| title: README | |
| emoji: 📈 | |
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| colorTo: green | |
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| # Enverge.ai | |
| **[Spark](https://spark.enverge.ai)** lets you rent NVIDIA DGX Spark devices — each with 128GB of unified memory — to train, fine-tune, and run large AI models. Zero setup, no infrastructure to manage: launch a DGX Spark in seconds and start building. | |
| 📄 See the [official NVIDIA DGX Spark spec sheet](https://www.nvidia.com/en-us/products/workstations/dgx-spark/) for full hardware details. | |
| 👉 **[Rent a DGX Spark at spark.enverge.ai](https://spark.enverge.ai)** | |
| ## Recent blog posts | |
| From [spark.enverge.ai/blog](https://spark.enverge.ai/blog): | |
| - **[How fast is the DGX Spark, really? Prefill vs. decode, and the 273 GB/s wall](https://spark.enverge.ai/blog/dgx-spark-prefill-vs-decode)** — Why DGX Spark decode tops out around 3 tok/s on dense 70B models — and why prefill, MoE models, and batched serving tell a very different story. | |
| - **[The Cheapest Way to Run a 70B Model Locally in 2026](https://spark.enverge.ai/blog/cheapest-way-to-run-a-70b-model-locally)** — The cheapest way to run a 70B model locally, compared: DGX Spark, GB10 clones, Mac Studio, RTX 5090, and cloud rental — with specs, prices, and break-even math. | |
| - **[How (and Why) to Quantize LLMs on NVIDIA DGX Spark](https://spark.enverge.ai/blog/quantize-llms-on-dgx-spark)** — Quantize LLMs on NVIDIA DGX Spark using NVFP4, FP8, and GGUF. Step-by-step calibration, evaluation, and tradeoffs for Llama 3.1 70B — under $2 of compute. | |
| - **[Running Research Experiments on DGX Spark: Why Smaller VRAM Can Be Cheaper for Iterative AI](https://spark.enverge.ai/blog/running-research-experiments-dgx-spark-vram-vs-cost)** — Why H100s are overkill for iterative research — and how DGX Spark at $0.65/hr lets you run 5–8x more experiment variants for the same budget. | |
| - **[Run AI Agents Locally: OpenClaw, Local LLMs, and Why the Cloud Should Be Yours](https://spark.enverge.ai/blog/run-ai-agents-locally-openclaw-local-llm)** — Why building AI agents on API calls is expensive and insecure — and how running OpenClaw with local LLMs on Spark Cloud keeps your data private while cutting costs by half. | |