Vision Encoder (ViT) CUDA Graphs¶

The CUDA Graphs infrastructure in vLLM primarily targets the decoder (language model) forward pass. vLLM also supports capturing the encoder (vision transformer) forward pass as CUDA Graphs, independently from the decoder. This is based on Pull Request #35963.

Motivation¶

Vision encoder inference incurs CUDA kernel launch overhead on the host side. The overhead is more significant when the batch size is small or image size is small.

Encoder CUDA Graphs eliminate this overhead by pre-capturing the full encoder forward pass at multiple token budget levels during model initialization, then replaying the appropriate graph at runtime.

Design¶

The encoder CUDA Graph system uses a budget-based capture/replay strategy, managed by EncoderCudaGraphManager. The system contains the following core components:

• EncoderCudaGraphManager: orchestrates capture, replay, greedy packing, and data-parallel execution for encoder CUDA Graphs.
• SupportsEncoderCudaGraph: a runtime-checkable protocol that models implement to opt-in to encoder CUDA Graphs.
• BudgetGraphMetadata: holds the captured CUDA Graph and its associated I/O buffers for a single token budget level.

Budget-based graph capture¶

Multiple CUDA Graphs are pre-captured at different token budget levels (e.g., [2048, 4096, 8192, 13824]). Each budget defines a fixed token capacity, and all budgets share the same maximum batch size (number of images). The BudgetGraphMetadata for each level stores the graph along with pre-allocated input, metadata, and output buffers:

@dataclass
class BudgetGraphMetadata:
    token_budget: int
    max_batch_size: int
    max_frames_per_batch: int
    graph: torch.cuda.CUDAGraph
    input_buffer: torch.Tensor       # e.g. pixel_values
    metadata_buffers: dict[str, torch.Tensor]  # e.g. embeddings, seq metadata
    output_buffer: torch.Tensor      # encoder hidden states

Budgets are auto-generated as power-of-2 levels from a model-provided range via get_encoder_cudagraph_budget_range(), with the maximum budget always included even if it does not fall on a power-of-2 boundary. Budgets can also be explicitly specified by the user via encoder_cudagraph_token_budgets in CompilationConfig.

Greedy bin-packing at runtime¶

When a batch of images arrives, the manager sorts images by output token count (smallest first) and greedily packs as many images as possible into each sub-batch while staying within the largest token budget and the maximum batch size. Once a sub-batch is finalized (the next image would overflow either constraint), the manager finds the smallest budget that fits the sub-batch's total tokens and replays the corresponding CUDA Graph. This repeats until the batch is exhausted. Images that exceed all budgets fall back to eager execution.

For each graph replay:

1. Zero the pre-allocated input_buffer, then copy input tensors (e.g., pixel_values) into it.
2. Zero metadata_buffers, then slice-copy precomputed values (e.g., rotary embeddings, sequence metadata).
3. Replay the CUDA Graph.
4. Clone outputs from output_buffer (cloning is necessary since the buffer is reused across replays).

Data-parallel support¶

When mm_encoder_tp_mode="data", the manager distributes images across TP ranks using load-balanced assignment via get_load_balance_assignment, executes locally on each rank, then gathers results back in the original order via tensor_model_parallel_all_gather.

Video inference support (experimental)¶

Following Pull Request #35963 (ViT full CUDA graph support for image inference), Pull Request #38061 extends the encoder CUDA graph framework to support video inference for Qwen3-VL. Previously, the CUDA graph capture/replay path only handled image inputs (pixel_values + image_grid_thw). Video inputs use different keys (pixel_values_videos + video_grid_thw) and require larger cu_seqlens buffers because each video item contributes multiple frames (T attention sequences). This PR generalizes the protocol and manager to handle both modalities through a single shared graph manager.

Model integration via SupportsEncoderCudaGraph¶

Models opt-in to encoder CUDA Graphs by implementing the SupportsEncoderCudaGraph protocol. This protocol encapsulates all model-specific logic so that the manager remains model-agnostic. The protocol defines the following methods:

• get_encoder_cudagraph_config() — returns static configuration (supported modalities, input key, buffer keys, output hidden size).
• get_encoder_cudagraph_budget_range(vllm_config) — returns (min_budget, max_budget) for auto-inference of token budgets.
• get_encoder_cudagraph_num_items(mm_kwargs) — returns the number of items (e.g. images) in the batch.
• get_encoder_cudagraph_per_item_output_tokens(mm_kwargs) — returns per-item output token counts, used for greedy packing.
• get_encoder_cudagraph_per_item_input_sizes(mm_kwargs) — returns per-item input sizes (e.g. patch counts), used for DP load balancing.
• select_encoder_cudagraph_items(mm_kwargs, indices) — extracts a sub-batch of items by index, used during greedy packing and DP sharding.
• prepare_encoder_cudagraph_capture_inputs(...) — creates dummy inputs for graph capture.
• prepare_encoder_cudagraph_replay_buffers(...) — computes new buffer values from actual batch inputs before replay.
• encoder_cudagraph_forward(...) — forward pass using precomputed buffers (called during capture and replay).
• encoder_eager_forward(...) — fallback eager forward when no graph fits.
• get_input_modality(...) - return the modality of the inputs.

