HP ZGX Nano AI Station

Compute and Throughput

• FP16 Performance: 29.71 TFLOPS
• INT8 Performance: 250 TOPS
• Memory Bandwidth: 273 GB/s
• TDP: 140 W (Powered by a 240W USB Type-C adapter)

While the 273 GB/s bandwidth is lower than what you would find on a dedicated H100 or a top-tier RTX 4090, it is optimized for the 20-Core Arm architecture (10 Cortex-X925 + 10 Cortex-A725). This configuration is designed for high-efficiency inference rather than raw training throughput. The 250 TOPS of INT8 performance makes it a powerhouse for quantized model execution, specifically leveraging NVIDIA's Blackwell-specific optimizations for FP4 and FP8 data formats.

Edge AI workloads often require rapid data ingestion. The ZGX Nano is equipped with a 10GbE ConnectX-7 interface, ensuring that model weights can be pulled from local registries or datasets at wire speed. It also supports Wi-Fi 7 and Bluetooth 5.4 for flexible edge deployments where hardwired infrastructure may be limited.

The HP ZGX Nano AI Station VRAM for large language models is its primary selling point. A 128GB buffer allows for the local execution of models that were previously restricted to cloud H100 instances.

The ZGX Nano is rated for models up to 200B parameters. This is a significant threshold for local AI agents and RAG (Retrieval-Augmented Generation) workflows.

• Llama 3.1 405B: Cannot run at full precision, but can be loaded at extreme quantization (IQ2_XS) if the KV cache is managed aggressively.
• Llama 3.1 70B: Runs comfortably at FP16 or Q8_0 with a massive context window (128k+ tokens) without spilling into system RAM.
• DeepSeek-V3 / DeepSeek-R1: These MoE (Mixture of Experts) models fit effectively within the 128GB ceiling at 4-bit or 5-bit quantization, providing high-quality reasoning capabilities locally.
• Qwen 2.5 72B: Can be run at high precision (BF16) with room to spare for multi-user inference sessions.

Given the 273 GB/s bandwidth, users can expect the following estimated performance:

• 7B - 8B Models (Llama 3, Mistral): 45–60 tokens/second (near-instantaneous for agentic loops).
• 30B - 34B Models (Yi, Codestral): 12–18 tokens/second (ideal for real-time coding assistance).
• 70B+ Models: 5–8 tokens/second (sufficient for background processing and complex reasoning tasks).

The 128GB VRAM is a "sweet spot" for multimodal models like Llava or CogVLM, where both image encoders and large language decoders must reside in memory simultaneously. It also enables long-context tasks, such as analyzing entire codebases or long PDF documents, where the KV cache can grow to tens of gigabytes.

The HP ZGX Nano AI Station is not a general-purpose computer; it is a specialized tool for specific AI development personas.

For those building autonomous agents, low latency for small models (8B) is critical. The ZGX Nano allows an agent to run local "thoughts" or "planning" steps at high speed while maintaining a massive local vector database in memory.

The "Nano" designation and 150 x 150 x 51 mm dimensions make this ideal for on-premises deployment in sensitive environments (legal, medical, or industrial). The 10GbE interface and ConnectX-7 support allow it to act as a micro-inference server for an entire department.

Researchers can use the HP ZGX toolkit to prototype models locally using the same CUDA-based ecosystem they will use in production. This "bridge to the data center" reduces the friction of moving from a local .ipynb file to a multi-node cluster.

While the ZGX Nano is an inference powerhouse, it is not intended for training large models from scratch. It is, however, highly capable for Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA or QLoRA. The 128GB VRAM allows for fine-tuning 70B parameter models using larger batch sizes than any consumer GPU currently allows.

When evaluating the best hardware for local AI agents in 2026, the ZGX Nano faces competition from two main sides:

The Mac Studio offers higher memory bandwidth (up to 800 GB/s), which generally translates to faster tokens per second for LLMs. However, the HP ZGX Nano utilizes the NVIDIA software stack (CUDA, TensorRT, DGX Spark). For developers who need to deploy to NVIDIA-based clouds (AWS P5, Azure NDv5), the ZGX Nano provides a more consistent environment than the Apple Silicon/MLX ecosystem.

AMD-based mini-PCs (like the Bosgame M5) offer a competitive price-to-VRAM ratio. However, the HP ZGX Nano’s enterprise-grade networking (ConnectX-7) and its 250 TOPS INT8 performance give it the edge in professional environments where reliability and integration with NVIDIA's enterprise tools are required.

A dual-RTX 4090 setup provides more raw TFLOPS and 48GB of high-speed VRAM. However, that setup consumes 900W+ and requires a full-tower chassis. The ZGX Nano provides nearly triple the VRAM (128GB) in a chassis that fits in a backpack, drawing only 140W under load. For practitioners who prioritize model size (parameter count) over raw generation speed, the ZGX Nano is the superior choice.