Running Llama 3.1 70B on 2x4090s with vLLM. Memory is a pain but works decent for most stuff.

Tbh for coding I just use the smaller ones like CodeQwen 7B. way faster and good enough for autocomplete. Only fire up the big model when I actually need it to think.

The annoying part is keeping everything updated, new model drops every week and half don't work with whatever you're already running.

2x 3090's running Ollama and VLLM... Ollama for most stuff and VLLM for the few models that I need to test that don't run on Ollama. Open Web UI as my primary interface. I just moved to Devstral for coding using the Continue plugin in VSCode. I use Qwen 3 32b for creative stuff and Flux Dev for images. Gemma 3 27b for most everything else (slightly less smart than Qwen, but its faster). Mixed Bread for embeddings (though apparently NV-Embed-v2 is better?). Pydantic as my main utility library. This is all for personal stuff. My stack at work is completely different and driven more by our Legal teams than technical decisions.

Ollama + mac mini 24gb (inference)

Ollama + M3 Max 36GB Mac. Usually with Python + SQLite3.

The models vary depending on the task. DeepSeek distilled has been a favorite for the past several months.

I use various smaller (~3B) models for simpler tasks.

recurse.chat + M2 max Mac

Ollama on a M1 MacBook pro but will be moving to a Nvidia GPU setup.

Great question. We're building Markhub, an AI-powered collaboration OS, and our stack is a hybrid one, because we believe the "best" model depends entirely on the task.

1. For Heavy, Complex Tasks (Summarization, Code Gen, Creative Work): We don't self-host. The performance of top-tier models is still unmatched. We use Gemini-based models via Google's Vertex AI. The reliability and raw power for complex reasoning are worth the API cost for these critical features.

2. For Fast, Specific, Private Tasks (Our Self-Hosted Stack): For smaller, high-frequency tasks like classifying feedback types or extracting specific keywords from a conversation, we use a self-hosted stack for speed and cost-efficiency.

Models: We use fine-tuned versions of smaller, open-source models like Llama 3 8B or Mistral 7B. They are incredibly fast and cost-effective for specific, repetitive tasks. Runtime/Orchestration: We use LangChain for chaining prompts and managing workflows. For serving the model, we're using a simple FastAPI server running in a Docker container. Hardware: We run this on a dedicated GPU instance (like an A10G on AWS/GCP) for inference. The cost is predictable and much lower than using a large model for every small task. My takeaway: The "go-to stack" in 2025 isn't one-size-fits-all. It's a pragmatic, hybrid approach using the bestin class cloud APIs for the heavy lifting, and deploying fast, fine-tuned open-source models for everything else.