HP ZGX Fury G1n AI Station

Generated from this product’s spec sheet. Editor reviews refine it over time.

The compute engine at the core of the ZGX Fury G1n AI Station is the NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip. This SoC integrates a 72-core ARM Neoverse V2 Grace CPU with an NVIDIA Blackwell Ultra GPU across an ultra-high-speed NVLink-C2C (Chip-to-Chip) interconnect delivering 900 GB/s of bidirectional bandwidth. This hardware interconnect allows the CPU and GPU to share a unified physical address space without standard PCIe bus serialization penalties.

• GPU Architecture: NVIDIA Blackwell Ultra with 160 Streaming Multiprocessors (SMs) and 5th-generation Tensor Cores.
• Compute Density: Delivers up to 20 PFLOPS of FP4 Tensor Core compute (15 PFLOPS dense), 10 PFLOPS of FP8/FP6 compute, and 5 PFLOPS of standard FP16/BF16 compute.
• GPU Memory: 252 GB of ECC HBM3e delivering 7,100 GB/s (7.1 TB/s) of memory bandwidth.
• CPU Subsystem: 72-core NVIDIA Grace Neoverse V2 (ARMv9 architecture) paired with 496 GB of ECC LPDDR5X delivering 396 GB/s bandwidth.
• Total Coherent Memory: 748 GB unified address space.
• Form Factor & Cooling: Liquid-cooled 5U-rackable tower, deployable as a freestanding workstation or converted for standard server rack integration.
• Storage: 2 TB or 4 TB self-encrypting NVMe M.2 SSD.
• Networking: Dual-port QSFP112 supporting up to 400 GbE per port via integrated NVIDIA ConnectX-8 networking, alongside 10 GbE RJ-45 and dedicated Baseboard Management Controller (BMC) remote management ports.

Memory bandwidth is the primary operational constraint for autoregressive large language model token generation. While conventional multi-GPU workstations relying on DDR5 or GDDR6 top out between 1 TB/s and 2 TB/s aggregate, the 252GB GPU for AI on this system reaches 7.1 TB/s across its HBM3e stack. This level of throughput translates directly into minimal per-token latency during the memory-bandwidth-bound decode phase of LLM inference.

Evaluating HP ZGX Fury G1n AI Station VRAM for large language models highlights its main structural advantage: memory headroom. Because the system provides 252 GB of ultra-fast HBM3e directly on the Blackwell Ultra GPU, coupled with 496 GB of CPU memory via NVLink-C2C, it eliminates standard model-fitting constraints.

Models that fit entirely inside the 252 GB HBM3e partition benefit from the uncompromised 7.1 TB/s memory bus:

• Meta Llama 3.1 70B and Qwen 2.5 72B: Run completely unquantized at native BF16/FP16 precision with expansive context windows (128k+ tokens) while leaving dozens of gigabytes free for dynamic KV cache allocation. HP ZGX Fury G1n AI Station tokens per second on 70B models comfortably exceed 80-120 tokens/sec in single-stream generation.
• Llama 3.1 405B at FP4: Utilizes Blackwell native 5th-generation Tensor Core FP4 micro-scaling formats, allowing the entire 405-billion parameter weights to sit directly within the 252 GB HBM3e space for interactive inference speeds.

For architectures exceeding 252 GB, the system leverages its 748 GB coherent memory space. This setup handles hardware for running 1T parameter models using native FP4 quantization pipelines:

• DeepSeek-R1 and DeepSeek-V3 (671B MoE): With MoE architectures, only a fraction of weights are activated per token. The 748 GB coherent footprint easily loads the entire model at FP4/FP8. Active expert routing operates across the high-speed interconnect, yielding sustained execution without out-of-memory crashes.
• Fine-Tuning: The system can fine-tune architectures up to 100 billion parameters locally using LoRA or full parameter adaptation, bypassing multi-system synchronization overhead.

The sweet spot for daily engineering tasks is FP8 execution for 70B-class models, granting zero perceptible perplexity loss while leaving massive headroom for multi-turn conversational agents with million-token context buffers.

The ZGX Fury G1n is designed for enterprise engineering environments rather than individual hobbyists:

• Teams Building Agentic Workflows: Multi-agent architectures run multiple concurrent model instances to critique code, browse web environments, and perform tool calling. The massive 748 GB memory pool lets teams host a centralized router alongside specialized sub-models simultaneously. This profile makes it among the best hardware for local AI agents 2026 demands.
• Local Inference and Fine-Tuning Servers: Organizations handling regulated data (healthcare, legal, proprietary codebases) that cannot route requests through third-party APIs can deploy the ZGX Fury G1n on-premises. The included dual 400 GbE QSFP112 ports allow the workstation to act as a departmental micro-cluster endpoint.
• Developers Running Heavy Local LLMs: Engineers testing custom quantization kernels, long-context retrieval-augmented generation (RAG), and mixture-of-experts pipelines gain a bare-metal Linux environment (Ubuntu 24.04 LTS) optimized for the latest CUDA, TensorRT-LLM, and vLLM releases without operating system virtualization layers.

When deciding on high-end local AI infrastructure, teams typically weigh dedicated Superchips against multi-GPU workstation towers.

A quad-RTX 6000 Ada workstation provides an aggregate 192 GB of VRAM (4x 48 GB GDDR6) over PCIe Gen 4/5 interfaces.

• Memory Capacity: The ZGX Fury G1n offers 252 GB of HBM3e on the GPU side, with 748 GB total coherent memory, easily surpassing the 192 GB hard limit of the 4x Ada configuration.
• Memory Bandwidth: The quad-Ada setup delivers roughly 3.8 TB/s aggregate bandwidth constrained by inter-GPU PCIe communication. The single Blackwell Ultra GPU reaches 7.1 TB/s on a single monolithic bus, avoiding pipeline stalls associated with tensor-parallel synchronization over PCIe lanes.
• Model Support: While a 4x 6000 Ada setup struggles with models larger than 200B parameters without aggressive offloading, the ZGX Fury G1n accommodates up to 1-trillion parameter MoE models via FP4.

Data center server appliances like the DGX H100 provide raw distributed horsepower (8x SXM GPUs), but require specialized 48-volt, high-density server racks and industrial HVAC installations.

• Deployment Flexibility: The liquid-cooled ZGX Fury G1n operates inside office environments or standard 5U rack configurations without requiring dedicated data center infrastructure.
• Architecture Generation: The GB300 desktop implementation brings 5th-generation Tensor Cores and native FP4 precision, an optimization that the Hopper architecture lacks. This architectural leap establishes the GB300 as the best AI chip for local deployment where power footprint, space, and local acoustic levels must remain tightly controlled.

For enterprise teams that require massive parameter scaling without the operational complexity of a data center cluster, the HP ZGX Fury G1n AI Station delivers unmatched local inference performance and architectural flexibility.