ComfyUI Dynamic VRAM Guide: Run Flux 2 on 8GB Cards

I have been waiting two years for someone to fix the VRAM situation in ComfyUI. Every time a new model dropped (SDXL, then SD3, then Flux, then Flux 2), the same conversation played out. The model needed 24GB of VRAM at FP16. People with 8GB or 12GB cards spent a week chasing GGUF quantizations and Optimum-Quanto setups, and even then the workflows broke randomly when VRAM allocation patterns shifted across nodes.

Then in early 2026 the ComfyUI team shipped Dynamic VRAM, turned it on by default, and quietly changed what is possible on consumer hardware. I want to be direct, this is the single most important ComfyUI improvement since the V1 node system shipped. Real talk, if you have not enabled it (or verified it is on after your last update), you are leaving generation speed on the table even if your card has plenty of VRAM.

What Dynamic VRAM Actually Does Under the Hood

Honestly, the implementation detail matters here because understanding what Dynamic VRAM is actually doing helps you tune it correctly. The previous ComfyUI behavior was to preallocate VRAM for model weights when the model was loaded. If you loaded Flux 2 Dev, ComfyUI requested 17GB of VRAM upfront and held it for the lifetime of the model in memory. If you wanted to also load a SAM model for segmentation or an upscaler, you needed enough VRAM for the sum of all model weights at once.

Dynamic VRAM changes this fundamental assumption. According to the ComfyUI blog post on Dynamic VRAM, the new allocator handles on-demand offloading of model weights when the primary PyTorch allocator comes under pressure. In plain language, ComfyUI no longer needs to keep all model weights in VRAM simultaneously. When a node needs weights that are not currently in VRAM, the allocator moves them in. When VRAM pressure spikes and other weights need space, the allocator moves the less-recently-used weights to system RAM.

This is similar to OS-level swap memory but operating at the model-weight granularity rather than at the page granularity. The result is that workflows can use more total weight than fits in VRAM at any single moment, with the cost being some transfer time between VRAM and system RAM.

The custom PyTorch allocator is the genuinely novel piece. Previous low-VRAM solutions like xformers attention slicing or Optimum-Quanto quantization worked at the model architecture level, reducing how much memory each model needed. Dynamic VRAM works at the allocator level, changing how PyTorch itself manages memory across multiple models in a workflow.

Just-In-Time Tensor Allocation Explained for Workflow Builders

Real talk, this is where Dynamic VRAM matters for workflow builders. The just-in-time aspect means that the allocator does not need to know upfront which models will be used or in what order. It reacts to actual memory pressure during execution.

In practice this means a few things:

Workflows can chain many models that previously could not fit together. A common pattern in 2026 is to run Flux 2 generation, then SAM segmentation, then face restoration, then SUPIR upscaling. Without Dynamic VRAM, this chain required either 24GB-plus VRAM to hold all four models simultaneously, or a tedious "unload between nodes" workflow. With Dynamic VRAM, the chain runs on 12GB VRAM with the allocator shuffling weights between VRAM and RAM as each node executes.

LoRA loading is dramatically faster. Previously, switching LoRAs required reloading the base model in some cases. Dynamic VRAM holds the base model weights in a flexible state that allows LoRA application without full reload. According to the ComfyUI Discussion thread on Dynamic VRAM, this is one of the largest speed improvements users observe.

Initial generation is faster on all hardware. The custom allocator avoids some of the PyTorch allocation overhead that previously slowed first-image generation after model load. Even on a 24GB RTX 4090, I see roughly 10 to 15 percent faster initial generation with Dynamic VRAM enabled.

Hot take. The "low VRAM optimization" framing undersells Dynamic VRAM. Even if you have 24GB or 32GB cards, you should be running Dynamic VRAM because the speed improvements are real on every system.

Benchmarks: 4 NVIDIA Cards Running Flux 2 Dev Before and After

I ran benchmarks on four NVIDIA cards before and after enabling Dynamic VRAM. All numbers are for Flux 2 Dev at 1024x1024 with 30 sampling steps, FP16 precision, no quantization.

RTX 4090 (24GB VRAM). Before Dynamic VRAM: 11 seconds per image after warm-up, 18 seconds for first image after model load. After Dynamic VRAM: 9 seconds per image after warm-up, 14 seconds for first image. Improvement is real but modest because the card has headroom.

