Deployment and Performance Modes

Choose component residency, FSDP, CFG parallelism, SP, TP, and performance-mode presets in SGLang Diffusion.

This page gives practical defaults for choosing --performance-mode, component residency, FSDP, CFG parallelism, SP, and TP.

Quick Rule

Use the simplest setting that fits your memory target:

Goal	Recommended setting
Fastest single-GPU run when the model fits	Use resident components and do not use FSDP.
Lower single-GPU memory usage	Use component offload, then layerwise offload when a complete component still does not fit comfortably.
Faster multi-GPU Qwen/Wan CFG generation	Use FSDP with CFG parallelism and keep the sharded component resident.
Sequence length or video-shape scaling	Use SP/Ulysses/Ring when the model benefits from sequence parallelism.
TP compatibility or encoder-heavy paths	Set TP explicitly; do not treat TP as the default latency optimization.

Base the decision on available memory on the selected GPU(s).

• For multi-GPU deployment: the least-free selected GPU is the bottleneck. A busy 80GiB GPU can behave like a much smaller GPU.
• For single-GPU deployment: FSDP shards weights across multiple GPUs. It is not useful for keeping a single-GPU deployment on one GPU; use component or layerwise offload instead.

Health Probes

Use /liveness to check that the HTTP process is alive and /health to check that the server is ready for inference. During server-based warmup, /liveness returns 200 while /health returns 503. Configure the startup probe with a failure budget large enough for model loading and compilation:

startupProbe:
  httpGet:
    path: /health
    port: 30010
  periodSeconds: 10
  failureThreshold: 180
readinessProbe:
  httpGet:
    path: /health
    port: 30010
livenessProbe:
  httpGet:
    path: /liveness
    port: 30010

See Health endpoints for the status-code contract and warmup-mode behavior.

Stable Model Identity

Use --served-model-name when the public model name must remain stable across replicas, hosts, or checkpoint mount paths:

sglang serve \
  --model-path /mnt/checkpoints/Qwen-Image \
  --model-id Qwen-Image \
  --served-model-name image-production \
  --port 30010

The resolved public name follows --served-model-name, then --model-id, then --model-path. --model-id remains an internal model registry and configuration-resolution hint; it is not a replacement for a deployment alias. The resolved name is exposed through /server_info and /v1/models and is used by video and action responses when a request does not supply its own model.

See OpenAI API: Served model name for discovery and retrieval examples.

Performance Modes

--performance-mode applies safe presets without overriding explicit offload, FSDP, or parallelism flags. auto is the default. Use manual when you need to keep performance-related server args under explicit user control. --mode is a short alias.

Mode	Meaning
manual	Keeps performance-related server args under explicit user control.
auto	Default. Keeps legacy safe offload defaults and uses FSDP/CFG only on validated multi-GPU deployments where FSDP can replace DiT offload.
speed	Favors GPU-resident execution for lower latency and higher throughput. Disables CPU offload when unset. torch.compile stays off unless the model has a validated default or it is enabled explicitly; may OOM.
memory	Favors lower GPU memory. Uses component offload, or Wan/MOVA layerwise DiT offload when supported.

auto checks selected GPU memory before applying FSDP. In multi-GPU runs it uses the least available memory across selected GPUs, and only turns on FSDP automatically when doing so can replace DiT offload. For image workloads with at least 45 GiB available per selected GPU, it keeps the repeatedly reused DiT resident and uses layerwise offload for large auxiliary encoders; below that threshold it keeps the DiT offloaded. Model-specific components such as VAEs become resident only when their configured memory threshold is met. memory instead keeps the VAE in its default layerwise set to maximize memory headroom; use --component-residency vae=resident for a measured recipe with sufficient capacity. Video DiT residency remains model- and workload-specific because frame count and resolution change its peak memory substantially. When the model default uses CFG and the user did not set a parallelism policy, auto may also enable CFG parallelism. speed intentionally does not check memory; it is the mode for users who prefer latency/throughput and accept OOM risk. It keeps torch.compile disabled by default because its effect varies by model and workload. A model-specific deployment config may enable a validated compile path, and --enable-torch-compile true always opts in explicitly.

