Best GPU for Stable Diffusion & FLUX.1 in 2026 — Benchmarked
Image Generation GPU Benchmarks (2026)
| GPU | VRAM | Bandwidth | SDXL (1024×1024) | FLUX.1-dev (1024×1024) | Best For |
|---|---|---|---|---|---|
| RTX 5070 Windforce | 12 GB GDDR7 | 672 GB/s | 2–3 sec | 4–6 sec | Speed + CUDA ecosystem |
| RTX 5070 SFF | 12 GB GDDR7 | 672 GB/s | 2–3 sec | 4–6 sec | Compact builds |
| RX 9060 XT 16G | 16 GB GDDR6 | 288 GB/s | 4–5 sec | 8–12 sec | Large batches + VRAM |
| Mac Mini M4 Pro | 24–64 GB Unified | 273 GB/s | 10–15 sec | 15–25 sec | Silent + LLM combo |
| RTX 4070 Super (prev gen) | 12 GB GDDR6X | 504 GB/s | 4–6 sec | 8–12 sec | Used / budget |
What Makes a GPU Good for Image Generation?
Image generation performance is driven almost entirely by memory bandwidth — not CUDA cores, not clock speed. During each diffusion step, the model reads and writes the entire latent tensor through VRAM. A GPU with 672 GB/s bandwidth (RTX 5070) will complete that operation 2.3× faster than a 288 GB/s card (RX 9060 XT), which is why the RTX 5070 generates SDXL images in 2–3 seconds while the RX 9060 XT takes 4–5 seconds despite having more VRAM.
VRAM capacity determines which models you can load and how large a batch you can run. SDXL at fp16 requires approximately 6–8 GB. FLUX.1-dev requires 10–12 GB. Adding ControlNet or IP-Adapter adds 2–4 GB. With 12 GB (RTX 5070), you can run FLUX.1 with one ControlNet. With 16 GB (RX 9060 XT), you can run FLUX.1 with two ControlNet models or generate in larger batches.
RTX 5070 vs RX 9060 XT for Stable Diffusion
The RTX 5070 wins on raw speed. GDDR7 at 672 GB/s means each diffusion step completes faster, translating to 2–3 second SDXL images versus 4–5 seconds on the RX 9060 XT. For solo image generation at standard resolutions, this difference is significant — you generate 2× as many images per hour.
The RX 9060 XT wins on capacity. 16 GB VRAM lets you run FLUX.1 with multiple ControlNet models simultaneously, load SDXL with both a base and refiner model, and generate larger batch sizes without VRAM errors. On Linux with ROCm, performance is competitive with CUDA for image generation workloads.
FLUX.1 VRAM Requirements
| Model | VRAM (fp16) | VRAM (fp8/quantized) | Notes |
|---|---|---|---|
| FLUX.1-schnell | ~12 GB | ~8 GB | Fastest, 4-step generation |
| FLUX.1-dev | ~12 GB | ~8 GB | Higher quality, 20-step |
| FLUX.1-dev + ControlNet | ~14–16 GB | ~10–12 GB | Structural control |
| FLUX.1-dev + IP-Adapter | ~14–16 GB | ~10–12 GB | Style/face reference |
| SDXL base + refiner | ~10 GB | ~7 GB | Two-pass quality boost |
Can You Run Stable Diffusion on a Mac?
Yes, but slowly. The Mac Mini M4 Pro runs SDXL at 10–15 seconds per image and FLUX.1-dev at 15–25 seconds — functional for occasional use but 3–5× slower than the RTX 5070. ComfyUI has native Apple Silicon support via Metal. If image generation is your primary use case, a discrete GPU PC is the better choice. If you want a quiet, all-in-one machine that handles both LLMs and occasional image generation, the Mac Mini M4 Pro is a reasonable compromise.
ComfyUI vs AUTOMATIC1111 — Which to Use?
ComfyUI is the current standard for power users and is updated more frequently. It supports FLUX.1 natively, has a node-based workflow for complex pipelines, and handles newer model architectures faster. AUTOMATIC1111 (A1111) has a more traditional UI and a larger library of extensions, but FLUX support came later and is less polished. For new setups in 2026, start with ComfyUI.