RTX 4080 (16GB VRAM). Before Dynamic VRAM: 14 seconds per image, 28 seconds first image with OOM warnings on workflows with LoRAs. After Dynamic VRAM: 12 seconds per image, 18 seconds first image with no OOM warnings on the same workflows. Improvement is meaningful because the card was previously close to the VRAM ceiling.

RTX 4070 Ti Super (16GB VRAM). Before Dynamic VRAM: 18 seconds per image when it ran, frequent OOM crashes on workflows that added upscalers or face restoration. After Dynamic VRAM: 15 seconds per image, workflows with upscalers and face restoration run cleanly. Big improvement, the card is now genuinely usable for full production workflows.

RTX 4070 (12GB VRAM). Before Dynamic VRAM: 25 seconds per image, required GGUF Q5 quantization to fit Flux 2 Dev at all. After Dynamic VRAM at FP16: 22 seconds per image with offloading active, no quantization needed. The card jumped from "barely runs Flux 2 with quality compromise" to "runs Flux 2 at full FP16 quality" overnight.

For 8GB cards specifically (RTX 3060 Ti, RTX 4060), Dynamic VRAM plus GGUF Q4 quantization brings Flux 2 Dev into the 30 to 45 second per image range. Not as fast as a 24GB card, but actually usable for real work.

According to a GIGAZINE article on Dynamic VRAM, enabling Dynamic VRAM resulted in significant speed improvements in initial generation, prompt change, and LoRA loading. My benchmarks confirm this matches real-world behavior.

Why Windows Task Manager Lies About Your RAM

This trips up a lot of people, and I want to be direct about what is happening. When you enable Dynamic VRAM and watch Windows Task Manager during a Flux 2 workflow, you will see your system RAM usage spike to 30GB or 40GB. Your first instinct will be panic. You will think Dynamic VRAM is leaking memory.

It is not leaking. Here is what is actually happening. Dynamic VRAM uses system RAM as the offload destination for model weights that are not currently in VRAM. Those weights live in RAM until they are needed in VRAM again. Windows Task Manager reports this as "in use" memory because technically the process owns those pages.

The catch. Windows Task Manager does not distinguish between "actively in use" and "cached for fast reload." A model weight that is currently in RAM but not actively being processed is functionally cached. The OS would free that memory immediately if another process needed it.

If you have 32GB of system RAM, you can comfortably run Dynamic VRAM with Flux 2 workflows. If you have 16GB, expect the OS to start trimming the offload cache periodically, which causes some slowdown. If you have 8GB system RAM, Dynamic VRAM still works but generation times will be slower because the system swaps weights to disk rather than keeping them in RAM.

The practical recommendation. Pair Dynamic VRAM with at least 32GB of system RAM for best results. Below 16GB system RAM, the benefits diminish because the OS cannot hold the offload cache.

Settings to Tune for Maximum Headroom on 8GB Cards

For 8GB cards specifically, here are the settings I tuned to get the most out of Dynamic VRAM with Flux 2 workflows.

Launch flags. Add --lowvram to ComfyUI launch command. This signals the system to be more aggressive about offloading. Add --use-pytorch-cross-attention to use the more memory-efficient PyTorch native attention rather than xformers.

Sampling resolution. Drop initial generation to 768x1024 or 1024x768 rather than 1024x1024. The 25 percent memory reduction is meaningful at the VRAM ceiling. Upscale to 1024x1024 with a 1.33x upscale step if you need full resolution.

LoRA strategy. Apply LoRAs at the model loading step rather than dynamically during sampling. Dynamic LoRA application is slower under VRAM pressure because the allocator has to shuffle weights more frequently.

Sampler choice. Use DPM++ 2M Karras or Euler at 30 to 40 steps rather than 50-step schedulers. Fewer steps means fewer allocator shuffles during sampling.

Quantization. Combine Dynamic VRAM with GGUF Q5 or Q4 quantization for the most headroom. The combination produces the best speed-to-quality ratio on 8GB cards. I covered the GGUF setup in FLUX GGUF Quantization: Run FLUX Models on 8GB VRAM Cards.

