ASUS Ascent GX10 - 2TB

The ASUS Ascent GX10 - 2TB is designed for large language models (LLMs) that exceed the capacity of standard 16GB or 24GB consumer cards. Its 128GB of VRAM allows for the local execution of models with up to 200B parameters.

• Llama 3.1 70B: Can be run entirely in memory at FP16 or BF16 with room to spare for massive KV caches (long context). At 4-bit (Q4_K_M) or 8-bit (Q8_0) quantization, the performance is exceptionally fluid.
• DeepSeek-V3 / DeepSeek-R1: These MoE (Mixture of Experts) models are ideal for the GX10. While the full 671B models will require heavy quantization or offloading, the GX10 can comfortably host the 130B-180B parameter variants or heavily quantized versions of the larger models.
• Qwen 2.5 72B & Mixtral 8x22B: These models sit in the "sweet spot" for this hardware. Users can expect high-speed inference even with long-context windows (up to 128k tokens), as the 128GB capacity prevents the system from spilling over into slower system RAM.
• Vision & Multimodal: The GX10 handles models like Llava-v1.6 or CogVLM with ease, providing enough headroom to process high-resolution image embeddings alongside the text LLM.

For a 70B parameter model at 4-bit quantization, the ASUS Ascent GX10 - 2TB AI inference performance typically yields 15–25 tokens per second, depending on the specific optimization (TensorRT-LLM vs. llama.cpp). This is well above the reading speed of a human and sufficient for real-time agentic tool-use.

For developers building agentic workflows (AutoGPT, CrewAI, or LangGraph), the GX10 serves as a reliable "brain." Local deployment ensures that sensitive API keys, proprietary data, and internal tool-use logs never leave the local network. The 128GB VRAM is particularly useful when running multiple specialized models simultaneously (e.g., a primary reasoning model alongside a smaller embedding model and a vision model).

The rugged, compact chassis and 10G networking make the GX10 a candidate for Edge AI. It can be deployed in retail, manufacturing, or healthcare settings to process telemetry or video feeds locally. The ConnectX-7 SmartNIC ensures that data ingestion doesn't bottleneck the Blackwell GPU's processing power.

Researchers can use the GX10 for fine-tuning smaller models (7B to 30B) using techniques like QLoRA. While not a replacement for an H100 cluster for pre-training, it is an excellent "dev-box" for prototyping models that will eventually be deployed to the cloud.

When evaluating the best hardware for local AI agents 2026, the GX10 is often compared to the following:

• ASUS Ascent GX10 - 2TB vs. Apple Mac Studio (M2/M3 Ultra, 128GB): The Mac Studio offers higher memory bandwidth (up to 800 GB/s), which can lead to faster token generation. However, the GX10 utilizes the NVIDIA Blackwell architecture, providing native CUDA support. For developers relying on TensorRT-LLM, Triton, or specific NVIDIA-only kernels, the GX10 is the superior choice.
• ASUS Ascent GX10 - 2TB vs. Multi-GPU PC (e.g., 2x RTX 5090): A dual-GPU PC provides more raw TFLOPS and higher bandwidth but at the cost of significantly higher power consumption (800W+), heat, and physical size. The GX10 offers a "turn-key" 128GB VRAM solution in a 1.5kg box that consumes only 140W, making it much more suitable for office or edge environments.

The ASUS Ascent GX10 - 2TB is the definitive choice for practitioners who need a high-VRAM, CUDA-compatible workstation that prioritizes efficiency and a small footprint over raw, power-hungry desktop performance. Its 2TB PCIe 4.0 storage provides ample space for a library of the latest GGUF or EXL2 model files, making it a versatile hub for local AI development.