Run Flux 2 on 24GB VRAM: Optimal Settings for RTX 3090 and 4090

I bought my RTX 3090 expecting to never think about VRAM again. 24GB felt limitless after years on 8GB cards. Then Flux 2 released and my "limitless" card hit 23.8GB usage on the first generation. I crashed ComfyUI four times that day.

Having 24GB of VRAM doesn't mean Flux 2 "just works." It means you have enough headroom to run full precision without quantization—if you configure everything correctly. Miss a setting and you're right back to out-of-memory errors wondering why you spent $1,400 on this card.

After two weeks of systematic testing, logging every crash, and measuring VRAM usage across hundreds of configurations, I finally found the settings that let my 3090 perform like it should. Here's exactly what works—and the settings that seemed fine but caused random crashes hours into generation sessions.

Quick Answer: RTX 3090 and RTX 4090 with 24GB VRAM can run Flux 2 at 1024x1024 in FP16 precision using 18-20GB VRAM with generation times of 12-15 seconds on 3090 and 8-11 seconds on 4090. For maximum quality use FP16 precision with Euler sampler at 20-28 steps. For speed priority use BF16 precision with 15-20 steps or FP8 quantization for 40% faster generation.

Why 24GB VRAM Is the Sweet Spot for Flux 2

The AI image generation community obsesses over memory requirements, but most discussions focus on minimum specs or aspirational 48GB setups. 24GB VRAM occupies the practical middle ground where you stop fighting memory constraints and start building sophisticated workflows.

The Minimum VRAM Myth

You can technically run Flux 2 on 8GB VRAM with aggressive quantization, reduced precision, and resolution limits. Many tutorials claim this works fine. What they don't tell you is that your workflow becomes a constant memory management exercise. Every additional LoRA, every resolution bump, every batch beyond one requires recalculation and testing.

With 24GB VRAM you escape this trap. The Flux 2 model loaded in FP16 precision consumes approximately 18GB of VRAM at 1024x1024 resolution. This leaves you 6GB of headroom for system operations, additional models, and experimental nodes without constant crashes.

What 24GB Enables Beyond Basic Generation

Memory headroom translates to workflow flexibility that lower VRAM cards simply cannot match. You can load ControlNets alongside Flux 2 for pose or depth conditioning. You can add IPAdapter nodes for style reference without unloading the base model. You can generate batches of 2-3 images simultaneously to compare variations.

For creators building professional ComfyUI workflows, this flexibility matters more than raw speed. You design a workflow once and run it repeatedly without memory-related modifications.

The Economics of 24GB Cards

RTX 3090 cards sell for around $800-1200 on the used market as of early 2025. RTX 4090 new retail sits around $1600-2000. Compare this to 48GB setups requiring either dual GPU configurations or professional cards starting at $4000.

The cost-per-VRAM ratio makes 24GB cards the most practical choice for serious hobbyists and small studios. You get professional capabilities without the enterprise price tag.

RTX 3090 vs RTX 4090 Performance Comparison for Flux 2

Both cards share 24GB VRAM capacity, but architectural differences create meaningful performance gaps. Understanding these differences helps you set realistic expectations and optimize appropriately for your specific hardware.

Architectural Differences That Matter

The RTX 3090 uses Ampere architecture with second-generation Tensor Cores that accelerate FP16 and INT8 operations. Memory bandwidth peaks at 936 GB/s using GDDR6X memory. Cuda core count reaches 10,496 with a boost clock around 1.7 GHz.

The RTX 4090 leverages Ada Lovelace architecture with fourth-generation Tensor Cores that add native BF16 and FP8 acceleration. Memory bandwidth jumps to 1,008 GB/s with faster GDDR6X. Cuda cores increase to 16,384 with boost clocks hitting 2.5 GHz. The architectural improvements translate to 50-60% faster generation times for identical workloads.

