> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sglang.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Encoder Parallelism

While the DiT denoises, the text and image encoders are idle — and while they
encode, the whole DiT replica is idle. `--encoder-parallel` decides how to use
those otherwise-unused GPUs for the encoding stage.

```bash theme={null}
--encoder-parallel {auto,fold,dp,replicate}
```

| Mode        | What it does                                                                                | Use when                                                         |
| ----------- | ------------------------------------------------------------------------------------------- | ---------------------------------------------------------------- |
| `auto`      | Picks `fold`, `dp`, or `replicate` per encoder from its width and the request's batch width | Default for `generate`; you want the decision made per encoder   |
| `fold`      | TP-shards the encoder weights across the idle DiT replica                                   | One wide encoder dominates a single-request encode               |
| `dp`        | Each rank encodes its slice of the prompt batch, then the outputs are all-gathered          | Default for `serve`; needs `--batching-max-size > 1` to engage   |
| `replicate` | Every rank encodes the whole batch redundantly                                              | You want the encoding stage to match single-GPU numerics exactly |

The two accelerated modes are mutually exclusive per encoder: folding shards the
weights for the lifetime of the loaded model, so a folded encoder cannot also be
data-parallel.

## Which Mode Wins

Measured on H100 across T5 (hidden 4096), Qwen3 (2560), and CLIP-L (768) at
batch 1–8 and replica sizes 2 and 4:

* **Folding** pays when the encoder is wide enough that sharding its GEMMs beats
  the per-layer all-reduce it adds. T5 gains; Qwen3 (+35%) and CLIP-L (+50%) get
  slower, so folding is gated at hidden ≥ 4096. Its benefit also saturates as
  the replica grows, since each rank's slice keeps shrinking.
* **Data-parallel** pays only when the encode is compute-bound, which needs a
  wide encoder (hidden ≥ 1024 — CLIP-L is slower at every batch and replica
  measured) and more than one prompt in a single encode call.
* **Replication** is the right answer whenever neither condition holds, which is
  most single-request latency work.

`auto` encodes exactly these rules, so prefer it unless you are pinning a
configuration you measured yourself.

## Numerics

`fold` and `replicate` are bitwise-identical to single-GPU encoding: folding
shards a GEMM and reduces it, which is the same arithmetic the unsharded kernel
performs.

`dp` is **not** bitwise-identical. Each rank runs the full unsharded encoder on
a smaller batch, so the GEMM tiling and reduction order differ from the batched
reference — the same floating-point reordering class as choosing a different
attention backend or parallelism strategy, not a precision loss. The gathered
result is mathematically equivalent, and per-request results stay deterministic
for a fixed batch shape, but embeddings will not match a `replicate` run
bit-for-bit, and long video sampling can amplify the difference into visible
frame differences. Use `replicate` (or `fold`) when you need bit-exact
reproducibility against a single-GPU reference, e.g. when refreshing consistency
baselines.

## Recommended Commands

Throughput serving. `serve` already defaults to `dp`, but a single encode call
must carry more than one prompt for it to engage, so raise the batching ceiling
too — an encoder flag deliberately does not change DiT batching for you:

```bash theme={null}
sglang serve \
  --model-path Qwen/Qwen-Image-2512 \
  --model-type diffusion \
  --num-gpus 2 \
  --encoder-parallel dp \
  --batching-max-size 2
```

Single-request latency with one wide text encoder:

```bash theme={null}
sglang serve \
  --model-path Wan-AI/Wan2.2-TI2V-5B-Diffusers \
  --model-type diffusion \
  --num-gpus 4 \
  --ulysses-degree 4 \
  --encoder-parallel fold
```

Bit-exact reproducibility against a single-GPU reference:

```bash theme={null}
sglang serve \
  --model-path Qwen/Qwen-Image-2512 \
  --model-type diffusion \
  --num-gpus 2 \
  --encoder-parallel replicate
```

## Interaction With Other Flags

* **Tensor / data parallel**: `dp` requires a replicated encoder, so it is
  skipped when `--tp-size > 1` or `--dp-size > 1`.
* **Dynamic batching**: `dp` only pays with a wide batch, so selecting it raises
  the default batching ceiling. See [Inference Batching](./dynamic_batching).
* **Sequence parallelism**: independent — SP splits the DiT's latent sequence,
  encoder parallelism splits the encoding stage. See
  [Sequence Parallelism](./ring_sp_performance).