-ups | The `max_parallel_requests` ceiling is above the server's sustained capacity. This is handled automatically, but you can lower the ceiling to reduce the sawtooth or tune `reduce_factor` / `success_window`.                     |
| **Long tail of slow generations**  | Most records fast, few very slow                  | Reduce `max_conversation_restarts`, simplify schemas, improve prompts                                                                                                                                                             |
| **Multi-model idle periods**       | One model busy, others idle                       | Reduce `buffer_size` for faster cycling, or consolidate models                                                                                                                                                                    |
| **Memory errors**                  | OOM crashes                                       | Reduce `buffer_size` and `max_parallel_requests`                                                                                                                                                                                  |
| **Too many errors**                | Generation fails frequently                       | Check prompts/schemas; adjust `shutdown_error_rate` or disable early shutdown for debugging                                                                                                                                       |

***

## Tuning Workflow

1. **Start with defaults** for initial development — AIMD handles rate-limit adaptation automatically
2. **Profile your workload**: How many LLM columns? How many records? What models?
3. **Identify bottleneck**: Low GPU util → increase `max_parallel_requests` (AIMD will self-correct if you overshoot). Memory issues → decrease `buffer_size`. Long tails → tune retry settings.
4. **Check throttle logs**: Look for "concurrency reduced" / "concurrency increased" messages to understand whether rate limits are the bottleneck
5. **Iterate**: Make one change at a time, measure impact before next change

***

## Related Documentation

* [Deployment Options: Library vs. Microservice](/concepts/deployment-options): Choosing between library and microservice
* [Model Configuration](/concepts/models/model-configs): Complete model settings reference
* [Inference Parameters](/concepts/models/inference-parameters): Detailed parameter reference