What Happened

A user on r/LocalLLaMA with an Intel Core i9-265KF, 64GB RAM, and an RTX 5070 (12GB VRAM) is transitioning from Gemini to locally-run AI models. Their use cases span chatbot interaction, creative novel writing, and music composition — three distinct workloads requiring different model families and tooling choices.

The RTX 5070 with 12GB VRAM is a capable card for local inference. At 12GB, you can comfortably run quantized models up to 13B parameters (Q4_K_M quant), and with careful offloading to the 64GB system RAM, even 30B-class models become usable at reduced speed. The Blackwell architecture also brings improved tensor core throughput over the RTX 4070, making it a solid mid-range inference card in 2025.

Technical Deep Dive

Text and Chat Models

For chatbots and novel writing, the primary runtime choices are Ollama and LM Studio. Ollama is CLI-first and suits developers; LM Studio offers a GUI ideal for writers unfamiliar with terminals.

Recommended models for 12GB VRAM:

  • Mistral-7B-Instruct Q5_K_M (~5GB VRAM) — fast, good for chat
  • LLaMA 3.1 8B Instruct Q5_K_M (~6GB VRAM) — strong instruction following
  • Qwen2.5-14B Q4_K_M (~9GB VRAM) — excellent for creative writing, fits with minor layer offload
  • Mistral-Small-3.1 22B Q3_K_M (~12GB VRAM) — pushes the limit, best quality for prose

Install Ollama and pull a model with:

curl -fsSL https://ollama.com/install.sh | sh ollama pull qwen2.5:14b

For novel writing specifically, models fine-tuned on creative fiction like Mistral-Nemo-Gutenberg or Llama-3-Lumimaid outperform base instruct models. These are available on HuggingFace in GGUF format for use with llama.cpp or LM Studio.

Music Composition

Music generation is a separate pipeline from text LLMs. The leading local options are:

  • MusicGen by Meta — runs via the audiocraft Python library, the medium model (1.5B params) fits in 8GB VRAM
  • Stable Audio Open by Stability AI — requires ~6GB VRAM, generates 44kHz stereo audio up to 47 seconds
  • Suno v3 (API only) — not local, but mentioned for comparison

To run MusicGen locally:

pip install audiocraft python -c "from audiocraft.models import MusicGen; m = MusicGen.get_pretrained('facebook/musicgen-medium'); m.set_generation_params(duration=15)"

Unlike text LLMs where GGUF quantization is standard, audio models typically run in fp16 or bf16, so VRAM headroom matters more. With 12GB, the MusicGen-medium and Stable Audio Open models both run without offloading.

Inference Backend Comparison

Unlike vLLM (which targets server deployments with PagedAttention), llama.cpp is optimized for consumer GPUs and supports mixed CPU/GPU offloading via --n-gpu-layers. Ollama wraps llama.cpp. For a 14B model that slightly exceeds VRAM, set OLLAMA_NUM_GPU_LAYERS=35 to offload 35 layers to GPU and the rest to RAM.

Who Should Care

This setup is relevant for three distinct user profiles. Writers wanting privacy-first drafting without cloud API costs will benefit most — local models mean no data leaves the machine, and inference costs zero per token after setup. Developers building chatbot prototypes can use Ollama's REST API (http://localhost:11434) as a drop-in replacement for OpenAI's API format. Musicians and audio hobbyists exploring AI-assisted composition can run MusicGen for stem generation or ambient texture creation without a subscription. Anyone currently paying for Gemini Advanced ($20/month) or Claude Pro ($20/month) can offset hardware costs within months if usage is high.

What To Do This Week

Start with these steps:

  • Install Ollama: https://ollama.com/download
  • Pull and test a chat model: ollama pull llama3.1:8b && ollama run llama3.1:8b
  • For creative writing, download LM Studio from https://lmstudio.ai and search for Qwen2.5-14B-Instruct-GGUF
  • For music, clone AudioCraft: git clone https://github.com/facebookresearch/audiocraft && pip install -e .
  • Browse GGUF models at https://huggingface.co/bartowski — a reliable quantization source