The artificial intelligence war has reached the desktop. On one side, NVIDIA with its DGX Spark ($4,679). On the other, AMD strikes back with the Ryzen AI Halo ($3,999). Both promise the same thing: run large language models (LLMs) locally, without relying on the cloud, without token costs, with server performance in your office.
But which is the better choice for your company? And more importantly: what does this mean in practice for your business?
The Showdown: Technical Specifications
The AMD Ryzen AI Halo has just hit the market based on the Ryzen AI MAX+ 395 processor (codename Strix Halo), while the NVIDIA DGX Spark has been available for a few months. Let's look at the direct comparisons:
| Feature | AMD Ryzen AI Halo | NVIDIA DGX Spark | Advantage |
|---|---|---|---|
| Price | $3,999 | $4,679 | 💰 AMD (-14%) |
| Memory | 128 GB LPDDR5X-8000 | 128 GB LPDDR5X | ⚖️ Tie |
| Storage | 2 TB PCIe Gen4 | 1 TB NVMe | 🏆 AMD (2x) |
| Dedicated NPU | 50 TOPS (XDNA 2) | None | 🏆 AMD |
| GPU | Radeon 8060S (40 RDNA 3.5 cores) | NVIDIA Blackwell (dedicated architecture) | 🏆 NVIDIA (CUDA) |
| NPU | 50 TOPS | None | 🏆 AMD |
| Supported Models | Up to 200B parameters | Up to 200B parameters | ⚖️ Tie |
| AI Software | ROCm 7.2.2 + LM Studio + ComfyUI | CUDA + NGC + TensorRT | 🏆 NVIDIA (ecosystem) |
| Connections | Wi-Fi 7, BT 5.4, Ethernet 10 Gbps, HDMI 2.1b | Wi-Fi 7, Ethernet 10 Gbps | ⚖️ Tie |
| Dimensions | 15 x 15 x 4.3 cm | 15 x 15 x 5 cm | ⚖️ Similar |
LLM Performance: Real Numbers
AMD has released direct performance comparisons in tokens per second (the most important speed measure for LLMs):
- GPT-OSS (120B): AMD is 7% faster than the DGX Spark
- Qwen 3.5 (122B): AMD is 12% faster
- Qwen 3.6 (35B): AMD is 4% faster
- GLM 4.7 (30B): AMD is 14% faster
In the benchmarks released, the Ryzen AI Halo has an advantage in text generation speed. However, for tasks that require a GPU (such as fine-tuning models or inference with large batches), NVIDIA's CUDA ecosystem is still more mature and supported.
What Does This Mean for Businesses?
Having a desktop capable of running LLMs locally is not just a matter of performance — it’s a business model shift. Your company can:
- Eliminate API costs: No more paying per token on ChatGPT, Claude, or Gemini. Run open-source models like Llama, Qwen, or Mistral locally.
- Process sensitive data: Customer information, contracts, and strategies never leave your hardware. GDPR compliance is automatically respected.
- Create content at industrial scale: Generate articles, posts, ads, and scripts 24/7 without usage limits.
- Automate analyses: Marketing reports, sales dashboards, sentiment analysis on feedback — all processed locally.
- Integrate with everyday tools: VS Code, ComfyUI, LM Studio — compatibility with AMD and NVIDIA's dev-ready ecosystem.
The Bottom Line: Local vs Cloud
AMD has done the math, and the results are impressive. Considering 8 hours/day of use with LLMs:
| Scenario | Initial Cost | Monthly Cost | Cost in 3 Years |
|---|---|---|---|
| Ryzen AI Halo | $3,999 | $16 (electricity) | ~$4,500 |
| DGX Spark | $4,679 | $16 (electricity) | ~$5,200 |
| Cloud AI (API) | $0 | $750 | $27,000+ |
The return on investment for either device occurs in less than 6 months. Compared to the cloud, the savings over 3 years exceed $20,000 — money that can be reinvested in other areas of the business.
And the Future?
AMD has already confirmed an updated version with the Ryzen AI MAX+ 495 for the third quarter of 2026, with 192 GB of memory, capable of running models with over 300 billion parameters. NVIDIA, on the other hand, has the advantage of the CUDA ecosystem, which dominates the AI market.
The truth is that both sides win — and so does the market. Having two giants competing to bring high-performance AI to desktops means lower prices, accelerated innovation, and more options for companies of all sizes.
If your company relies on AI to operate, the time to migrate to local processing is now. The giants' battle has only one winner: those who adopt the technology first.
Source: Adapted from WCCFTech — AMD Ryzen AI Halo Review and NVIDIA DGX Spark