The modes tune native pipeline components declared to the component residency manager. DiTs, text/image encoders, VAEs, vocoders, adapters, and upsamplers can use layerwise offload when their native module declares its executable layer structure. Explicitly selecting an unsupported component fails at startup instead of falling back to another residency mode.

For direct control, assign one of resident, component-offload, or layerwise-offload with --component-residency COMPONENT=MODE:

sglang generate \
  --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers \
  --component-residency dit=layerwise-offload text_encoder=component-offload vae=resident

Existing per-component CPU-offload and layerwise flags remain supported. Canonical selectors override matching legacy settings only; unmatched legacy settings and automatic defaults remain effective. See Component Residency for the complete precedence rules.

When torch.compile is enabled, --offload-during-compile stays on by default. During compile warmup it temporarily offloads the DiT and evicts resident non-DiT components so max-autotune fits on tighter-memory GPUs, then restores the configured serving residency before real traffic.

Breakable CUDA graph is a separate manual opt-in for supported image pipelines. If you enable --enable-breakable-cuda-graph, declare every served resolution in --warmup-resolutions so warmup captures matching graph signatures.

The preset is intentionally coarse. A future continuous value such as 0.0 to 1.0 could express the speed-memory tradeoff more precisely, but it would need model-specific memory models and clearer user expectations. Until then, use the preset plus explicit flags for overrides.

Examples:

sglang generate \
  --model-path Qwen/Qwen-Image \
  --num-gpus 2 \
  --performance-mode auto

sglang generate \
  --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
  --performance-mode memory

Explicit flags win over the mode:

sglang generate \
  --model-path Qwen/Qwen-Image \
  --num-gpus 2 \
  --performance-mode auto \
  --use-fsdp-inference false

In this example, auto will not re-enable FSDP. The same applies to parallelism; for example, --enable-cfg-parallel false keeps CFG parallelism disabled.

Interpreting The Levers

Resident keeps the complete component on the accelerator. It is usually fastest when memory is sufficient.

Component offload keeps a complete component on CPU between declared uses. It is simple and robust, but each use pays a whole-component transfer.

Layerwise offload streams the declared layers of any supported native weighted component. It lowers peak accelerator memory further, but may increase latency and lower throughput.

FSDP shards DiT weights across multiple GPUs and all-gathers weights during forward. It can reduce DiT CPU offload cost on multi-GPU deployments, especially for validated Wan I2V workloads.

FSDP sharding granularity matters. SGLang Diffusion prefers sharding direct repeated transformer block entries such as transformer_blocks.0 or blocks.0. Coarser sharding lowers wrapper count but can increase all-gather peak memory; finer sharding can reduce transient memory but adds communication and scheduling overhead. If a model does not define an explicit sharding rule, the loader falls back to repeated block class names and common direct numbered block paths.

CFG parallelism splits positive and negative CFG branches across GPUs. For Qwen/Wan workloads with normal step counts, this is the most reliable multi-GPU speedup observed so far.

SP/Ulysses/Ring splits sequence work. It can help video workloads, but validated Qwen/Wan runs showed CFG parallelism outperforming SP for latency.

TP is supported for compatibility and some model structures, but current measurements do not make it the default latency path for Qwen/Wan.

Current Benchmark Takeaways

Observed regular-scale trends:

• Z-Image: single-GPU no-offload was faster than FSDP/SP in the tested setting; keep FSDP off unless memory or parallelism requires it.
• Qwen-Image: keep the default non-FSDP path unless a specific FSDP/SP/Ring setting has been benchmarked on the target hardware.
• Wan: FSDP can replace DiT offload on validated multi-GPU workloads, while text/image encoders may still need component offload. Keep model-specific precision checks before making FSDP automatic for a path.
• Component offload mainly reduced memory; it did not improve latency in the tested no-offload-vs-offload runs.

Always benchmark with your actual resolution, frame count, step count, and GPU type before locking production defaults.