Principle:Mlc ai Mlc llm Engine Configuration
| Knowledge Sources | |
|---|---|
| Domains | Deep_Learning, Model_Serving, Systems_Engineering |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
Engine configuration is the practice of parameterizing an inference engine's runtime behavior -- including memory budgets, batching limits, cache strategies, and decoding modes -- to optimize performance for a specific deployment scenario.
Description
Large language model inference engines must balance multiple competing constraints: GPU memory capacity, request concurrency, sequence length limits, prefill throughput, and decoding latency. Rather than hard-coding these trade-offs, a well-designed engine exposes a structured configuration that lets operators tune behavior to their specific deployment context.
The key configuration dimensions fall into several categories:
Deployment Mode Presets: Engines typically offer preset modes that auto-configure many parameters at once:
- Local mode targets single-user or low-concurrency deployments. It uses a small batch size (e.g., 4) and bounds total sequence length to the model's context window, minimizing GPU memory consumption.
- Interactive mode is the extreme case of one concurrent request, suitable for chatbot-style usage where a single conversation occupies the engine.
- Server mode maximizes GPU utilization by automatically inferring the largest feasible batch size and total sequence length within the available memory budget.
Memory Management: The configuration controls KV cache sizing through parameters like gpu_memory_utilization (fraction of GPU memory the engine may consume), max_total_sequence_length (total token capacity across all active sequences), and kv_cache_page_size (granularity of paged KV cache allocation). These parameters directly determine how many concurrent requests the engine can handle and how long individual sequences can grow.
Batching and Sequence Limits: Parameters such as max_num_sequence (maximum batch size), max_single_sequence_length (per-sequence cap), and prefill_chunk_size (maximum tokens processed in a single prefill step) govern the scheduling and execution of request batches.
Advanced Decoding: Configuration includes speculative decoding parameters (speculative_mode, spec_draft_length, spec_tree_width), prefix caching modes (prefix_cache_mode), and prefill strategies (prefill_mode: chunked vs. hybrid).
Parallelism: For multi-GPU deployments, tensor_parallel_shards and pipeline_parallel_stages control how model weights and computation are distributed across devices.
Usage
Engine configuration is applied at engine initialization time and remains fixed for the lifetime of the engine instance. The appropriate configuration depends on the deployment scenario:
- Local/Edge Deployment: Use local or interactive mode to minimize memory footprint. Leave most parameters at defaults and let the engine auto-configure from the model's context window.
- Production Server: Use server mode and tune
gpu_memory_utilizationto control memory headroom. Setmax_num_sequencebased on expected peak concurrency. - High-Throughput Batch Processing: Maximize
max_total_sequence_lengthandprefill_chunk_sizeto keep the GPU saturated. - Latency-Sensitive Applications: Enable speculative decoding to reduce per-token latency. Enable prefix caching to accelerate repeated prompt prefixes.
Theoretical Basis
Memory Budget Planning
The fundamental constraint in LLM serving is GPU memory. The total memory budget is partitioned among:
Total GPU Memory * gpu_memory_utilization =
Model Weights
+ Activation Memory
+ KV Cache Memory
+ Runtime Overhead
The KV cache memory is the primary tunable component. Given a paged KV cache with page size P, the number of available pages determines both the maximum number of concurrent sequences and the maximum total token count:
KV Cache Memory = num_pages * P * 2 * num_layers * head_dim * num_heads * dtype_size
where the factor of 2 accounts for both key and value tensors.
Mode Selection Logic
function configure_engine(mode, model_config, gpu_memory):
if mode == "local":
max_batch_size = 4
max_total_length = model_config.context_window
prefill_chunk_size = model_config.context_window
elif mode == "interactive":
max_batch_size = 1
max_total_length = model_config.context_window
prefill_chunk_size = model_config.context_window
elif mode == "server":
available_kv_memory = gpu_memory * utilization - model_weight_size
max_batch_size = infer_max_batch(available_kv_memory)
max_total_length = infer_max_total_length(available_kv_memory)
prefill_chunk_size = auto_tune(max_total_length)
return EngineConfig(max_batch_size, max_total_length, prefill_chunk_size)
Speculative Decoding
Speculative decoding uses a faster draft model (or mechanism like EAGLE/Medusa) to propose multiple candidate tokens, which the main model verifies in parallel. The configuration parameters spec_draft_length and spec_tree_width control the depth and breadth of speculation. When spec_draft_length is 0, adaptive speculation is used, where the draft length adjusts dynamically based on acceptance rates.