- The flagship GPU of Nvidia offers domination while other models barely advance
- Intel Arc GPUs are promising in AI, hampered by a fragile assembly support
- AMD is positioned between Nvidia levels with incoherent results between software
The content creation workflows are increasingly dependent on GPUs, the requests extending from the video editing in real time to the offline rendering and the tools assisted by the AI.
A recent series of Pugetsystem benchmarks comparing the latest NVIDIA, AMD and Intel class general class cards has shown a complex landscape.
In this sector, raw performance, application support and profitability tell of each of the different stories.
Performance trends in rendering engines
The references take place on Blender, V-Ray, Redshift and Octane highlight the continuous force of Nvidia.
While Blender is now taking charge of Intel and AMD, most rendering engines remain linked to Cuda and RTX, leaving non -Nvidia GPUs in a disadvantage.
The GeForce RTX 5090 has always obtained the highest score, with margins of more than 20% compared to the RTX 4090 in certain tests.
However, the rest of the Nvidia 50 series has often taken place with their predecessors 40.
AMD has entered the graphics with the Radeon RX 7900 XTX, which has positioned between the Nvidia maps of intermediate level.
Intel, despite promising results in specific engines, has remained grouped at the lower end, suggesting that if Nvidia dominates the supported software, the door is not entirely closed to the alternatives.
In professional publishing tools such as Premiere Pro, After Effects and Davinci Resolve, the results were mixed.
NVIDIA Hardware leads to GPU dependent tasks, in particular with heavy projects of the effects.
The RADEON RX 9070 XT of AMD has shown solid results in certain types of codecs, surpassing new NVIDIA cards in certain LongGop workloads, but it fought in 3D -controlled pipelines where NVIDIA maintained a large lead.
The Intel CRA range has offered decent competitions in terms of entry, especially when associated with its Openvino toolbox.
However, instability in resolution benchmarks highlighted growing pain in optimization.
These inconsistencies make it more difficult to recommend Intel GPUs for reliable production, although their presence suggests an evolving role in specific workloads.
AI -centered tests, such as the MLPERF client, added another dimension, because here, the GPUs with Intel Arc provided unexpected performance, with head times for the first generation of tokens.
NVIDIA has found domination in the sustained token flow, the RTX 5090 increased by almost 30% compared to 4090.
In the UNREAL engine references, NVIDIA also led overall, although the 9070 XT of AMD sometimes corresponded to the RTX 5070 Ti.
Intel cards have constantly dragged but have demonstrated the competitiveness adjusted to the price.
This suggests that the Intel Arc range could serve as a cheap GPU for experimental AI or Secondary systems, despite traditional limited adoption.
For creators focused on the reliability of rendering and publishing, Nvidia continues to hold the strongest position, in particular with its flagship GPU.
However, apart from high -end results, performance improvements in relation to previous generations are uneven.
AMD offers competitive options in certain workflows and remains an alternative for those who envisage graphics cards for video publishing at specific price levels.
Intel, despite the delay in most professional tools, has shown surprising results in AI inference benchmarks and still provides low -cost entry points.
The global image suggests that if Nvidia establishes the standard, AMD and Intel offer niches where their GPUs can serve creators looking for alternatives.
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