Real-World Flux 2 Benchmarks

Testing Flux 2 at 1024x1024 resolution using FP16 precision with 20 steps and Euler sampler produces consistent results. The RTX 3090 generates images in 12-15 seconds depending on prompt complexity and model caching. The RTX 4090 completes the same task in 8-11 seconds.

Switching to BF16 precision narrows the gap slightly. RTX 3090 takes 10-13 seconds while RTX 4090 completes generation in 7-9 seconds. The 4090's native BF16 acceleration provides more benefit than 3090's software emulation.

FP8 quantization widens the gap again. The RTX 3090 lacks hardware FP8 support and relies on software implementation with modest gains around 15-20% faster than FP16. The RTX 4090's hardware FP8 Tensor Cores deliver 40-50% speedup compared to FP16, making FP8 the optimal precision choice for 4090 users.

If you're considering the newer RTX 5090, our Flux 2 FP8 on RTX 5090 guide covers the latest Blackwell architecture's capabilities.

Memory Bandwidth Impact on Batch Generation

Single image generation relies heavily on compute performance, where the 4090's architectural advantages shine. Batch generation shifts the bottleneck toward memory bandwidth where the gap narrows.

Generating two 1024x1024 images simultaneously pushes VRAM usage to 22-23GB on both cards. The RTX 3090 completes the batch in 18-22 seconds. The RTX 4090 finishes in 14-17 seconds. The performance delta shrinks from 40% to 25% because both cards spend more time moving data than computing results.

This characteristic makes the RTX 3090 a surprisingly viable option for batch workflows where the absolute speed matters less than cost efficiency.

What Precision Settings Should You Use on 24GB VRAM?

Precision formats determine how the model stores and processes numerical values during generation. The choice between FP16, BF16, and FP8 affects quality, speed, and memory usage. With 24GB VRAM you have the luxury of choosing based on workflow priorities rather than memory constraints.

FP16 for Maximum Quality

FP16 (16-bit floating point) remains the gold standard for Flux 2 quality. Black Forest Labs trained the model primarily in FP16, making it the precision format that best preserves the intended output characteristics.

VRAM consumption sits around 18GB for 1024x1024 generation, leaving adequate headroom for most workflows. Generation speed represents the baseline against which other precisions are measured. Quality preservation is effectively perfect with no perceptible degradation compared to full FP32 precision.

Use FP16 when output quality matters most and you're not time-constrained. Commercial work, portfolio pieces, and client deliverables benefit from FP16's quality ceiling.

BF16 for Balanced Performance

BF16 (Brain Float 16) represents Google's alternative to FP16 with different tradeoffs. It maintains FP32's exponent range while reducing mantissa precision, which helps with numerical stability in certain operations.

For Flux 2 on RTX 4090, BF16 offers 15-20% speed improvement over FP16 with imperceptible quality differences in most cases. Memory usage drops slightly to 16-17GB. The RTX 4090's native BF16 support makes this an excellent default choice.

The RTX 3090 requires software emulation for BF16, reducing the speed advantage to 5-10% over FP16. Unless you're specifically testing BF16 for compatibility reasons, stick with FP16 on 3090 hardware.

FP8 for Maximum Speed

FP8 quantization compresses the model to 8-bit precision with significant speed gains. The RTX 4090's hardware FP8 acceleration delivers 40-50% faster generation compared to FP16. The RTX 3090's software implementation provides more modest 15-20% improvements.

Quality degradation exists but remains subtle for most prompts. Fine details like text rendering show slight degradation. Complex textures lose minor fidelity. Facial features maintain quality surprisingly well.

VRAM usage drops to 12-14GB, opening possibilities for larger batch sizes or additional models loaded simultaneously. If you're exploring this option, our complete guide to Flux 2 covers quantization strategies in detail.

Use FP8 when generating large volumes of images where speed matters more than absolute quality. Social media content, iteration workflows, and bulk generation benefit from FP8's throughput.

Precision Recommendations by Use Case

For client work and portfolio: FP16 on both RTX 3090 and 4090. Accept the slower speed for guaranteed quality.

