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llama.cpp batch size tuning

Q: llama.cpp batch size tuning

To tune batch size in llama.cpp, adjust the n_batch parameter when creating the Llama instance or calling the inference method. Increasing n_batch improves throughput by processing multiple prompts simultaneously but requires more VRAM; find the optimal size by testing your hardware limits.

Quick answer

To tune batch size in llama.cpp, adjust the n_batch parameter when creating the Llama instance or calling the inference method. Increasing n_batch improves throughput by processing multiple prompts simultaneously but requires more VRAM; find the optimal size by testing your hardware limits.

PREREQUISITES

Python 3.8+
pip install llama-cpp-python
llama.cpp GGUF model file downloaded

Setup

Install the llama-cpp-python package and download a compatible GGUF model file for llama.cpp. Ensure your environment has sufficient VRAM to handle larger batch sizes.

bash

pip install llama-cpp-python

output

Collecting llama-cpp-python
  Downloading llama_cpp_python-0.1.0-cp38-cp38-manylinux1_x86_64.whl (10 MB)
Installing collected packages: llama-cpp-python
Successfully installed llama-cpp-python-0.1.0

Step by step

Use the n_batch parameter to set batch size when initializing the Llama model or invoking create_chat_completion. Larger batch sizes increase throughput but consume more memory.

python

from llama_cpp import Llama
import os

# Path to your GGUF model file
model_path = "./models/llama-3.1-8b.Q4_K_M.gguf"

# Initialize Llama with batch size 8
llm = Llama(model_path=model_path, n_ctx=2048, n_batch=8)

# Prepare multiple prompts for batch inference
messages = [
    {"role": "user", "content": "Hello, how are you?"},
    {"role": "user", "content": "Explain batch size tuning."},
    {"role": "user", "content": "What is llama.cpp?"}
]

# Create chat completions for all messages in one batch
response = llm.create_chat_completion(messages=messages)

# Print each response
for i, choice in enumerate(response["choices"]):
    print(f"Response {i+1}: {choice["message"]["content"]}\n")

output

Response 1: I'm doing well, thank you! How can I assist you today?

Response 2: Batch size tuning in llama.cpp involves adjusting the n_batch parameter to optimize throughput and memory usage.

Response 3: llama.cpp is a lightweight C++ implementation for running LLaMA models efficiently on local hardware.

Common variations

You can tune n_batch dynamically depending on your hardware. For GPU with limited VRAM, start with smaller batch sizes like 1 or 2. For CPU or high VRAM GPUs, increase up to 16 or 32. Also, adjust n_ctx for context length. Use create_chat_completion for chat-style prompts or llm() for single prompt generation.

python

from llama_cpp import Llama

# Smaller batch size for low VRAM
llm_small_batch = Llama(model_path=model_path, n_ctx=2048, n_batch=2)

# Larger batch size for high VRAM
llm_large_batch = Llama(model_path=model_path, n_ctx=2048, n_batch=16)

# Single prompt generation
output = llm_small_batch("Explain batch size in llama.cpp", max_tokens=50)
print(output["choices"][0]["text"])

output

Batch size in llama.cpp controls how many tokens are processed simultaneously, improving throughput but increasing memory usage.

Troubleshooting

If you get CUDA out of memory errors, reduce n_batch or n_ctx.
If inference is slow, try increasing n_batch to better utilize GPU parallelism.
Ensure your GGUF model file matches the llama-cpp-python version.

✅

Key Takeaways

Set n_batch in Llama to control batch size for inference.
Larger batch sizes improve throughput but require more VRAM.
Test batch sizes incrementally to find the optimal balance for your hardware.

Verified 2026-04 · llama-3.1-8b.Q4_K_M.gguf

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