Nvidia fires back at AMD, claims RTX 5090 is twice as fast as top Radeon in DeepSeek benchmarks

Skye Jacobs

Posts: 2,010   +58
Staff
The big picture: Nvidia has fired back at AMD with new benchmark results showcasing the superior performance of its latest GPUs running DeepSeek's AI models. This comes after AMD's recent publication of benchmarks that positioned its Radeon RX 7900 XTX ahead of Nvidia's offerings.

Nvidia's counterattack claims that its new GeForce RTX 5090 GPU outperforms AMD's flagship by a staggering margin. According to Team Green, the RTX 5090 is up to 2.2 times faster than the RX 7900 XTX when running DeepSeek R1 AI models.

The tech giant conducted extensive benchmarks using three versions of the DeepSeek R1 AI model: Distill Qwen 7b, Llama 8b, and Qwen 32b. When using the Qwen LLM with 32b parameters, Nvidia reports that the RTX 5090 was 124 percent faster than AMD's contender, while the previous-generation RTX 4090 still managed a 47 percent lead.

Similar patterns emerged across other tests. With Llama 8b, the RTX 5090 reportedly outpaced the RX 7900 XTX by 106 percent, while the RTX 4090 maintained a 47 percent advantage. Even in the Qwen 7b test, Nvidia's latest offering was 103 percent quicker, with the RTX 4090 showing a 46 percent performance edge.

These results starkly contrast with AMD's earlier benchmarks, which had shown the RX 7900 XTX outperforming NVIDIA's RTX 4090 and 4080 in most scenarios, with leads of up to 113 percent and 134 percent, respectively.

Nvidia also claimed that its GeForce RTX 50 Series GPUs, powered by up to 3,352 trillion operations per second of AI processing capability, are uniquely positioned to run DeepSeek's family of distilled models faster than any other option in the PC market. This is because DeepSeek's R1 model family, which Nvidia described as part of a new class of 'reasoning models.

These LLMs are designed to mimic human problem-solving processes by allocating more computational resources to 'thinking' and 'reflecting' on complex issues. This approach, known as test-time scaling, allows the model to dynamically allocate computing resources during inference to reason through problems more effectively.

Nvidia also noted that its RTX 50 Series GPUs, featuring dedicated fifth-generation Tensor Cores, are built on the same Blackwell GPU architecture that drives AI innovations in data centers. This architecture enables RTX to fully accelerate DeepSeek models, delivering peak inference efficiency on personal computers.

The company also touted its RTX AI platform, an ecosystem that opens up DeepSeek-R1 capabilities to over 100 million Nvidia RTX AI PCs worldwide, including those equipped with the latest GeForce RTX 50 Series GPUs.

Nvidia argued that high-performance RTX GPUs ensure AI capabilities remain accessible, even without an internet connection. This not only offers low latency but also enhances privacy, as users can avoid uploading sensitive materials or exposing their queries to online services.

Permalink to story:

 
The one and only metric is the video card burn rate per second and nVidia has outdone itself once again

nVidia: The Way It's Meant To Burn.
 
I think the big players will do their own research, unless kickbacks ie corruption.
Plus they will demand full reports from AMD and Nvidia . If I was buying $20 Million dollars of cards I wouldn't take no for an answer

Other factors as well than just total cost /result, Support, ease of use , supply time etc
Plus a long-term biggy . F Nvidia and their proprietary closed system
 
So... are we back for a crypto-craze situation again for GPUs since client grade GPUs are now a viable option in comparison to accelerators?
 
So... are we back for a crypto-craze situation again for GPUs since client grade GPUs are now a viable option in comparison to accelerators?
No - at least not for larger server parks. People want blade servers, they won’t be buying clunky gpu’s. Ideally a blade server would contain 4 or 5 gpu’s on a single blade with watercooled pipes, powered by 2x 1500w hotswappable psu’s.
The Blackwell server gpus also have the full 24k cuda cores compared to the 5090, which only has 21k. They are stacked with HBM memory which is better suited for AI workloads as well.
Consumer gpu’s aren’t suited for large businesses.
However - graphics/cad designers will choose regular RTX cards instead now that they’re close to their equivalent as they perform almost as good compared to workstation gpus at a lower cost
 
Yes Nvidia your imaginary card maybe faster, but who cares if nobody can get one to use ? Paper launches are not viable products. Nvidia can't be trusted anymore as they lie about their launch dates.
 
Back