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Model performance means optimizing every layer of your model serving infrastructure to balance four goals:
  1. Latency: How quickly does each user get output from the model?
  2. Throughput: How many requests can the deployment handle at once?
  3. Cost: How much does a standardized unit of work cost?
  4. Quality: Does your model consistently deliver high-quality output after optimization?

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Performance engines

Baseten’s performance-optimized engines deliver the best possible inference speed and efficiency:

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Engine-Builder-LLM - Dense Models

  • Best for: Llama, Mistral, Qwen, and other causal language models
  • Features: TensorRT-LLM optimization, lookahead decoding, quantization
  • Performance: Lowest latency and highest throughput for dense models

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BIS-LLM - MoE Models

  • Best for: DeepSeek, Mixtral, and other mixture-of-experts models
  • Features: V2 inference stack, expert routing, structured outputs
  • Performance: Optimized for large-scale MoE inference

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BEI - Embedding Models

  • Best for: Sentence transformers, rerankers, classification models
  • Features: OpenAI-compatible, high-performance embeddings
  • Performance: Fastest embedding inference with optimized batching

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Performance concepts

Detailed performance optimization guides are now organized in the Performance Concepts section:

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⚡ Quick performance wins

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Quantization

Reduce memory usage and improve speed with post-training quantization: 
trt_llm:
  build:
    quantization_type: fp8  # 50% memory reduction

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Lookahead Decoding

Accelerate inference for predictable content (code, JSON): 
trt_llm:
  build:
    speculator:
      speculative_decoding_mode: LOOKAHEAD_DECODING
      windows_size: 5

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Performance Client

Maximize client-side throughput with Rust-based client: 
pip install baseten-performance-client

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🔧 Where to start

  1. Choose your engine: Engines overview
  2. Configure your model: Engine-specific configuration guides
  3. Optimize performance: Performance concepts
  4. Deploy and monitor: Use performance client for maximum throughput
💡 Tip: Start with the default engine configuration, then apply quantization and other optimizations based on your specific performance requirements.