Principle:InternLM Lmdeploy Engine Configuration
| Knowledge Sources | |
|---|---|
| Domains | LLM_Inference, Configuration |
| Last Updated | 2026-02-07 15:00 GMT |
Overview
A configuration pattern that parameterizes inference engine behavior including precision, parallelism, memory allocation, and batching strategy for LLM serving.
Description
Engine Configuration is the principle of encapsulating all tunable parameters of an inference engine into a single, validated configuration object. In the context of LLM deployment, this includes critical decisions about:
- Data type precision (float16, bfloat16, auto) affecting memory usage and numerical accuracy
- Tensor parallelism (tp) for distributing model across multiple GPUs
- KV cache management (cache_max_entry_count) controlling GPU memory allocation for key-value caches
- Batching parameters (max_batch_size, session_len) governing concurrent request handling
- Model format (hf, awq, gptq) specifying weight quantization format
The principle separates engine selection from engine configuration, allowing the same model to be served with different performance characteristics by varying only the configuration object.
Usage
Use this principle when initializing an inference pipeline and you need to control hardware resource allocation, precision, or parallelism. The configuration object is created before pipeline initialization and passed as a parameter. Choose between TurboMind (C++/CUDA, highest performance) and PyTorch (broader model support, multi-platform) backends based on model compatibility.
Theoretical Basis
Engine configuration follows the Builder Pattern where a configuration object accumulates validated parameters before being consumed by the engine constructor.
The key tradeoffs are:
- Precision vs. Memory: Lower precision (4-bit, 8-bit) reduces memory but may impact quality
- Parallelism vs. Throughput: Higher tensor parallelism enables larger models but adds communication overhead
- Cache Size vs. Batch Size: More KV cache memory allows longer contexts but reduces batch capacity
Pseudo-code:
# Abstract algorithm
config = EngineConfig(
dtype=select_precision(model_requirements, hardware),
tp=count_available_gpus(),
cache_fraction=estimate_kv_cache_needs(model_size, context_length),
max_batch_size=optimize_for_throughput(memory_budget)
)
pipeline = create_pipeline(model, config)