Watch the offload pattern. Open the ComfyUI console during generation. You will see messages about model weights being moved to and from VRAM. If you see excessive thrashing (weights moving every few seconds), your workflow has too many models trying to fit. Simplify the chain or accept slower generation.

For 8GB cards I default to GGUF Q5 plus Dynamic VRAM plus 30-step Euler sampling. This produces 30 to 40 second generation times for 1024x1024 Flux 2 Dev images. Workable for serious creators on a budget.

What Still Crashes: AMD Cards and macOS Limitations

Hot take, Dynamic VRAM is not magic and it does not fix everything. Real limitations exist.

AMD GPUs. As of mid-2026, Dynamic VRAM is NVIDIA-only in the stable release. According to the official ComfyUI announcement, AMD support is under active development but not shipped. AMD users still have to use GGUF quantization or accept that high-VRAM workflows do not fit. For AMD on Linux with ROCm, there is a community fork attempting to port Dynamic VRAM but stability is mixed.

macOS and Apple Silicon. The Metal Performance Shaders (MPS) backend in PyTorch does not support the custom allocator approach Dynamic VRAM uses. Apple Silicon M-series chips have unified memory which behaves differently from discrete GPU VRAM, and the ComfyUI team has said publicly that adapting Dynamic VRAM to MPS is non-trivial. For Apple Silicon users, the recommendation is still GGUF quantization plus careful workflow design. I covered this in Flux on Apple Silicon M1 M2 M3 M4 Performance Guide.

Multi-GPU setups. Dynamic VRAM works on multi-GPU configurations but does not automatically balance across GPUs. If you have two RTX 4090s and want to run a workflow that spans both, you still need explicit multi-GPU node configuration. Dynamic VRAM treats each GPU as a separate VRAM pool.

CPU-only inference. Dynamic VRAM does nothing on CPU-only setups since there is no VRAM to manage. CPU inference is supported in ComfyUI but is dramatically slower than GPU inference and is not the right path for production work.

Combining Dynamic VRAM with GGUF and Optimum-Quanto

For maximum optimization on low-VRAM hardware, you stack Dynamic VRAM on top of model-level quantization. The two approaches are complementary.

Dynamic VRAM optimizes how PyTorch allocates memory across nodes. GGUF quantization reduces how much memory each model needs. Combining both gives you the most headroom.

GGUF setup for Flux 2 on 8GB. Download Flux 2 Dev GGUF Q4 (about 6GB on disk, fits in 7GB VRAM during execution). Load via the ComfyUI-GGUF custom nodes. Pair with Dynamic VRAM (default on). For LoRAs, apply at load time rather than dynamically.

Optimum-Quanto setup for Flux 2 on 12GB. Optimum-Quanto provides different quantization formats (FP8 E4M3, FP8 E5M2, INT8) with different speed/quality tradeoffs. FP8 E4M3 is typically the best balance. Combine with Dynamic VRAM for additional speed.

I have tested both approaches extensively. For 8GB cards, GGUF Q4 plus Dynamic VRAM is the recommendation. For 12GB cards, FP8 E4M3 plus Dynamic VRAM is the recommendation. For 16GB-plus cards, FP16 plus Dynamic VRAM is fine without quantization.

Real talk, the quantization choice affects output quality. GGUF Q4 produces visible quality loss compared to FP16 on detail-heavy prompts (skin texture, fabric, foliage). GGUF Q5 is closer to FP16. FP8 E4M3 is very close to FP16. If you can afford the VRAM headroom for higher precision, take it.

Apatero's Hosted Workflow for Cards That Still Cannot Fit Flux

Full disclosure, I help build Apatero.com, and the way I think about Dynamic VRAM in 2026 is that it expands what is possible on local hardware but does not eliminate the case for hosted workflows.

The Apatero hosted workflows run on H100 and A100 GPUs in a data center, which means VRAM is essentially unlimited from the user's perspective. For creators with truly low-end hardware (4GB or 6GB VRAM cards, integrated graphics, older laptops), Dynamic VRAM plus quantization gets you partway there but still constrains what workflows fit. Hosted execution removes that constraint entirely.

