Local LLMs vs cloud: hardware costs, privacy, and performance trade-offs are real-world, not just theory. A budget local rig in India demonstrates the math: a Dell T7910 with five RTX 3090 GPUs, 96GB VRAM, runs about $4.6k USD. That kind of rig is a reality for hobbyists and small teams, not just glossy hype—but you’ll need cooling, power, and know-how to keep it humming. [4]
Budget realities you can actually buy today: - Dell T7910 with five RTX 3090 GPUs, 96GB VRAM; total around $4.6k. [4] - Networking matters: 10Gbps links and multi-node setups can push bigger models, but add cost and complexity. [4]
Bubble dynamics and what it could mean for locals: If the AI hardware bubble pops, there could be a glut of A100 and H100 GPUs, pushing prices down while power costs stay similar or drop as supply shifts. [1]
Privacy and home-built coaching: offline on-device is more doable than you might think. A Mac Mini M3 Pro can run offline LLMs with llama.cpp; “Apple Silicon is a first-class citizen” for these setups. [2]
Local-model variety and tool-calling realities: In practice, posters hype models like Gemma3-12B-QAT and Qwen3; others praise gpt-oss and Hermes 4. Many run on M4 Pro with 64GB. Tool calling reliability varies, so match models to your workflow. [3]
Bottom line: choose local inference when privacy and offline access matter and you can budget for hardware; cloud-first makes sense when you need scaling and the latest tooling. [4][1][2][3]
References
If the bubble really pops how can that affect local AI models?
Discusses AI bubble's effect on local LLMs, costs, open-source models, training vs inference, and market dynamics future pricing and availability
View sourceTotal noob here who wants to run a local LLM to build my own coach and therapist chatbot
Begins with local LLMs; compares tools (llama.cpp, Koboldcpp, Open WebUI) and cloud options; warns therapy risks privacy, context, cost concerns.
View sourceDrop your underrated models you run LOCALLY
Community debates locally runnable LLMs, compares Gemma, Qwen3, GPT-Oss; tool-calling, performance, hardware, and use cases.
View sourceWhen you have little money but want to run big models
India hardware limits; builds 96GB VRAM with 5x3090; compares vLLM, llama.cpp; discusses MoE, 230B/120B models, 10GbE networking, cost heat noise
View source