Our Recommendation
For most users: the RTX 5070 Windforce is the best GPU for Stable Diffusion and FLUX in 2026. It generates images fast, supports all current models at 12 GB, and the CUDA ecosystem means every ComfyUI node and A1111 extension works out of the box. If you frequently run FLUX with ControlNet or generate large batches, step up to the RX 9060 XT 16G for the VRAM headroom — just be prepared to use Linux for the best ROCm experience.
Frequently Asked Questions
Q1What is the minimum GPU for FLUX.1-dev in 2026?
FLUX.1-dev requires approximately 12 GB VRAM at fp16 precision, or 8 GB with fp8 quantization via ComfyUI. The RTX 5070 (12 GB) runs it at full quality. The RX 9060 XT (16 GB) runs it with room for ControlNet. GPUs with 8 GB VRAM can run FLUX.1 with fp8 quantization, but quality is slightly reduced.
Q2How many seconds per image does the RTX 5070 generate at 1024×1024?
SDXL at 1024×1024 with 20 steps DPM++ 2M Karras: approximately 2–3 seconds. FLUX.1-dev at 1024×1024 with 20 steps: approximately 4–6 seconds. FLUX.1-schnell (4-step fast mode) at 1024×1024: approximately 1–2 seconds. These times are for single image generation without batching.
Q3Does the RX 9060 XT work with ComfyUI?
Yes, on Linux with ROCm. ComfyUI supports AMD GPUs via ROCm on Linux — install ROCm, then run ComfyUI with the --use-pytorch-cross-attention flag. On Windows, DirectML support works but is slower and occasionally incompatible with newer custom nodes. For professional Stable Diffusion workflows on AMD, Linux is strongly recommended.
Q4Is 8 GB VRAM enough for Stable Diffusion in 2026?
For SD 1.5 and basic SDXL: yes. For FLUX.1 at full quality: no — you need fp8 quantization which slightly reduces quality. For FLUX with ControlNet: 8 GB is too tight. If you're buying new hardware in 2026, 12 GB is the minimum recommended for full FLUX.1 support, and 16 GB gives comfortable headroom for complex workflows.
Q5Can I run both an LLM and Stable Diffusion at the same time on 12 GB VRAM?
Not comfortably. A 7B LLM at fp16 requires ~14 GB VRAM — more than the RTX 5070's 12 GB. At Q4 quantization, a 7B model uses ~5 GB, leaving ~7 GB for Stable Diffusion — barely enough for SD 1.5 but not SDXL or FLUX. In practice, switch between applications rather than running both simultaneously. For this use case, a Mac Mini M4 Pro with 24+ GB unified memory handles both workloads natively.
Q6What is the best GPU for Stable Diffusion under $600?
The RTX 5070 Windforce at approximately $549 is the best GPU for Stable Diffusion under $600 in 2026. It offers 672 GB/s GDDR7 bandwidth, 12 GB VRAM, full FLUX.1 support, and CUDA compatibility with every ComfyUI node. The RX 9060 XT 16G is a close alternative with more VRAM but slower image generation.
Q7How does Stable Diffusion performance scale with VRAM?
More VRAM primarily affects which models you can load (not speed per se). Speed is driven by bandwidth. With 8 GB: SD 1.5 and SDXL with attention slicing. With 12 GB: full SDXL, FLUX.1, one ControlNet. With 16 GB: FLUX.1 with multiple ControlNets, larger batches, SDXL base + refiner simultaneously. Going from 8 GB to 12 GB is the most impactful upgrade; 12 GB to 16 GB matters if you use ControlNet heavily.
Q8Does Apple Silicon (Mac) support Stable Diffusion natively?
Yes. ComfyUI and AUTOMATIC1111 both support Apple Silicon via Metal acceleration. SD 1.5 runs at 6–10 it/s on the Mac Mini M4, SDXL at 10–20 seconds per image on the M4 Pro. FLUX.1 is supported but takes 15–30 seconds per image. All models that run on CUDA or ROCm also run on Metal — there are no major compatibility gaps in 2026. Performance is the main limitation, not compatibility.