NVIDIA 5070 Ti Users Report Speed Gains in Stable Diffusion Image Generation — Expert Analysis

Recent user reports from the r/StableDiffusion subreddit reveal that owners of NVIDIA’s newly released RTX 5070 Ti are experiencing notable gains in image generation speed when using Forge Neo, a popular frontend for Stable Diffusion. With 16GB of VRAM and 32GB of system RAM, users are achieving generation times as low as 7.5 seconds for repeated prompts — a dramatic improvement over the initial 28-second runtime. However, performance remains inconsistent, especially when enabling high-resolution upscaling or generating multiple images in batch.

One user, operating under the username /u/okayaux6d, noted that generating a single 1152x896 image at 25 steps took 54.6 seconds for a batch of four images, and over two and a half minutes when applying a 1.5x high-resolution enhancement. The system, while powerful, showed memory usage peaking at nearly 10GB of VRAM and 11.3GB of system RAM — suggesting potential inefficiencies in memory allocation or pipeline management. The user also received a system hint: "your device supports --cuda-malloc for potential speed improvements," indicating that critical optimizations remain unactivated.

According to technical experts in AI rendering communities, enabling --cuda-malloc can significantly reduce memory fragmentation and accelerate tensor operations on modern NVIDIA GPUs. This flag instructs PyTorch to use CUDA’s memory allocator instead of the default one, which is often slower and less efficient for deep learning workloads. Users unfamiliar with these settings may inadvertently limit their hardware’s potential. Additionally, the message "CUDA Using Stream: False" suggests that asynchronous computation streams — which allow overlapping memory transfers and kernel execution — are not being utilized, further capping throughput.

Another critical factor is the attention mechanism. The system logs confirm that PyTorch’s native cross-attention and VAE attention are being used, which are generally slower than optimized alternatives like xFormers or FlashAttention. While these are more stable across hardware, they sacrifice speed. For users with RTX 5000-series cards, switching to FlashAttention-2 (if compatible with their Forge Neo build) could yield 20–40% faster inference, according to benchmarks from AI optimization forums.

Notably, the user reported playing light games during generation — a practice that can introduce GPU context switching and memory contention. Even minimal gaming activity can fragment VRAM allocation and delay CUDA kernel launches, especially when the GPU is already under heavy load from diffusion models. Experts recommend dedicating the GPU exclusively to AI tasks during intensive generation sessions.

While the referenced sources — Forge Forums and files.minecraftforge.net — relate to Minecraft modding infrastructure and are unrelated to AI image generation, they highlight a common confusion among users: the term "Forge" in "Forge Neo" refers to a Stable Diffusion UI framework, not the Minecraft mod loader. This naming overlap has led to misdirected searches and misinformation. The Stable Diffusion Forge Neo project is an independent open-source initiative built on Automatic1111’s WebUI, optimized for speed and stability on modern hardware.

For users seeking to maximize performance on RTX 5070 Ti systems, experts recommend: enabling --cuda-malloc, installing xFormers or FlashAttention, disabling background GPU tasks, setting VAE dtype to float16 instead of bfloat16 (if stability permits), and using a dedicated CUDA stream. With these adjustments, generation times for high-res images could drop below 90 seconds — approaching the theoretical limits of the hardware.

As generative AI becomes increasingly integrated into creative workflows, optimizing these tools is no longer a niche concern — it’s a necessity for professionals. The RTX 5070 Ti’s raw power is undeniable, but unlocking its full potential requires more than hardware — it demands technical understanding and precise configuration.