Best GPU for ComfyUI in 2026 (Flux, SDXL, ControlNet)

Cross-posted from Best GPU for AI — visit the original for our VRAM calculator, GPU comparison table, and current Amazon pricing.

Quick answer: The RTX 4070 Ti Super (16GB) is the best GPU for ComfyUI for most users in 2026. It handles Flux Dev, SDXL, ControlNet stacks, and multi-LoRA workflows comfortably at fast generation speeds without RTX 4090 pricing.

See the recommended pick on the original guide

Why ComfyUI demands a proper GPU

ComfyUI is a node-based interface for running diffusion models locally. The power of ComfyUI is also what makes it GPU-hungry: you can chain multiple models together in a single workflow — a Flux or SDXL base checkpoint, one or more ControlNet preprocessors, LoRA adapters, upscalers, IP-Adapters, and VAE decoders all running sequentially in your node graph.

Each active node consumes VRAM. When your workflow exceeds available VRAM, ComfyUI starts swapping model data to system RAM, which turns a 10-second generation into a 2–5 minute crawl. A well-specced GPU doesn't just make ComfyUI faster — it makes complex workflows actually viable.

VRAM requirements by workflow complexity

ComfyUI workflows range from simple single-model runs to complex multi-model pipelines. VRAM needs scale with complexity:

The 16GB threshold is significant: it's the point where virtually every current ComfyUI workflow runs without memory-swapping. Cards below 16GB can run many workflows but hit walls with Flux + ControlNet combinations.

GPU comparison for ComfyUI

Performance across common ComfyUI workflows at 1024×1024, 20 steps:

*Flux Dev on 12GB requires FP8 quantization — without it, generation fails or takes minutes via CPU offloading.

Memory bandwidth is the hidden performance variable. Notice the RTX 4060 Ti 16GB has only 288 GB/s bandwidth despite 16GB VRAM — identical to the 4070 Ti Super in memory capacity but 2.3x slower in bandwidth. That's almost entirely why Flux Dev takes 18 seconds on the 4060 Ti vs 11 seconds on the 4070 Ti Super.

Understanding VRAM overhead in complex workflows

VRAM consumption in ComfyUI is additive. Here's how a typical Flux + ControlNet workflow stacks up:

This is why 16GB is tight and 24GB is comfortable for full Flux creative workflows. On a 16GB card, you may need to unload the ControlNet preprocessor node after generating the control image to free VRAM before running the main generation.

Multi-model loading and node graph optimization

ComfyUI's biggest advantage is explicit control over when models load and unload. Optimizing your node graph can recover 2–4GB of effective VRAM:

• Unload preprocessors after use — ControlNet preprocessors (Canny, depth, pose) stay loaded in VRAM by default. Add a node to unload them after generating the control map.
• Use SDXL Turbo or Lightning checkpoints for fast previews, then switch to full model for finals
• Checkpoint switching nodes allow swapping between models without reloading ComfyUI
• FP8 Flux checkpoints are drop-in replacements that use ~25% less VRAM with minimal quality loss
• KSampler settings — fewer steps for iteration (4–8 steps with Flux Schnell), full steps for final renders
• Queue size management — avoid queuing multiple generations on low VRAM cards, as queued jobs stack model instances

For Flux-specific ComfyUI workflows, using Flux Schnell for iteration and Dev for finals is the single most impactful optimization regardless of GPU.

ControlNet stacking: the 16GB wall

ControlNet is one of the most powerful ComfyUI features — it lets you control pose, depth, edges, and style with reference images. But it has real VRAM cost. For SDXL-specific VRAM numbers across all model sizes, see our Stable Diffusion VRAM requirements guide:

With Flux instead of SDXL, add ~4GB to all of these numbers. Running three active ControlNets simultaneously with Flux Dev exceeds 20GB — the 4090's 24GB handles it, the 4070 Ti Super's 16GB does not without node-level memory management.

LoRA stacking in ComfyUI

LoRA adapters are smaller than ControlNets but still consume VRAM, especially when stacked:

• Each LoRA adapter: ~100–500MB depending on rank and precision
• 3–4 LoRAs simultaneously: ~400MB–2GB total
• Style LoRAs + character LoRAs + concept LoRAs stacked: adds up fast

On a 16GB card, you can typically stack 3–5 LoRAs with SDXL or Flux without hitting memory limits, assuming you're not also running multiple ControlNets. The combination of 3 LoRAs + 2 ControlNets + Flux Dev is where 24GB becomes necessary.

Batch rendering in ComfyUI

Batch generation (multiple images per queue run) multiplies VRAM needs linearly:

For Flux Dev, batch size 2 already approaches 20GB — realistically requiring the RTX 4090. For professional workflows generating large batches, the 4090 is the starting point.

