H3 Acceleration Options Compared: What to Pick, and What Stacks

Updated 2026-09 · Speed data from public benchmarks and our own regressions

H3 acceleration comes in three layers, and many tutorials lump them together — which is why you end up asking "why didn't stacking make it faster?" or "why did quality collapse?". Remember three categories first: step distillation (fewer sampling steps), attention backends (faster per-step compute), and caching (skipping part of the compute).

Layer 1: Step distillation (pick only one!)

Method	Steps	Relative speed	Notes & dependencies
Official baseline	20	1.0×	Most stable quality, slowest
LightX2V Turbo	8 / 4	~2.1× / 3.4×	Built-in turbo_mode; 4 steps loses a little detail; the community favorite
larryvrh Turbo v4	8	~2.1×	Requires the dual-clock sampler (dual audio schedule); well regarded
FastH3 (FastVideo)	4	~4×	DMD2 + VSA sparse distillation; on ComfyUI you need the converted LoRA
TaoMate-H3	3	Fastest tier	Targets streaming long video; bigger quality cost
Alibaba PAI PDD	8 / 4	~2×	Official distillation; must use the dedicated nodes and dedicated sigmas, and cannot stack with other distillation

You can only pick one distillation method: they all alter the sampling trajectory, so stacking them ruins each other's effect, and each one takes extra VRAM on its own. PDD also needs an additional custom node pack (it can read the official original files directly).

Layer 2: Attention backends (stackable)

SageAttention: the default recommendation — 1.3-2× faster sampling, almost no quality loss. Note: 50-series cards risk silent noise on very long sequences (roughly 160k tokens or more); for long videos, switch to a sparse backend or Kitchen;
comfy-kitchen built-in attention: bundled with ComfyUI 0.32+, same speed tier as Sage; pick it if you don't want to install anything;
SLA sparse attention: requires the H3 SLA Attention node (Triton) used together with the SLA-trained Turbo LoRA as a set; measured on long videos at 44→25 s per step (5090 768p/15s), with limited gains on short clips;
VSA (FastVideo Video Sparse Attention): the 90% sparse attention from the same source as the FastH3 4-step distillation; needs the VSA node, or enable it with the latest ComfyUI core Block Sparse Attention node (comfy-kitchen ≥0.2.33).

Layer 3: Caching (comes at a quality cost)

Step caches like EasyCache / Spectrum save another 15-40% of the time, but they work by "skipping part of the compute", so you need to spot-check frames; they conflict with PDD (they disrupt PDD's sigma boundaries). Conditional caching (e.g. CLIPCached) is lossless: when you generate several seeds in a row from the same prompt, from the second run on it skips the entire text-encoding stage.

Recommended combinations

Your goal	Recommended combo
Fast idea scouting	LightX2V 4 steps + Sage (or Kitchen)
Balance of quality and speed	LightX2V 8 steps / larry 8 steps + Sage
Maximum speed (quality loss acceptable)	FastH3 4 steps + VSA (or Sage)
Official distillation route	PDD 8 steps + dedicated nodes (do not stack with any distillation/caching)
Batch output from the same prompt	Any combo above + CLIPCached conditional caching

In step 2 of the calculator you can toggle these methods one by one: it automatically flags conflicts (for example, PDD can't be selected together with other distillation) and reflects the accelerated runtime in the results matrix. All the files live in the "Acceleration" category of the model library.

Related pages: RTX 5090 with acceleration · RTX 4070 recommended combo · INT8 HQ — compatible GPUs