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It’s been a long wait for NVIDIA’s newest GPU lineup. The NVIDIA GeForce RTX™ 50-series graphics cards, announced at CES in Las Vegas on January 6, brought a rush of information, comments, impressions, and perspectives that left us sorting through the noise to better grasp what these new GPUs offer. The list of features and technical specifications is a lot of information to take in at first, but after letting the dust settle, the picture becomes clearer as to what these GPUs have to offer content creators.

Source | Slide from NVIDIA Keynote at CES 2025
NVIDIA has announced the following graphics cards, which are built on the new Blackwell Architecture:
- NVIDIA GeForce RTX™ 5090
- NVIDIA GeForce RTX™ 5080
- NVIDIA GeForce RTX™ 5070 Ti
- NVIDIA GeForce RTX™ 5070
The RTX 5090 and RTX 5080 have a release date of January 30, with the RTX 5070 Ti and RTX 5070 following in February. NVIDIA also announced a new line of RTX 50 series laptops available in March and April. However, for this blog post, we want to focus on the new graphics cards and some of the features that content creators, in particular, should be aware of.

Source | NVIDIA
With the RTX 50-series, NVIDIA integrates AI processing into the GPU’s architecture to process data more efficiently, taking the processing load off of the GPU by utilizing features such as neural rendering to increase performance. However, relying on AI to manage portions of rendering and processing presents some uncertainty. Limited information is available about how this ‘hybrid’ approach to processing and rendering affects visual fidelity and quality and whether this will be used much outside of gaming.
It will be interesting to see the real-world impact for users and how these improvements stack up against previous GPU models once the RTX 50-series GPUs are available and tested in the field.

Source | NVIDIA Keynote Recap CES 2025
For content creators, specifically those in the post-production, virtual production, and VFX pipelines, NVIDIA GPUs have been a key component in systems for the past couple of years. They offer processing power that outperforms other GPUs, particularly in the top tier of the graphics card market. At Puget Systems, NVIDIA GPUs make up the majority within our systems, so the launch of this new product will influence future recommended systems for content creators, their workflows, and the applications they depend on.

Source | NVIDIA
Content creators should note the technologies featured in the RTX 50-series listed in the spec sheet above. However, understanding which features specifically benefit content creators requires further analysis. Below, we have curated a list of these features and what they mean for content creation.
It’s worth noting that technical specifications are intentionally excluded from this blog. However, we have published two articles examining the performance of the RTX 5090 and RTX 5080 in content creation applications. For more details, refer to our GeForce RTX 5090 content creation review and GeForce RTX 5080 content creation review articles.
Additionally, some software applications have experienced delays in adopting hardware support for certain RTX 50-series features. As a result, performance metrics may not yet fully reflect some of the performance metrics NVIDIA has claimed in their release. We have documented known issues in a separate blog and will provide updates as new software patches become publicly available. For information on when specific applications will receive support for RTX 50-series GPUs, visit NVIDIA’s support page for a list of software applications and their expected timelines for updates.
NVENC and NVDEC 4:2:2 Chroma Subsampling Support
NVIDIA Encoder (NVENC) and NVIDIA Decoder (NVDEC) are hardware-based components embedded in the GPU designed to offload the encoding/decoding process of modern codecs from the GPU, freeing up space for the GPU and CPU to process other tasks. These codecs include H264 (AVC), H265 (HEVC), AV1, VP8, VP9, VC-1, MPEG-1, and MPEG-2, supporting bit depths of 8, 10, and 12, along with chroma subsampling formats like 4:2:0, 4:2:2, and 4:4:4. A full list of supported codecs and formats can be found on NVIDIA’s GPU Support Matrix.
This announcement about hardware decoding and encoding support for the 4:2:2 chroma subsampling format may have flown under the radar compared to the many RTX 50-series announcements, but this new feature provides content creators access to a wide range of hardware-accelerated decoding/encoding support for modern codecs, bit depths (8,10,12), and chroma subsampling formats (4:2:0, 4:2:2, 4:4:4).
According to NVIDIA SDK Developers, the Blackwell Architecture supports 4:2:2 decoding and encoding in H264, H265, and Multi-view HEVC (for 3D and AR), as well as 4:2:2i and 4:2:0i H264. However, this is at the hardware level, and different ISVs (Adobe, Blackmagic, etc.) need to add support on the software side to integrate hardware-accelerated decoding and encoding support from NVIDIA’s GeForce RTX 50-series GPUs. Nonetheless, this new feature offers content creators in video production and post-production workflows the ability to work within the 4:2:2 color space without making compromises based on processing limitations.