For personal projects and experimentation: BF16 on RTX 4090, FP16 on RTX 3090. The speed-quality balance favors efficiency without meaningful compromises.

For high-volume generation: FP8 on RTX 4090, BF16 on RTX 3090. Maximize throughput when you need hundreds of images.

For workflow development: FP16 on both cards. Optimize your nodes and prompts against the quality ceiling before introducing precision as a variable.

Maximum Resolution Without Optimization Tricks

24GB VRAM unlocks resolution capabilities that smaller cards can't reach without tiling, VAE offloading, or other workarounds that slow generation and complicate workflows. Understanding your resolution ceiling helps you design workflows that maximize quality without crashes.

Resolution Limits by Precision

Running Flux 2 in FP16 precision handles 1024x1024 comfortably with 18GB VRAM usage. Pushing to 1280x1280 increases consumption to 21-22GB, approaching your memory ceiling. 1536x1536 exceeds 24GB and triggers out-of-memory errors without optimization.

BF16 precision adds modest resolution headroom. 1024x1024 uses 16-17GB. 1280x1280 consumes 19-20GB. 1536x1536 pushes to 23-24GB and may work depending on system memory overhead and background processes.

FP8 quantization opens high-resolution possibilities. 1024x1024 uses just 12-14GB. 1280x1280 sits at 16-18GB. 1536x1536 reaches 20-22GB, fitting comfortably within your memory budget.

Aspect Ratio Considerations

Square resolutions represent worst-case memory scenarios because they maximize pixel count. Non-square aspect ratios that maintain similar total pixel counts use slightly less memory due to how the model processes dimensions.

A 1024x1280 portrait (1.31 MP) uses marginally less VRAM than 1152x1152 (1.33 MP) despite similar pixel counts. The difference rarely exceeds 5-10% but can prevent out-of-memory errors when operating near your ceiling.

When to Use Upscaling Instead

Generating at lower resolution then upscaling often produces better results than pushing to your maximum native resolution. A 1024x1024 image upscaled to 2048x2048 using a dedicated upscaling model preserves details better than generating directly at 1536x1536 near your memory limit.

The two-stage workflow costs more generation time but uses memory more efficiently. Generate your base image at comfortable memory levels with optimal sampler settings. Upscale in a second pass using models like RealESRGAN or Ultimate SD Upscale.

Platform like Apatero.com handle this two-stage process automatically without requiring manual workflow configuration or memory management.

How to Configure ComfyUI for Optimal 24GB Performance

ComfyUI's flexibility becomes a liability without proper configuration. The default settings prioritize compatibility over performance. Optimizing for 24GB VRAM requires specific launch arguments and node configurations that match your hardware capabilities.

Essential Launch Arguments for 24GB Cards

Launch ComfyUI with these flags to optimize memory management for RTX 3090 and RTX 4090 hardware.

For RTX 3090 users running FP16 precision, use the standard CUDA memory format without special flags. Add lowvram flag only if running multiple models simultaneously. The preview-method auto flag enables adaptive preview generation.

For RTX 4090 users, add cuda-malloc to leverage improved memory allocation in recent CUDA versions. The force-fp16 flag ensures models load in FP16 by default. Add bf16-unet if testing BF16 precision.

For both cards, disable xformers if using PyTorch 2.0 or newer as the built-in memory-efficient attention provides better performance. Enable gpu-only to prevent CPU memory fallback that slows generation.

Node Configuration Best Practices

The KSampler node contains settings that dramatically impact memory usage and generation quality. Set your steps between 20-28 for FP16 precision. Lower to 15-20 for BF16 or FP8 where the precision reduction benefits from fewer inference steps.

Configure CFG scale between 5-8 for most prompts. Higher values improve prompt adherence but increase memory usage and generation time. Euler and DPM++ 2M samplers provide the best speed-quality balance for Flux 2.

