What's the best way to persist a loaded LLaMA model across multiple queries in a long-running process?

[YuXing404]

Hi everyone,

I'm working on integrating llama.cpp into a desktop application where the model should stay loaded to handle multiple user queries over time — without reloading the model from disk for each call (since loading takes time and resources).

What’s the recommended way to keep the model in memory across multiple inputs in a long-running process?

• Is there a specific mode or function that helps with this?
• Should I be using the C API directly, or can this be done cleanly via the Python bindings?
• How do I manage sessions or state, especially if I want to maintain a short chat context between inputs?

Thanks in advance! Would love to hear best practices from others who’ve built interactive apps with llama.cpp.

[officiallyutso]

To keep the model loaded and avoid reloading it every time, you should definitely use a long-running process where the model context stays in memory.

Best Practices:

Use the Python bindings (llama-cpp-python) if you're working in Python. They wrap the C API cleanly and allow persistent contexts.

from llama_cpp import Llama

llm = Llama(model_path="path/to/your/model.gguf", n_ctx=4096, n_threads=8)

The llm object keeps the model in memory. You can call llm(prompt) multiple times without reloading.

Maintain a rolling context manually: Concatenate the prompt + past few interactions to simulate a short-term memory/chat history. Keep track of the token limit (n_ctx) to avoid context overflow.

If you're building a multi-user app, you may want to:

• Keep separate contexts per session (use separate Llama instances or manage token histories yourself).
• Use cache=False when calling the model if you want more control.

---

If you're doing performance-critical work or need finer control (e.g., streaming, quantization tweaks), using the C API directly might make sense — but for most interactive apps, the Python wrapper is more than enough.

Also worth checking out:

llama-cpp-python docs

ggml.c and C API in llama.cpp

[YuXing404]

Okay sure, will look into this.