Source | NVIDIA Studio Youtube Channel
The 10-bit 4:2:2 format is widely used in professional cameras and remains a favorite among production and post-production teams. The format’s balance of image quality, color accuracy, and manageable file sizes makes it well-suited for individuals, small teams, agencies, and other creatives seeking flexibility in their footage to produce cinematic-quality content.
However, these cameras tend to record their media in an All-Intra or LongGOP (H264) codec in the 4:2:2 format. The problem is that hardware decoding/encoding support for H264 4:2:2 media has not been available from any hardware manufacturer (until now), leaving a void at the software level, where ISVs lacked opportunities to incorporate hardware decoding/encoding support for H264 4:2:2 into their applications.
The only technology that has come close to supporting 4:2:2 chroma subsampling at both the hardware and software level is Intel’s Quick Sync technology, which is supported in a H265 (HEVC) codec. Now, with support for 4:2:2 chroma subsampling, NVIDIA’s RTX 50-series GPUs allow ISVs to incorporate support for H264 and H265 4:2:2 decoding/encoding into their applications, which should give content creators an additional option other than Intel CPUs for hardware-accelerated encoding/decoding using 4:2:2 media.
Currently, Premiere Pro does not currently provide support at the software level for hardware-accelerated decoding in the H264 4:2:2 format. We have tested in Premiere Pro Beta v25.2, Build 96, and have not yet seen adoption but are expecting Adobe to add support in February. However, DaVinci Resolve does support 4:2:2 chroma subsampling in H.264 and HEVC, but this feature is included in a version that is not yet publicly available. Look for the latest update from Blackmagic, as version 19.1.3 does not currently support 4:2:2.
Decoder and Encoder Upgrades
Another feature that NVIDIA introduced is their next generation of encoders and decoders. NVENC on the RTX 50-series GPUs encodes with the Gen 9 encoder and NVDEC decodes with the Gen 6 decoder. Additionally, NVIDIA increased the total number of encoders and decoders available on the RTX 50-series GPUs to complement the generational updates of NVENC and NVDEC.
GeForce RTX™ Graphics Card | Number of Encoders | Number of Decoders |
RTX 5090 | 3x Gen 9 | 2x Gen 6 |
RTX 5080 | 2x Gen 9 | 2x Gen 6 |
RTX 5070Ti | 2x Gen 9 | 1x Gen 6 |
RTX 5070 | 1x Gen 9 | 1x Gen 6 |
RTX 4090 | 2x Gen 8 | 1x Gen 5 |
RTX 4080 Super | 2x Gen 8 | 1x Gen 5 |
RTX 4070Ti Super | 2x Gen 8 | 1x Gen 5 |
RTX 4070 Super | 1x Gen 8 | 1x Gen 5 |
Source | NVIDIA
For the previous generation RTX 40-series GPUs, the entire product stack featured at least one Gen 5 decoder and up to two Gen 8 encoders. With the new RTX 50-series, both the RTX 5090 and RTX 5080 include two Gen 6 decoders, and the RTX 5090 stands out by offering three Gen 6 encoders, while the RTX 5080 includes two Gen 9 encoders. This is a notable improvement for content creators, as NVIDIA addresses a long-standing bottleneck that stems from decoding media, where performance and latency came from working with specific media formats.
NVIDIA addresses the playback efficiency of specific codecs by providing two Gen 6 decoders within the RTX 5090 and RTX 5080. Modern media files are complex to decode, and two decoders provide content creators with the capability to process multiple files, up to 8 4K timelines at 60fps. Processing eight media files in a 10-bit 4:2:2 format historically would have been hard to process in real-time (without latency). With two decoders, NVIDIA is ensuring content creators that their hardware can scale in processing as their projects shift in complexity.
By providing up to three Gen 9 encoders, content creators have an added capability to export codecs, such as HEVC and AV1, at expedited rates. These highly compressed (but very efficient) codecs generate smaller file sizes than H264 but maintain a high-quality image that minimizes pixelation, artifacts, and banding. Additionally, multiple encoders provide users the capability to export larger video files, such as 8K at 60fps+, within reasonable timings. For those working under tight deadlines or dealing with longer-duration timelines, this means faster exports and more time to focus on creative tasks. The time spent waiting for the rendering process to finish is reduced with multiple encoders.
Deciding which RTX 50-series GPU is best suited for your system ultimately comes down to your workflow as well as the specific needs of your project. It’s important to consider the codecs you’ll be working with, and its impact on your workflow from a cost-benefit perspective. For some workflows, a single decoder and encoder offered by the RTX 5070 Ti and RTX 5070 might be more than enough. However, for those working under tight deadlines, the additional encoders and decoders offered from the RTX 5090 and RTX 5080 should speed up your workflows and may be worth the investment.
Decoder and Encoder Performance
NVIDIA introduces what appears to be significant performance improvements in encoding/decoding with Gen 9 encoders and Gen 6 decoders. They claim that the multi-encoder design can export video 60% faster than the GeForce RTX 4090 and up to 4x faster than the GeForce RTX 3090. However, it is unclear whether the performance gains are driven by generational improvements, the increased number of encoders and decoders, or a combination of both.

