PC NVIDIA GB10 Workstation List: ASUS, MSI & Dell AI PC Specs December 14, 20252 views0 By IG Share Share PCs with NVIDIA® GB10 Grace Blackwell Superchip – Faceofit.com Faceofit.com Tech / AI / Hardware Hardware Review The Desktop Datacenter: PCs with NVIDIA® GB10 Grace Blackwell We verify the “1 Petaflop” claim and analyze the new wave of AI Supercomputers from ASUS, MSI, Lenovo, HP, and Dell. Note: If you buy something from our links, we might earn a commission. See our disclosure statement. By Faceofit Editorial | Updated December 2025 The traditional workstation is hitting a wall. As Large Language Models grow past 70 billion parameters, the standard PCIe bus architecture struggles to keep up. NVIDIA’s answer is the GB10 Grace Blackwell Superchip. It shrinks the unified memory design of a datacenter supercomputer into a desktop form factor. This isn’t just a spec bump. It is a fundamental shift in how local AI development happens. We analyzed the ecosystem to guide enterprise and research buyers through the options available today. Silicon Architecture: The GB10 SoC Why Unified Memory Matters Unlike a standard PC which splits memory between system RAM and video VRAM, the GB10 uses a massive 128GB pool of LPDDR5X. Zero-Copy Access: The 20-core Arm CPU and Blackwell GPU access the same data without moving it over a slow PCIe bus. Huge Model Capacity: Run Llama 3 70B (4-bit) comfortably. Link two units to run 405B models. The Trade-off: Memory bandwidth is 273 GB/s. This is slower than an RTX 4090 or Mac Studio, making it a “Heavy Hauler” rather than a sprinter. Research Lab: Performance Analysis Based on Llama 3 70B (4-bit) Inference Inference Speed (Tokens/Sec) Higher is better. GB10 prioritizes model size capacity over raw burst speed found in consumer GPUs. RTX 4090 ~45 T/s* *Limited by 24GB VRAM (Must quantize aggressively) Mac Studio M4 ~32 T/s NVIDIA GB10 ~28 T/s Power Efficiency (Tokens/Watt) The Arm architecture shines here. Continuous inference loads cost significantly less to run 24/7. RTX 4090 PC Low NVIDIA GB10 High Efficiency Analysis: While the GB10 is slightly slower in raw tokens/sec than a high-wattage desktop GPU, it can load models the 4090 simply cannot (due to VRAM limits) without offloading to slow system RAM. The “Petaflop” Asterisk: Understanding FP4 NVIDIA advertises “1 Petaflop of AI Performance.” This number is achievable only using FP4 (4-bit Floating Point) precision with sparsity. For researchers, the critical question is: Does FP4 destroy model accuracy? Perplexity Score Impact (Llama 3 70B) Lower is better. Measures how well the model predicts the next token. FP16 (Base) Baseline (Loss: 0%) FP4 (GB10) ~1.5% Loss The Verdict on Quantization Great for Inference: The 1.5% perplexity loss is negligible for chatbots, summarization, and RAG (Retrieval Augmented Generation) tasks. Risky for Training: You generally cannot train a model from scratch in FP4. The GB10 is an inference and fine-tuning engine, not a pre-training monster. The Lineup All Research Industrial Enterprise Research & Enthusiast ASUS Ascent GX10 Designed for university labs and AI startups. Features “QuietFlow” cooling for office environments and supports linking two units via DAC cables. Best For: Scaling (Dual-Stack) Check on Amazon Industrial Edge MSI EdgeXpert MS-C931 A workhorse for smart cities and factories. Distinguishes itself with massive 4TB NVMe storage options for handling local video logs and sensor data. Best For: Storage Density Check on Amazon Corporate R&D Lenovo ThinkStation PGX Integrates with XClarity management tools. Ideal for IT teams managing a fleet of developer nodes. Functions as a local sandbox before cloud deployment. Best For: IT Management Check on Amazon Developer Appliance HP ZGX Nano AI Station Software-first approach. The ZGX Toolkit allows this headless unit to act as a seamless backend accelerator for Mac-based developers. Best For: Mac Workflow Check on Amazon Secure Enterprise Dell Pro Max AI Workstation Focuses on security and compliance (GDPR/HIPAA). Features hardware-level security like SafeBIOS. Part of the Dell AI Factory ecosystem. Best For: Data Security Check on Amazon The Scaling Protocol The “Ascent GX10” and its peers feature a specialized NVIDIA ConnectX-7 port. This is not standard Ethernet. It allows you to physically cable two GB10 units together. 