OSC-LLM

A lightweight LLM inference toolkit focused on minimizing inference latency.

Chinese README

Features

• CUDA Graph: Compilation optimizations that reduce inference latency
• PagedAttention: Efficient KV-cache management enabling long-sequence inference
• Continuous batching: Supports dynamic batch inference optimization
• FlashAttention: Memory optimization for long sequences

💡 All technical details are built on osc-transformers, please visit for more details.

Installation

• Install the PyTorch
• Install flash-attn: recommended to use the official prebuilt wheel to avoid build issues
• Install osc-llm

pip install osc-llm --upgrade

Quick Start

Basic Usage

from osc_llm import LLM, SamplingParams

# Initialize the model
llm = LLM("checkpoints/Qwen/Qwen3-0.6B", gpu_memory_utilization=0.5, device="cuda:0")

# Chat
messages = [
    {"role": "user", "content": "Hello! What's your name?"}
]
sampling_params = SamplingParams(temperature=0.5, top_p=0.95, top_k=40)
result = llm.chat(messages=messages, sampling_params=sampling_params, enable_thinking=True, stream=False)
print(result)

# Streaming generation
for token in llm.chat(messages=messages, sampling_params=sampling_params, enable_thinking=True, stream=True):
    print(token, end="", flush=True)

Supported Models

• Qwen3ForCausalLM
• Qwen2ForCausalLM