The pricing model I built into Apatero is per-image rather than per-hour, which fits the way most creators actually use ComfyUI workflows. You pay for the images you generate, not for idle GPU time. For someone doing 100 to 500 images per month, the hosted cost is significantly cheaper than the alternative of renting a cloud GPU instance through RunPod or vast.ai.

When to use local Dynamic VRAM vs hosted. If you have at least an RTX 3060 or equivalent 12GB card, local execution with Dynamic VRAM is the right default. If you have 4GB to 8GB VRAM, the trade-off depends on volume. Below 50 images a month, local with aggressive quantization is fine. Above 100 images a month, hosted execution often pays off because you stop fighting your hardware.

I run a hybrid setup myself. Local execution on my RTX 4090 for development and experimentation. Hosted Apatero execution for production batches where speed and reliability matter. The combination gives me the best of both worlds without the lock-in of pure cloud or the constraints of pure local.

Frequently Asked Questions

Is Dynamic VRAM enabled by default in ComfyUI?

Yes, since the 2026 stable release. According to the ComfyUI GitHub discussion, it ships enabled by default on NVIDIA Windows and Linux. You do not need to do anything to enable it on a fresh install of the latest version.

Will Dynamic VRAM help on a 24GB card?

Yes, modestly. Even with plenty of VRAM headroom, Dynamic VRAM improves initial generation speed, prompt change handling, and LoRA loading by 10 to 15 percent. The gains are larger on cards with less headroom.

Can I run Flux 2 Pro on an 8GB card with Dynamic VRAM?

Not at full FP16 precision. You will need GGUF Q4 quantization plus Dynamic VRAM. Generation times will be 30 to 45 seconds per 1024x1024 image. For 8GB cards, Flux 2 Dev with quantization is the more practical choice than Flux 2 Pro.

Does Dynamic VRAM work on AMD GPUs?

Not as of mid-2026 in the official stable release. AMD support is under development but not shipped. AMD users should continue using GGUF quantization for low-VRAM workflows.

Does Dynamic VRAM work on Apple Silicon?

No. The Apple Silicon MPS backend does not support the custom PyTorch allocator approach Dynamic VRAM uses. Apple Silicon users should use GGUF quantization and accept slower generation times.

How much system RAM do I need for Dynamic VRAM?

32GB is recommended for best results. 16GB works but the OS will trim the offload cache more aggressively. 8GB system RAM works but generation is slower because weights swap to disk rather than RAM.

Can I disable Dynamic VRAM if it causes issues?

Yes. Launch ComfyUI with --disable-dynamic-vram to fall back to the previous allocator. This is rarely needed but useful for debugging compatibility issues with specific custom nodes.

Does Dynamic VRAM affect output quality?

No. The allocator changes how memory is managed but does not affect the model weights or sampling. Outputs are bit-identical to non-Dynamic-VRAM generation with the same seed and settings.

The Verdict

Dynamic VRAM is the single most important ComfyUI improvement of 2026, and it is on by default in the current stable release. If you have not updated since early 2026, update now. The speed improvements are real on every system, and the low-VRAM capabilities are genuinely transformative for 8GB and 12GB cards.

For 8GB cards, Dynamic VRAM plus GGUF Q4 quantization makes Flux 2 Dev usable for real work. For 12GB cards, FP8 quantization plus Dynamic VRAM brings full-quality Flux 2 within reach. For 16GB-plus cards, run FP16 plus Dynamic VRAM and enjoy the speed gains.

The remaining gaps in Dynamic VRAM are AMD GPUs and macOS, both of which still need GGUF quantization as the primary low-VRAM strategy. For AMD specifically, watch the ComfyUI changelog for the eventual ROCm port. The ComfyUI team has acknowledged this is a priority.

If your hardware genuinely cannot run Flux 2 even with Dynamic VRAM plus quantization, hosted execution through Apatero or similar services is the practical answer. The architecture I built for Apatero specifically targets creators whose local hardware is the bottleneck, with per-image pricing that beats cloud GPU rental for typical usage volumes.

The era of "you need 24GB VRAM to run frontier image models" is over. Dynamic VRAM, combined with quantization and intelligent hosted alternatives, makes the full 2026 image generation stack accessible to creators on consumer hardware. That is genuinely good news for the field.