The VAE Decode node should use tiled decoding only when generating above 1280x1280. For standard resolutions the tiled decoding overhead costs more performance than it saves in memory.

Memory Management Strategies

ComfyUI loads models into VRAM and keeps them cached between generations. This speeds up subsequent runs but consumes memory that could enable larger batch sizes or higher resolutions.

Clear your model cache between different workflows by restarting ComfyUI or using the Clear Cache button in the UI. This seems wasteful but prevents memory fragmentation that causes mysterious out-of-memory errors after hours of generation.

Monitor your VRAM usage using nvidia-smi in a terminal window or GPU-Z on Windows. If you consistently see 23GB+ usage, you're operating too close to your ceiling and should reduce resolution or batch size.

For users new to ComfyUI optimization, our essential custom nodes guide covers the tools that help manage memory and performance.

What Batch Sizes Work with 24GB VRAM?

Batch generation creates multiple images in a single pass, dramatically improving throughput for workflows requiring variations or volume. Understanding your batch size limits prevents wasted time from out-of-memory crashes.

Single Image Baseline

Before experimenting with batches, establish your single-image baseline. Generate one 1024x1024 image using your preferred precision and note the VRAM consumption. FP16 typically uses 18GB, BF16 around 16GB, FP8 near 13GB.

Subtract this from your 24GB total to determine available memory for additional batch images. Remember to reserve 1-2GB for system overhead and cache, leaving approximately 4-5GB for FP16, 6-7GB for BF16, and 9-10GB for FP8 batches.

Realistic Batch Limits

With FP16 precision you can generate batches of 2 images at 1024x1024 consuming 22-23GB total VRAM. Attempting 3 images triggers out-of-memory errors. Reducing resolution to 896x896 enables 3-image batches while maintaining reasonable quality.

BF16 precision pushes batch limits slightly. Two 1024x1024 images use 19-20GB. Three images reach 23-24GB and may work but risk crashes. Two 1152x1152 images provide higher resolution at safer memory levels.

FP8 quantization excels at batch generation. Four 1024x1024 images consume 20-22GB comfortably. Five images push boundaries at 23-24GB. Two 1280x1280 images provide higher resolution alternatives to multiple 1024 batches.

Sequential vs Parallel Batching

ComfyUI supports two batch approaches. Parallel batching generates all images simultaneously using maximum VRAM. Sequential batching generates one image at a time with the same seed and prompt variations.

Parallel batching provides faster total generation time but requires enough VRAM for all images. Sequential batching runs slower but works within any memory constraint. For 24GB cards, parallel batching is viable for 2-3 images. Beyond that, sequential batching prevents crashes without meaningful time penalties.

Batch Generation vs Multiple Single Generations

Generating 4 images as a batch of 4 takes approximately 35-40 seconds on RTX 4090 in FP8. Generating the same 4 images individually takes 32-44 seconds (4 images at 8-11 seconds each). The batch advantage disappears when you factor in the higher memory requirement and crash risk.

Use batch generation when you specifically need variations with identical seeds or when comparing sampler settings. For general volume production, sequential single generations offer better reliability without meaningful speed loss.

When 24GB Still Needs Optimization

24GB VRAM handles most Flux 2 workflows comfortably, but certain advanced use cases push beyond this threshold. Knowing when you've hit a legitimate memory wall versus a configuration issue saves hours of troubleshooting.

Multi-Reference Workflows Hit Memory Walls

Flux 2's multi-reference capability allows up to 10 reference images for character or style consistency. Each reference image loaded through IPAdapter or similar nodes adds 1-3GB VRAM consumption depending on resolution and precision.

Loading 3-4 reference images alongside the base Flux 2 model in FP16 quickly approaches your 24GB ceiling. Generation at 1024x1024 with 4 reference images consumes 23-24GB. Add any ControlNet and you crash.

Solutions include reducing base model precision to BF16 or FP8 to free memory for reference images. Alternatively, reduce reference image resolution to 512x512 before loading. The model extracts style and character information equally well from lower resolution references.

