Dell Pro Precision with GB300

The Dell Pro Precision with GB300 is a specialized deskside AI workstation engineered to bring enterprise-grade compute out of the cloud and into local office and lab environments. Powered by the NVIDIA Grace Blackwell Ultra Desktop Superchip, this 40kg machine bypasses the recurring operational costs, latency spikes, and security exposures associated with third-party inference APIs. At a base MSRP of $160,195, it sits in an elite tier: far beyond consumer setups or standard workstation PCs, directly targeting engineering organizations, defense teams, and quantitative research groups building sovereign AI systems.

For teams searching across hardware directories for enterprise systems or evaluating Unknown hardware for AI development, the GB300 workstation delivers data center silicon in an office-deployable chassis. It integrates a 72-core ARM Neoverse V2 Grace processor with a Blackwell GPU, utilizing an ultra-high-bandwidth coherent memory architecture. Dell configures the workstation with Ubuntu 24.04 LTS and the complete NVIDIA AI Developer Tools stack under Ubuntu Pro, providing a turnkey platform that boots straight into a production-grade CUDA environment.

Deploying the Dell Pro Precision with GB300 for AI workloads establishes an on-premises compute node capable of sustained high-throughput inference and agent orchestration. Rather than leasing rack space in a remote facility or paying millions of tokens monthly to model providers, engineering teams gain dedicated access to 20 petaFLOPS of 4-bit AI compute with single-tenant data sovereignty.

The engineering core of the system is the unified Grace Blackwell Ultra architecture, delivering extreme bandwidth and dense tensor acceleration.

• GPU Compute: Up to 20 petaFLOPS of 4-bit (FP4) performance. The Blackwell tensor cores incorporate native FP4 micro-tensor scaling, doubling inference throughput over FP8 while preserving accuracy in quantized architectures.
• VRAM Capacity: 252GB of ultra-fast HBM3e GPU memory. This 252GB GPU for AI workloads removes standard VRAM limitations, allowing massive parameter weights and long KV caches to reside entirely on the accelerator.
• Host Processor & System Memory: 72-core NVIDIA Grace CPU (ARM Neoverse V2) paired with 496GB of LPDDR5X memory. The CPU and GPU are linked via high-speed NVLink-C2C (Chip-to-Chip), creating a 748GB coherent memory pool.
• Display & Auxiliary Compute: NVIDIA RTX PRO 2000 Blackwell Workstation Edition (75W, 16GB GDDR7). This secondary discrete card handles multi-monitor display outputs and local desktop acceleration, keeping the HBM3e pool entirely dedicated to compute.
• Storage Array: 16TB total high-speed storage via 4x 4TB M.2 2280 PCIe Gen5x4 NVMe TLC self-encrypting drives (SED), configured for high-throughput checkpoint streaming and dataset loading.
• Networking: Integrated NVIDIA ConnectX-8 SuperNIC, providing native multi-gigabit fabric support for distributed cluster scale-out.
• Operating System: Ubuntu 24.04 LTS with Ubuntu Pro, pre-loaded with NVIDIA AI Developer Tools (CUDA, TensorRT-LLM, NeMo, Triton Inference Server).
• Physical Profile: 40kg deskside chassis engineered with proprietary MaxCool thermal architecture to handle sustained enterprise thermal loads without data-center HVAC requirements.

The critical metric in local AI inference performance is memory bandwidth. When generating tokens, generative LLMs are strictly memory-bandwidth bound during the autoregressive phase. With 252GB of HBM3e, the Dell Pro Precision with GB300 achieves multi-terabyte-per-second memory bandwidth on the primary accelerator. The NVLink-C2C interconnect allows the GPU to access the 496GB LPDDR5X CPU memory at bidirectional rates exceeding standard PCIe Gen5 slots by an order of magnitude, offering practitioners 748GB of unified address space to prevent out-of-memory errors on massive contexts.

The primary strength of the Dell Pro Precision with GB300 local LLM setup is memory headroom. Modern large language models require distinct allocations for model weights and key-value (KV) cache. With 252GB of native HBM3e backed by 496GB of coherent memory, this system redefines what can run on a single deskside workstation.