RTX 4090 — for complex workflows and batch work

If your ComfyUI workflows routinely stack multiple ControlNets, you generate in batches, or you train custom models through ComfyUI's training nodes:

• 24GB VRAM handles Flux + dual ControlNet + IP-Adapter without manual VRAM management
• ~6 second Flux Dev generation — significantly faster for iterating complex workflows
• Batch size 4–6 for SDXL generation (useful for generating variation grids)
• Handles AnimateDiff with Flux or SDXL for video generation — see our best GPU for AI animation guide for motion-specific workflows

See the recommended pick on the original guide

RTX 4060 Ti 16GB — cheapest path to 16GB

The RTX 4060 Ti 16GB at ~$400 has the same VRAM as the 4070 Ti Super but dramatically less bandwidth (288 GB/s vs 672 GB/s). This makes it significantly slower for Flux and complex workflows, but it's the cheapest way to get 16GB for ComfyUI. For a detailed look at what Flux workflows this card can handle, see can the RTX 4060 Ti run Flux?:

• Handles every SDXL + ControlNet workflow without VRAM issues
• Flux Dev at ~18 seconds per image — slow but functional
• Not suitable for batch generation or professional throughput
• Good for hobbyists generating a few dozen images per session

See the recommended pick on the original guide

Not sure what hardware you need? Test in the cloud first

Before spending $700–$1,600 on a GPU, test your specific ComfyUI workflow on cloud hardware. RunPod lets you rent RTX 4090s or H100s by the hour to benchmark your exact node graph.

For a broader look at image generation hardware, see our Best GPU for Stable Diffusion and Best GPU for Flux guides. Still deciding between ComfyUI and Automatic1111? Our Automatic1111 vs ComfyUI comparison covers the practical tradeoffs in UI complexity, node flexibility, and memory handling. If you prefer a polished creative interface with canvas editing, InvokeAI is also worth trying. For a lighter-weight alternative to ComfyUI, see our Forge UI GPU guide, or Fooocus for a no-knobs SDXL experience. For LoRA training rigs paired with ComfyUI generation, see best GPU for Kohya SS.

GPU tier list available at the original article

Which GPU should YOU buy for ComfyUI?

• You run simple SDXL workflows (base checkpoint, maybe one LoRA, no ControlNet): A used RTX 3060 12GB (~$250) is enough. Save your budget.
• You run SDXL with ControlNet or LoRA stacks, or you're getting started with Flux: 16GB is the sweet spot. The RTX 4070 Ti Super at $700 gives the best speed. The RTX 4060 Ti 16GB at $400 is the budget option with slower generation.
• You run Flux Dev with multiple ControlNets, IP-Adapters, or heavy LoRA stacks: You need 24GB. The RTX 4090 prevents out-of-memory errors on complex workflows.
• You generate large batches or train custom models through ComfyUI: RTX 4090 minimum. Batch size and training both scale directly with VRAM.
• You generate infrequently and want cheapest viable option: RTX 4060 Ti 16GB at $400 runs everything, just slower.

Common mistakes to avoid

1. Buying an 8GB GPU for ComfyUI in 2026. Flux requires 12GB minimum, and even SDXL with ControlNet pushes past 10GB. An 8GB card will constantly swap to system RAM and crawl.
2. Ignoring memory bandwidth. The RTX 4060 Ti 16GB and RTX 4070 Ti Super have identical VRAM, but the 4070 Ti Super has 2.3x more bandwidth — making it nearly 2x faster for Flux. VRAM capacity matters, but bandwidth determines how fast generation actually runs.
3. Overbuying for SDXL-only workflows. If you only run SDXL without Flux or complex ControlNet stacks, 12GB is plenty and you don't need a $700+ card. Be honest about your actual use case.
4. Not optimizing your node graph. Unloading preprocessors, using FP8 Flux checkpoints, and batching generations properly can recover 2–4GB of effective VRAM without spending anything.

Final verdict

See the recommended pick on the original guide

See the recommended pick on the original guide

ComfyUI rewards VRAM and memory bandwidth above all else. Get at least 16GB and you'll handle any mainstream workflow in 2026 without hitting a wall.

The best GPU for ComfyUI is the one with enough VRAM to keep all your active models loaded simultaneously — swapping to RAM kills the whole workflow.

Related guides on Best GPU for AI

• Best GPU for AI Art in 2026: Every Budget Compared
• How Much VRAM Do You Need for Stable Diffusion in 2026?
• Automatic1111 vs ComfyUI in 2026: Speed, VRAM, Workflow

Read the full guide on Best GPU for AI — includes our VRAM calculator, GPU comparison table, and live pricing.