ControlNet Stacking Requires Trade-offs

Single ControlNet models like Canny edge detection or depth maps add 3-4GB VRAM when loaded alongside Flux 2. This fits within 24GB for single ControlNet workflows. Stacking multiple ControlNets for precise pose and composition control exceeds your memory budget.

Two ControlNets plus Flux 2 in FP16 consume 24-25GB, causing crashes. Three ControlNets reach 27-28GB, well beyond your capacity. Reduce to FP8 precision or use ControlNet union models that combine multiple control types in a single 3-4GB footprint.

LoRA Stacking and Memory Considerations

Individual LoRA models add negligible memory overhead, typically 100-300MB each. However, the activation of multiple LoRAs during generation increases compute requirements that translate to VRAM pressure.

Loading 5-6 LoRAs onto Flux 2 rarely causes out-of-memory errors from size alone. Generation with all LoRAs active at high strength values increases effective VRAM consumption by 2-3GB through expanded computation graph.

If experiencing crashes with multiple LoRAs, reduce LoRA strength values from 1.0 to 0.6-0.8. This decreases their influence slightly but significantly reduces memory pressure during generation.

Advanced Workflows Possible with 24GB VRAM

Moving beyond basic text-to-image generation, 24GB VRAM enables sophisticated workflows that smaller cards cannot handle. These advanced techniques separate hobbyist output from professional results.

Image-to-Image with High Denoising

Standard image-to-image workflows use 30-50% denoising strength to modify existing images. This adds minimal memory overhead. High denoising at 70-90% essentially regenerates the image from scratch, doubling effective VRAM requirements.

With 24GB you can run high denoising image-to-image at 1024x1024 in FP16 precision. Start with a rough composition in a traditional image editor, feed it to Flux 2 with 80% denoising, and get photorealistic results while maintaining your composition.

This workflow requires 20-22GB VRAM and produces results impossible on lower memory cards without resolution reduction.

Inpainting at Native Resolution

Inpainting generates new content within masked regions of existing images. Naive implementations generate a full new image then composite the masked area. Proper inpainting uses the masked region context, requiring both the original image and generation model in memory simultaneously.

ComfyUI inpainting workflows with Flux 2 consume 20-23GB VRAM for 1024x1024 images in FP16. The dual memory load makes this barely viable on 24GB cards. Switching to BF16 drops consumption to 17-19GB, enabling comfortable inpainting workflows.

Animation Frame Generation

Creating AI-generated animations requires maintaining consistency across frames. The typical workflow generates a key frame, then uses it as a reference for subsequent frames with slight prompt variations.

With 24GB VRAM you can load Flux 2, a reference frame through IPAdapter, and a ControlNet for pose consistency simultaneously. Generate 512x768 frames in FP16 or 768x1024 frames in BF16 while maintaining character and style consistency.

This workflow runs uncomfortably close to memory limits but works reliably with proper optimization. Users generating animation sequences might benefit from cloud platforms like Apatero.com that handle memory management automatically across longer generation sessions.

Should You Use Cloud Solutions Like Apatero Instead?

Owning a 24GB VRAM card represents a significant investment in local generation capability. Cloud solutions offer different tradeoffs that make sense for certain workflows and usage patterns.

Cost Analysis for Different Usage Levels

An RTX 3090 costs $800-1200 used, providing unlimited local generation after initial purchase. Electricity costs approximately $0.10-0.20 per hour of generation at 350W power draw. Monthly costs stay near zero for typical hobbyist usage.

Cloud platforms like Apatero.com charge per generation or subscription models. Typical pricing runs $10-30 monthly for casual use, $50-150 for regular use. Heavy users generating hundreds of images daily exceed $200-300 monthly costs.

The breakeven point sits around 6-12 months depending on usage. Generate casually and cloud stays cheaper. Generate daily and local hardware pays for itself within a year.