While typical prosumer hardware struggles with medium-weight models under extended context, the GB300 is the ultimate hardware for running 70B parameter models at full precision:

• Llama 3.1 70B and Qwen 2.5 72B: These models load into HBM3e in unquantized 16-bit (FP16/BF16) format, requiring approximately 140GB to 144GB of VRAM. The remaining 100GB+ of HBM3e is allocated directly to KV cache, allowing maximum context windows (up to 128k tokens) across multiple concurrent request streams without spilling into slower memory tiers.
• DeepSeek-R1 and DeepSeek-V3 (671B MoE): Dense or massive mixture-of-experts (MoE) models can be loaded locally using modern quantization. Using FP4 or INT4 quantization, DeepSeek-R1 (which requires roughly 340GB to 380GB of memory) maps cleanly across the 748GB coherent memory pool. The 37B active parameters execute across Blackwell's tensor cores while system memory handles inactive routing weights.
• Llama 3.1 405B: A flagship challenge for local inference. At FP4 precision, Llama 3.1 405B requires roughly 210GB to 230GB of weight storage. The entire model fits directly inside the 252GB HBM3e pool, enabling deskside execution of the world's premier open weight foundation model at native hardware speeds.

Dell Pro Precision with GB300 tokens per second metrics vary by model architecture and precision:

• Llama 3.1 70B (FP8 / FP16): Expect between 45 to 80+ tokens per second on single-stream generation, scaling with optimized TensorRT-LLM kernels.
• Llama 3.1 405B (FP4): Sustained throughput lands between 15 to 30 tokens per second, making real-time reasoning and agentic function calling entirely practical on a deskside unit.
• Multi-Agent Concurrency: Dell rates the system to handle up to 150 concurrent AI agents on-device. When running lighter task-specific worker models (such as Llama 3.1 8B or Mistral NeMo 12B at INT4/FP8), the workstation easily parallelizes dozens of simultaneous tool-calling loops.

The architectural sweet spot for the GB300 is native FP4 via TensorRT-LLM. Unlike older generations that relied on INT4 with precision degradation, Blackwell introduces micro-tensor FP4 scaling. This hardware feature preserves output perplexity close to FP8 while doubling raw compute throughput up to 20 petaFLOPS and halving memory footprints.

The Dell Pro Precision with GB300 is not designed for hobbyists experimenting with casual home chat interfaces. At an entry price of $160,195, it is precision equipment built for enterprise and institutional buyers searching for the best hardware for local AI agents 2026.

• Autonomous Multi-Agent Systems: Development teams building multi-agent workflows (using frameworks like AutoGen, CrewAI, or LangGraph) face runaway costs and rate-limiting on cloud APIs. Chaining thousands of intermediate reasoning steps locally eliminates API latency and recurring per-token fees.
• Secure Enterprise Inference: Healthcare organizations, defense contractors, and financial institutions handling confidential codebases or protected health information (PHI) cannot route data outside their perimeter. The GB300 provides sovereign execution with SED hardware encryption and local Ubuntu Pro security maintenance.
• R&D and Fine-Tuning Teams: With 252GB of dedicated HBM3e and an integrated ConnectX-8 SuperNIC, research teams can run extensive parameter-efficient fine-tuning (PEFT/LoRA) and full-parameter fine-tuning on sub-70B models, then scale across nodes using standard NVIDIA cluster software.

When evaluating the best AI chip for local deployment and deciding on the best hardware for running AI models locally, the GB300 occupies a distinct operational profile compared to alternative workstation architectures.

A standard quad-GPU workstation equipped with four NVIDIA RTX 6000 Ada Generation cards yields 192GB of aggregate VRAM (48GB x 4) at a lower hardware price point ($35,000 to $45,000). However, the architecture is fundamentally split. Memory must be shared over PCIe Gen4 or standard bridge connections, creating severe communication bottlenecks during tensor parallelism. The GB300 provides 252GB of unified HBM3e on a single accelerator alongside 496GB of CPU memory, eliminating inter-card PCIe latency. Furthermore, the Grace Blackwell architecture delivers native FP4 compute, giving the GB300 significantly higher inference density and compute throughput for ultra-large models like Llama 405B.

A single NVIDIA DGX H100 node provides 640GB of aggregate VRAM across eight SXM5 GPUs, offering raw compute that exceeds the GB300. However, DGX systems cost upwards of $350,000 to $450,000, demand high-density 3-phase 208V/480V rack power, generate over 10kW of heat, and require dedicated enterprise data-center facilities. The Dell Pro Precision with GB300 condenses 252GB of next-generation HBM3e and 748GB of coherent system memory into a single 40kg deskside footprint with MaxCool thermal management, allowing teams to place massive AI compute directly into an office laboratory without capital facility investments.