Source | NVIDIA Studio Youtube Channel
These improvements aren’t just about speed; they also include image quality, which can be an interesting metric to measure. With the Gen 9 Encoder, NVIDIA claims that there is a 5% quality improvement in exporting HEVC, AV1 (BD-DR), and AV1 UQ (Ultra Quality). However, there is some ambiguity, as NVIDIA also mentions a 5% increase in compression without sacrificing quality. Some further clarification would be beneficial to determine if the 5% quality improvement is related to the 5% increase in compression quality, or if there is another metric that NVIDIA is using internally to define ‘quality’. Nonetheless, a 5% improvement is still minor, but in the long run will help content creators produce a slightly better quality image, with reduced storage capacities for holding media, all while exporting at much faster rates as compared to previous gen GPUs.

Source | NVIDIA Studio Youtube Channel
The feature that should impact content creators the most is the Gen 6 NVIDIA Decoder. Decoders take some of the processing load off of the GPU, and this should provide smooth playback of footage within a timeline or viewport, as long as the codec is supported for hardware-accelerated decoding. The claim is that dual decoders can process H264 media twice as fast as previous-generation GPUs. H264 (AVCHD) is a highly compressed codec that demands significant system resources for decoding and playback within a timeline.
With the Blackwell architecture supporting H264 4:2:2, processing this format twice as fast could eliminate bottlenecks found in workflows, such as multicam timelines, using media that is All-Intra or LongGOP H264 4:2:2. Multicam timelines typically experience latency and dropped frames when live switching between cameras within an NLE timeline. Dual decoders should be able to process these files more efficiently, benefitting those who are looking for uninterrupted playback from their program monitor when switching between cameras.
FP4 Support
The last notable feature that NVIDIA announced with the RTX 50-series GPUs is support for FP4. What is FP4 and how does this fit into content creation workflows? As generative AI tools become more common among content creators, they must consider the trade-offs when creating images via generative models like Stable Diffusion or FLUX. They can either generate highly detailed images that take longer to process or generate images at a faster rate but with less detail included in the final output.
Compared to the more ubiquitous FP16, a model in FP4 is essentially a compressed version of the model, sacrificing accuracy for speed and memory footprint. Think of FP4 as a simplified block of information (bits) needed to generate an image. FP16 is that same block but with additional blocks stacked on top, providing more data for the model to work with to produce an image. However, these extra blocks take longer to process the ‘bits’ of information within that stack, resulting in slower image generation but with additional details included in the final output.
If that analogy isn’t clear, consider FP16 and FP4 as similar to uncompressed and compressed media files: FP16 resembles an uncompressed 10-bit 4:2:2 file, while FP4 is more like a compressed 8-bit 4:2:0 file. There will be times when a project requires a higher quality output, but if you are generating an image for a project that does not need extremely fine details, like posting to your social media distribution channels, then a more compressed file should be fine.
AI models need VRAM to process data and produce images, but with the optimizations introduced in the RTX 50-series GPUs, NVIDIA claims that FP4 can process images faster than FP16 and creates images using less VRAM with minimal effect on image quality, when compared to previous-gen GeForce RTX GPUs via FLUX.1 [dev]. While FP4 is faster than FP16 in terms of processing, the trade-off is in the level of detail. FP4 pulls from a smaller (compressed) model, which may generate an image lacking finer details that some content creators prefer.

Source | NVIDIA Technical Blog
As generative AI tools become more common among content creators, they must consider the trade-offs when creating images through a generative model. The options are to generate highly detailed images that take longer to process or generate images at a faster rate but with less detail included in the final output. Researching software that supports FP4 at both the hardware and software level is worth the effort. While FP4 promises faster model processing in conjunction with decreased hardware processing requirements, its adoption will depend on how widely ISVs adopt FP4 into their applications to make it available for content creators.
Conclusion
As the public release date nears for NVIDIA’s RTX 50-series GPUs, we will see how these features perform in real-world scenarios, assuming that ISVs integrate these features into their applications. For us, a good portion of this next year will include evaluating the GeForce RTX 50-series GPUs in action.
Certain features such as support for 4:2:2 chroma subsampling formats has potential to bridge gaps between production and post-production, by improving playback within post-production applications for codecs such as H264 and H265 10-bit 4:2:2. Next-gen NVDEC decoders are expected to enhance timeline performance when working with media that requires heavy processing in post-production, while next-gen NVENC encoders should make exporting footage faster and at higher quality, freeing up more time for creative tasks. Additionally, the total quantity of encoders and decoders might reduce bottlenecks by offloading processing from the GPU, leading to more efficient performance in post-production applications.
Hardware support for FP4 could open up opportunities for creators who want to generate AI images locally on their systems. Lastly, NVIDIA’s integration of AI processing in GPU processing and rendering into the hardware processing of a GPU may change how content creators utilize their GPUs.
The true impact of these features will only be understood as we (and others) are able to test them, and as content creators share their experiences, demonstrating how the RTX 50-series GPUs integrate into real-world workflows.
Update Log
1/31/2025 – updated to include link to RTX 5090 and RTX 5080 performance benchmark for content creation articles.