When Cloud Makes More Sense

Beginners exploring AI generation benefit from cloud platforms that eliminate installation, configuration, and troubleshooting. Apatero.com provides working Flux 2 workflows immediately without ComfyUI learning curve.

Users with laptop-only setups or incompatible hardware access cutting-edge models through cloud platforms without hardware investment. The flexibility to scale compute for specific projects without permanent hardware purchases suits freelancers and contractors.

Team workflows benefit from cloud infrastructure that provides consistent environments across multiple users without individual hardware configuration.

When Local 24GB Hardware Wins

Privacy-sensitive work requiring local generation without cloud upload makes local hardware essential. Creative professionals retaining intellectual property control need local infrastructure.

High-volume generation where costs scale linearly with cloud usage quickly exceeds local hardware investment. Generating 50-100 images daily makes local hardware obviously cheaper within months.

Workflow experimentation and development requires rapid iteration that cloud per-generation pricing makes expensive. Testing different samplers, steps, and precision settings cost nothing locally but add up quickly in cloud environments.

The Hybrid Approach

Many professionals maintain both local hardware for experimentation and cloud resources for client work. Develop and test workflows locally on RTX 3090/4090. Deploy production generation to cloud infrastructure with better monitoring and scaling.

This hybrid approach provides cost-effective development with production reliability. You avoid paying cloud costs during workflow design while leveraging cloud benefits for final output.

Future-Proofing Your 24GB Investment

AI models evolve rapidly, raising questions about whether today's hardware investment remains relevant tomorrow. Understanding the trajectory helps make informed purchasing decisions.

Model Size Trends

Flux 1 used 12 billion parameters. Flux 2 jumped to 32 billion parameters. This trend toward larger models concerns users investing in fixed hardware capacity.

However, quantization techniques evolve alongside model sizes. FP8 quantization reduces Flux 2's memory footprint by 50% compared to FP16 with minimal quality loss. Future quantization methods like INT4 promise further reductions.

24GB VRAM will likely handle next-generation models through improved quantization even if base model sizes continue growing. The bigger concern is compute performance, where older cards like RTX 3090 will show age against newer architectures.

Architectural Advantages of Newer Cards

RTX 4090's fourth-generation Tensor Cores provide native BF16 and FP8 acceleration. RTX 5090's fifth-generation Tensor Cores add FP4 support and improved INT8 throughput.

These architectural improvements ensure newer cards maintain relevance longer through precision advantages that older hardware cannot match. An RTX 3090 will run future models but increasingly rely on higher precision with slower generation.

For users choosing between RTX 3090 and RTX 4090 today, the 4090's architectural advantages justify the price premium for anyone planning 3+ year hardware lifecycles.

Alternative Uses for 24GB Cards

AI image generation represents just one use case for high-VRAM hardware. Language model inference, video generation, and 3D rendering all benefit from 24GB memory capacity.

If image generation workflows evolve beyond your hardware capability, the card retains value for other AI workloads. This versatility provides insurance against rapid obsolescence in any single domain.

Frequently Asked Questions

Can I run Flux 2 on RTX 3090 with only 20GB usable VRAM?

Some RTX 3090 cards report only 20GB usable VRAM due to memory allocation overhead. You can still run Flux 2 at 1024x1024 in FP16 but with tighter memory margins. Reduce batch sizes to 1, avoid loading multiple ControlNets simultaneously, and close background applications using GPU memory. Consider BF16 or FP8 precision for additional headroom.

Does RTX 3090 Ti perform differently than RTX 3090 for Flux 2?

RTX 3090 Ti uses the same Ampere architecture as RTX 3090 with slightly higher clock speeds and memory bandwidth. Generation speed improves by 5-10% but memory capacity remains 24GB. For Flux 2 specifically the performance difference is marginal. Buy whichever card offers better pricing.

Should I use FP8 on RTX 3090 or stick with FP16?