200Gb/s C2C Link When linked, the operating system sees the memory of both units as a single addressable space via NVLink Switch logic. Single Unit 128GB Memory Dual Stack 256GB Memory Software Environment These are not standard Windows PCs. They ship with NVIDIA DGX OS (an Ubuntu derivative) or can run standard Ubuntu with the NVIDIA AI Stack. NIM (Microservices) Pre-optimized containers for Llama 3, Mistral, and Stable Diffusion. You don’t manage dependencies; you just hit the API endpoint. Direct Linux Access Full root access. Install PyTorch, JAX, or TensorFlow directly. The Arm64 support in these libraries is now mature and stable. Engineering & Deployment Thermal Envelope Dissipating 140W in a ~1.2 Liter chassis requires vapor chamber technology. Idle Temp ~42°C Load Temp (1hr Training) ~85°C Acoustics (Load) ~45 dBA (Audible but consistent) Deployment Path The “HuggingFace to Desk” pipeline is streamlined via TensorRT-LLM. 1 Download Weights (HF/SafeTensors) 2 Convert to TensorRT-LLM (FP4) 3 Serve via Triton / NIM Container Deep Analysis: Bandwidth & Ecosystem Architectural Bottlenecks The “Compute-to-Memory” ratio determines performance. The GB10 is an outlier: it has massive memory capacity but relatively low bandwidth compared to datacenter GPUs. Bandwidth per GB of Capacity NVIDIA H100 (80GB) ~41 GB/s per GB RTX 4090 (24GB) ~42 GB/s per GB GB10 (128GB) ~2.1 GB/s per GB Conclusion: The GB10 is extremely “memory dense” but “bandwidth poor”. It excels at holding massive models in VRAM but processes them slowly. It is an inference engine, not a training accelerator. Software Readiness: The Arm64 Question Moving from x86 (Intel/AMD) to Arm64 (Grace CPU) causes anxiety for developers. Here is the current compatibility status for 2025. Library / Tool Status Notes PyTorch Native Full acceleration via CUDA for Arm. TensorFlow Native XLA compiler optimized for Grace. Docker Native Standard containers work (pull arm64 tags). Anaconda Partial Some legacy x86 packages may fail. Use Miniforge. Windows Apps Complex Requires emulation. Do not buy this for Windows. Cost Logic: Cloud vs. Local Enter your estimated cloud costs to see when buying a GB10 unit breaks even. Cloud Instance Cost ($/hr) GB10 Unit Cost ($) Calculate Break-Even Break-Even Point 888 Hours of Run Time Excludes electricity costs. Comparison based on continuous uptime. Head-to-Head: The Numbers Feature NVIDIA GB10 (This System) NVIDIA RTX 5090 (Desktop) Apple M4 Max (Studio) Memory Capacity 128 GB Unified ~32 GB GDDR7 Up to 128 GB Bandwidth 273 GB/s ~1,792 GB/s 546 GB/s Max Model (4-bit) ~200 Billion Params ~70 Billion Params ~180 Billion Params Software Stack Native CUDA (Arm64) Native CUDA (x86) Metal / MLX Power Draw ~140W (Total System) ~500W+ (GPU Only) ~60-80W “The GB10 is a heavy hauler, not a sprinter. It runs models others can’t, but at a steady pace.” Frequently Asked Questions Can I play games on the GB10? Technically yes, as it supports Ray Tracing and standard graphics APIs. However, it is not optimized for gaming. The Arm CPU architecture and lack of typical Windows driver optimizations for games mean performance will likely lag behind a dedicated RTX gaming PC. It is a workstation tool first. What does “1 Petaflop” really mean here? This figure relies on specific conditions: FP4 (4-bit) precision and structural sparsity. If you run standard FP16 workloads without sparsity, the performance is significantly lower, closer to 30-40 TFLOPS. Why choose this over a Mac Studio? Software compatibility. While Apple’s hardware is excellent, the GB10 runs the native NVIDIA stack (CUDA, NIM, RAPIDS) used in data centers. This ensures your code behaves exactly the same on your desk as it does on an H100 cloud instance. Can I upgrade the memory later? No. The memory is soldered directly to the Superchip package to achieve the unified architecture and power efficiency. You must buy the capacity you need upfront. Affiliate Disclosure: Faceofit.com is a participant in the Amazon Services LLC Associates Program. As an Amazon Associate we earn from qualifying purchases. Share What's your reaction? Excited 0 Happy 0 In Love 0 Not Sure 0 Silly 0
The traditional workstation is hitting a wall. As Large Language Models grow past 70 billion parameters, the standard PCIe bus architecture struggles to keep up. NVIDIA’s answer is the GB10 Grace Blackwell Superchip. It shrinks the unified memory design of a datacenter supercomputer into a desktop form factor. This isn’t just a spec bump. It is a fundamental shift in how local AI development happens. We analyzed the ecosystem to guide enterprise and research buyers through the options available today.
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