SGT: “Wouldn’t an RTX5090 make a better computer as an All In One unit in the style of Project DIGITS, compared to Project DIGITS or AGX Thor for office use in AI work, as a compact computer?”
GPT-4o: RTX 5090 as an All-in-One Unit: The Future of Compact AI Workstations
Introduction
1. Performance Comparison
RTX 5090
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AI Performance: 3.352 AI petaflops (via Tensor Cores, optimized for consumer AI applications).
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Performance: NVIDIA claims the RTX 5090 offers up to twice the performance of its predecessor, the RTX 4090. (The Verge)
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Specifications: The RTX 5090 features 21,760 CUDA cores and 32GB of GDDR7 memory.
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Versatility: Suitable for training small-to-medium AI models, running inference tasks, creative workloads, and more.
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Gaming and Rendering: Industry-leading performance with advanced ray-tracing capabilities.
DIGITS Project
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AI Performance: Up to 1 AI petaflop.
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Specialization: Designed specifically for AI research and enterprise-level model optimization.
AGX Thor
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AI Performance: 2 AI petaflops.
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Focus: Real-time, safety-critical AI tasks such as autonomous decision-making for vehicles.
Verdict
2. Form Factor and Compact Design
RTX 5090 in a DIGITS-like Unit
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Transforming the RTX 5090 into a pre-built, all-in-one system would result in a powerful, compact workstation tailored for offices and small businesses.
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It would eliminate the need for users to assemble components, making it accessible to non-technical professionals.
DIGITS Project
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A compact, book-sized design that is portable and space-saving.
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While highly efficient for AI research, its use case is narrow, limiting general office functionality.
AGX Thor
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Larger and designed for industrial setups, making it less practical for compact office environments.
Verdict
3. Software Ecosystem and Usability
RTX 5090
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OS Support: Compatible with Linux and Windows.
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AI Frameworks: Supports TensorFlow, PyTorch, CUDA, and more.
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Use Case Flexibility: Handles AI tasks, productivity, creative applications, and even gaming.
DIGITS Project
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Runs on NVIDIA DGX OS, optimized for AI research but less adaptable to general-purpose office workflows.
AGX Thor
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Runs NVIDIA Drive OS or enterprise-grade Linux, focused on real-time deterministic AI.
Verdict
4. Cost and Accessibility
RTX 5090
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Price: Estimated at $1,999 for the GPU; a pre-built system could cost $2,500 – $3,000.
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Value: Offers superior performance per dollar compared to DIGITS or AGX Thor.
DIGITS Project
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Price: Starts at $3,000.
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Specialization: Optimized for AI research, with limited general-purpose use.
AGX Thor
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Price: Enterprise-grade pricing far exceeding $3,000, tailored for automotive and industrial applications.
Verdict
5. Market Need
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Professionals and small businesses require affordable, high-performance AI workstations that are compact, pre-built, and easy to use.
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Current solutions like DIGITS and AGX Thor cater to niche markets, leaving a gap for versatile office-friendly systems.
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An RTX 5090 all-in-one system could redefine how individuals and businesses approach technology upgrades. Instead of discarding old machines, users could progressively add new, more powerful systems to build their own scalable data centers over time.
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This approach allows for: Sustainability: Reduced electronic waste by re-purposing older systems. Modularity: Seamless integration of new units without replacing the entire setup. Future-Proofing: Users can expand computing power as their needs grow, creating a personal or small-scale data center tailored to their evolving requirements.
Harnessing the Power of RTX 5090 for Large-Scale AI Models
The RTX 5090, with its revolutionary Blackwell architecture, redefines the boundaries of AI computing. Boasting an industry-leading 3.352 AI petaflops per GPU, this powerhouse is not only capable of handling intensive gaming and creative tasks but is also perfectly suited for running large-scale open-source AI models like Meta’s Llama 3.1 and Groq’s Llama-3-Groq-Tool-Use models.
When paired in a dual-GPU configuration, the RTX 5090 provides unparalleled scalability, enabling researchers and developers to tackle even the most demanding workloads. Models like Llama 3.1 (70B), Groq’s advanced tool-use architectures, and Meta’s Llama 3.3 (an enhanced model with even greater capabilities) benefit from the GPU’s massive 32GB GDDR7 memory per card, allowing seamless parallelization of tasks. Additionally, the RTX 5090 can efficiently run powerful Mixture of Experts (MoE) models such as Google’s Switch Transformer or GLaM, which intelligently distribute computational tasks across GPUs for optimal resource utilization.
The RTX 5090 is also capable of handling other cutting-edge models like NVIDIA’s NeMo Megatron, which supports GPT-style architectures with hundreds of billions of parameters, and open-source giants like Falcon 180B and Mistral 7B, which are optimized for high-speed inference and training on massive datasets.
By leveraging the modular scalability of RTX 5090 GPUs, users can build AI systems that grow with their needs. This makes the RTX 5090 not just a piece of hardware, but the foundation for a sustainable, future-proof AI infrastructure that allows businesses and individuals to run state-of-the-art models while expanding capabilities over time. Whether it’s advanced NLP tasks, generative AI, or function-specific applications, the RTX 5090 ensures you’re ready to meet the demands of tomorrow’s AI landscape.
Conclusion: The Case for an RTX 5090 All-in-One System
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Unmatched AI performance for diverse workloads.
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A compact, plug-and-play design ideal for offices.
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Broad compatibility with standard software and workflows.
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Lower costs compared to DIGITS or AGX Thor.
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Sustainability: A future-proof philosophy where new systems complement rather than replace existing setups, enabling users to build their own scalable data centers over time in expanding cluster configuration. (the GPU blocks should plug and play with one cable double their own computation capability).
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The modular approach (adding GPUs or upgrading systems over time) works best with open OS platforms like Linux, which supports scalability and sustainability.
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Scaling – Theoretical Performance: Each RTX 5090 offers 3.352 AI petaflops of performance. In theory, adding a second RTX 5090 could double the raw compute performance to 6.704 AI petaflops. This assumes ideal scaling, where the workload is perfectly distributed between the GPUs.
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