V4.1 Flash 本地部署七种配置：从双 DGX Spark 256GB 到四机 TP4 512GB

FHILY👑 @Oluwaphilemon1原推发布于 2026.10.23

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Qwen3.8-27B and DeepSeek-V4.1-Flash have powered seven local AI agent experiments, ranging from a 16GB consumer GPU to systems with 512GB of memory. The goal was to let AI agents build entire worlds that you can explore directly in your browser, using open-weight models and locally configured inference engines. No cloud API required for the experiments described. The seven hardware configurations 1. RTX 5060 Ti, 16GB VRAM Qwen3.8-27B running with MiaAI_lab’s inference kit. This configuration explores what a consumer GPU can accomplish with a capable local model. 2. RTX 5090, 32GB VRAM The same Qwen3.8-27B model and inference kit on a higher-memory GPU, providing a useful hardware comparison for local agent workflows. 3. One DGX Spark, 128GB memory Qwen3.8-Flash-Next running on TensorFold with MiaAI_lab’s recipe. This configuration brings a larger model architecture into the experiment using a system with substantial unified memory. 4. Two DGX Sparks, 256GB memory DeepSeek-V4.1-Flash running on TensorFold across a two-system configuration. 5. Mac with 256GB unified memory Qwen3.8-Flash-Next running through oMLX, demonstrating another route for experimenting with large open-weight models on Apple Silicon. 6. Mac with 256GB unified memory DeepSeek-V4.1-Flash running through oMLX, allowing the same general hardware platform to be used with a different model family. 7. Four DGX Sparks, 512GB memory DeepSeek-V4.1-Flash running with Tensor Parallelism 4 (TP4), distributing the model across four systems. Why this experiment is interesting These setups cover very different hardware budgets and inference strategies. At one end, a 16GB GPU is being used to run Qwen3.8-27B for an agent-based world-building task. At the other, four DGX Sparks provide a combined 512GB memory configuration for DeepSeek-V4.1-Flash. The model architecture, available memory, inference engine, and parallelization strategy all influence what can run and how efficiently it runs. A larger memory budget can make larger models or less aggressive quantization practical. Meanwhile, optimized inference software helps developers get more from the hardware they already have. The experiment also highlights a growing use for coding agents: generating interactive software rather than simply returning code snippets. An agent must translate a prompt into a working environment, produce the necessary code, and create something that can actually be explored in a browser. What about the results? The worlds are reportedly playable directly in the browser, making it possible to inspect the output of each configuration rather than judging the experiment from benchmark numbers alone. One caveat: these are different models running on different hardware with different inference stacks. The seven setups demonstrate the range of possible configurations, but they are not a controlled benchmark of which GPU, model, or inference engine is fastest. The exciting part is the accessibility of the workflow. Local AI agents can now be used to build interactive experiences, with configurations ranging from consumer hardware to multi-system setups. Models from Alibaba’s Qwen team and DeepSeek, combined with community-built inference recipes, are opening up more ways to experiment with AI-generated software without depending entirely on hosted APIs. https://x.com/niklaslenz_ai/status/2112666535932117104/video/1