RTX 3090 lacks hardware FP8 acceleration, making FP8 quantization less attractive. You gain 15-20% speed improvement and significant VRAM savings but rely on software emulation. Use FP8 on RTX 3090 only when memory constraints require it or when generating high volumes where modest speed gains compound. For quality-focused work stick with FP16.

What resolution can I generate with Flux 2 on 24GB in FP8?

FP8 quantization enables 1536x1536 generation within 24GB VRAM, consuming approximately 20-22GB. Some users report successful 1664x1664 generation with minimal background processes. Beyond this resolution you need tiling, VAE offloading, or optimization techniques regardless of FP8's memory benefits.

How many LoRAs can I load with Flux 2 on RTX 4090?

LoRA models themselves add minimal memory overhead. You can load 10-15 LoRAs alongside Flux 2 in FP16 precision on RTX 4090. The limitation comes from generation complexity when multiple LoRAs activate simultaneously at high strength. Keep combined LoRA strengths below 3.0 to avoid memory pressure during generation.

Can I generate 2K or 4K images directly with 24GB VRAM?

Direct 2K generation at 2048x2048 exceeds 24GB VRAM in standard precision formats. Use FP8 quantization plus tiled VAE decoding to generate up to 2048x2048. For 4K resolution at 4096x4096, even FP8 with aggressive optimization cannot fit within 24GB. Use upscaling workflows instead, generating at 1024x1024 then upscaling to 4K in a separate pass.

Is RTX 4090 worth the extra cost over RTX 3090 for Flux 2?

RTX 4090 generates 40-50% faster than RTX 3090 for Flux 2 workloads, particularly with BF16 or FP8 precision. For users generating hundreds of images monthly, this speed advantage pays for the price premium through time savings. Casual users generating occasional images should buy the cheaper RTX 3090. Professional users should invest in RTX 4090 for workflow efficiency.

Does Flux 2 work better on 24GB than 48GB cards?

48GB VRAM enables higher resolutions, larger batches, and more complex workflows than 24GB but provides no quality advantage for standard generation. If your workflow stays within 1280x1280 resolution with moderate ControlNet use, 24GB provides identical quality to 48GB. The extra cost of 48GB cards rarely justifies the benefit unless running extreme multi-reference or ControlNet stacking workflows.

What happens if I exceed 24GB during generation?

Exceeding VRAM capacity triggers out-of-memory errors that crash generation. ComfyUI attempts to offload models to system RAM but this dramatically slows generation from minutes to hours. Monitor VRAM usage and stay below 23GB consistently. Build workflows with memory headroom rather than pushing absolute limits.

Can I use two RTX 3090 cards for 48GB total VRAM?

Flux 2 generation runs on a single GPU and cannot split across multiple cards. Two RTX 3090 cards provide 24GB VRAM each, not 48GB combined for single generation tasks. You can run separate generation workflows simultaneously on each card but this requires workflow duplication and manual management. Single-GPU solutions work better for AI image generation.

Conclusion

24GB VRAM represents the practical sweet spot for Flux 2 generation where memory stops being your primary constraint and workflow design takes priority. RTX 3090 offers cost-effective entry at $800-1200 used with respectable 12-15 second generation times. RTX 4090 provides future-proofed performance at $1600-2000 with 40-50% faster generation and superior precision support.

Configure your setup with FP16 for maximum quality, BF16 for balanced performance on RTX 4090, or FP8 when speed and batch generation matter most. Stay within 1280x1280 resolution without optimization tricks, generate 2-3 image batches comfortably, and build sophisticated workflows incorporating ControlNets, LoRAs, and multi-reference generation.

For users who want these capabilities without hardware investment or technical configuration, platforms like Apatero.com provide optimized Flux 2 workflows with automatic memory management and precision selection. The cloud approach trades ongoing costs for zero setup friction and consistent performance.

Whether you choose local hardware or cloud infrastructure depends on your generation volume, privacy requirements, and technical comfort level. Both paths deliver professional results with 24GB-class capabilities. The key is matching your tooling to your workflow rather than fighting memory constraints that smaller configurations impose.