Supported models:

Configuration¶

Three fields in CompilationConfig control encoder CUDA Graphs:

• cudagraph_mm_encoder (bool, default False) — enable CUDA Graph capture for multimodal encoder. When enabled, captures the full encoder forward as a CUDA Graph for each token budget level.
• encoder_cudagraph_token_budgets (list[int], default []) — token budget levels for capture. If empty (default), auto-inferred from model architecture as power-of-2 levels. User-provided values override auto-inference.
• encoder_cudagraph_max_vision_items_per_batch (int, default 0) — maximum number of images/videos per batch during capture. If 0 (default), auto-inferred as max_budget // min_budget.
• encoder_cudagraph_max_frames_per_batch (int, default 0) — maximum number of video frames per batch during capture. If 0 (default), auto-inferred as encoder_cudagraph_max_vision_items_per_batch * 2 (to be optimized).

Usage guide¶

Image inference¶

Enable encoder CUDA Graphs via compilation_config:

vllm serve Qwen/Qwen3-VL-32B \
  --compilation-config '{"cudagraph_mm_encoder": true}'

With explicit budgets:

vllm serve Qwen/Qwen3-VL-32B \
  --compilation-config '{"cudagraph_mm_encoder": true, "encoder_cudagraph_token_budgets": [2048, 4096, 8192, 13824], "encoder_cudagraph_max_vision_items_per_batch": 8}'

Python example:

import vllm

compilation_config = {
    "cudagraph_mm_encoder": True,
    # Optional: override auto-inferred budgets
    # "encoder_cudagraph_token_budgets": [2048, 4096, 8192, 13824],
    # "encoder_cudagraph_max_vision_items_per_batch": 8,
}

model = vllm.LLM(
    model="Qwen/Qwen3-VL-32B",
    compilation_config=compilation_config,
)

The manager tracks hit/miss statistics and logs them periodically. A "hit" means an image was processed via CUDA Graph replay; a "miss" means eager fallback (image exceeded all budgets).

Video inference¶

Enable encoder CUDA Graphs via compilation_config:

vllm serve Qwen/Qwen3-VL-32B \
  --limit-mm-per-prompt '{"image": 0}' \
  --compilation-config '{"cudagraph_mm_encoder": true}'

With explicit budgets:

vllm serve Qwen/Qwen3-VL-32B \
  --limit-mm-per-prompt '{"image": 0}' \
  --compilation-config '{"cudagraph_mm_encoder": true, "encoder_cudagraph_token_budgets": [2048, 4096, 8192, 13824], "encoder_cudagraph_max_vision_items_per_batch": 8, "encoder_cudagraph_max_frames_per_batch": 64}'

Python example:

import vllm

compilation_config = {
    "cudagraph_mm_encoder": True,
    # Optional: override auto-inferred budgets
    # "encoder_cudagraph_token_budgets": [2048, 4096, 8192, 13824],
    # "encoder_cudagraph_max_vision_items_per_batch": 8,
    # "encoder_cudagraph_max_frames_per_batch": 64,
}

model = vllm.LLM(
    model="Qwen/Qwen3-VL-32B",
    limit_mm_per_prompt='{"image": 0}',
    compilation_config=compilation_config,
)

About the Performance¶

The following benchmarks were run on Blackwell GPUs (GB200) using vllm bench mm-processor. See #35963 for full details.

Single GPU (1x GB200)¶

Model: Qwen/Qwen3-VL-30B-A3B-Instruct, dataset: lmarena-ai/VisionArena-Chat (3000 prompts, 300 warmup), max_model_len=32768.

To reproduce:

vllm bench mm-processor \
  --model Qwen/Qwen3-VL-30B-A3B-Instruct \
  --dataset-name hf --dataset-path lmarena-ai/VisionArena-Chat \
  --num-prompts 3000 --num-warmups 300 \
  --max-model-len 32768 --seed 42 \
  --mm-encoder-attn-backend FLASH_ATTN \
  --compilation-config '{"cudagraph_mm_encoder": true, "encoder_cudagraph_token_budgets": [512, 1024, 1536, 2048, 2560, 3072, 3584, 4096, 4864], "encoder_cudagraph_max_vision_items_per_batch": 8}'

Multi-GPU (4x GB200, TP=4, DP=4)¶

Model: Qwen/Qwen3-VL-32B-Instruct, dataset: random-mm (1000 prompts, 200 warmup, 20 images/request at 336x336), max_model_len=8192.

To reproduce:

vllm bench mm-processor \
  --model Qwen/Qwen3-VL-32B-Instruct \
  --dataset-name random-mm \
  --random-mm-base-items-per-request 20 \
  --random-mm-num-mm-items-range-ratio 0.0 \
  --random-mm-bucket-config '{"(336,336,1)": 1.0}' \
  --num-prompts 1000 --num-warmups 200 \
  --max-model-len 8192 --seed 42 \
  --mm-encoder-attn-backend FLASHINFER \
  --tensor-parallel-size 4 --mm-encoder-tp-mode data \
  --compilation-config '{"cudagraph_mm_encoder": true, "encoder_cudagraph_token_budgets": [512, 1024, 1536, 2048, 2560, 3072, 3584, 4096, 4864], "encoder_cudagraph_max_vision_items_per_batch": 8}'