LLM Inference Optimization Made Easy: BentoML llm-optimizer Revolutionizes Model Deployment

Deploying large language models (LLMs) in production environments presents a significant challenge: how to find the optimal configuration for latency, throughput, and cost without relying on tedious manual trial and error. BentoML’s recently released llm-optimizer addresses this exact problem, providing a systematic approach to LLM performance tuning.

Why Is LLM Inference Tuning So Challenging?

Optimizing LLM inference requires balancing multiple dynamic parameters—batch size, framework selection (such as vLLM or SGLang), tensor parallelism strategies, sequence lengths, and hardware utilization. Each factor influences performance differently, making it extremely difficult to find the perfect combination of speed, efficiency, and cost.

Most teams still rely on repetitive manual testing, an approach that is not only slow and inconsistent but often inconclusive. For self-hosted deployments, configuration mistakes come at a high price: poorly tuned setups lead to increased response latency and wasted GPU resources.

Reflection: From practical engineering experience, performance tuning has often been viewed as a “black art,” heavily dependent on expert knowledge and repeated experimentation. This situation significantly limits the ability of small and medium-sized teams to deploy LLMs efficiently.

What Makes llm-optimizer Different?

llm-optimizer provides a structured methodology for exploring the multidimensional configuration space of LLM performance. It eliminates guesswork through automated parameter scanning and constraint-driven optimization.

Core capabilities include:

• Running standardized tests across multiple inference frameworks like vLLM and SGLang
• Supporting constraint-driven tuning, such as filtering for configurations with first-token latency below 200ms
• Automating parameter sweeps to identify optimal settings
• Visualizing trade-offs between latency, throughput, and GPU utilization through interactive dashboards

The framework is completely open source and available on GitHub.

Getting Started with llm-optimizer

Installation

You can quickly install llm-optimizer using pip:

pip install -e .

For development purposes, install with additional dependencies:

pip install -e .[dev]

Performance Estimation: Rapid Exploration of Optimal Configurations

The fastest way to get started with llm-optimizer is through its performance estimation feature. This functionality predicts latency, throughput, and concurrency limits without running full benchmarks, significantly reducing initial research time:

llm-optimizer estimate \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --gpu A100 \
  --input-len 1024 \
  --output-len 512

For models requiring authorization, request access on Hugging Face and set the environment variable first:

export HF_TOKEN=<your access token>

Additional usage examples:

# Specify GPU type and quantity
llm-optimizer estimate \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --input-len 2048 \
  --output-len 1024 \
  --gpu H100 \
  --num-gpus 8

# Add performance constraints and generate recommended commands
llm-optimizer estimate \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --input-len 1024 \
  --output-len 512 \
  --gpu H100 \
  --num-gpus 4 \
  --constraints "ttft:mean<300ms;itl:p95<50ms"

You can also use interactive mode for step-by-step configuration:

llm-optimizer estimate --interactive

Running Your First Benchmark Test

llm-optimizer currently supports running benchmark tests with various configurations based on SGLang and vLLM. Here’s an example using SGLang:

# Test multiple TP/DP combinations
llm-optimizer \
  --framework sglang \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --server-args "tp_size*dp_size=[(1,4),(2,2),(4,1)];chunked_prefill_size=[2048,4096,8192]" \
  --client-args "max_concurrency=[50,100,200];num_prompts=1000" \
  --output-json sglang_results.json

This command automatically performs the following steps:

• Tests 3 TP/DP combinations × 3 prefill sizes, totaling 9 server configurations
• Tests each server configuration with 3 client concurrency settings
• Runs 27 different benchmark tests in total, saving results in sglang_results.json

Additional execution examples:

# vLLM batch size tuning
llm-optimizer \
  --framework vllm \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --server-args "tensor_parallel_size*data_parallel_size=[(1,2),(2,1)];max_num_batched_tokens=[4096,8192,16384]" \
  --client-args "max_concurrency=[32,64,128];num_prompts=1000;dataset_name=sharegpt" \
  --output-json vllm_results.json

# Complex parameter grid for throughput optimization
llm-optimizer \
  --framework sglang \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --server-args "tp_size*dp_size=[(1,8),(2,4),(4,2)];schedule_conservativeness=[0.3,0.6,1.0];chunked_prefill_size=range(2048,8193,2048)" \
  --client-args "max_concurrency=range(50,201,50);request_rate=[10,20,50]" \
  --gpus 8 \
  --output-json complex_benchmark.json

Practical Application Scenario: Suppose your team needs to maximize throughput on an 8-GPU cluster while ensuring first-token latency remains below 200 milliseconds. Through the parameter grid scanning above, you can quickly identify 3-4 candidate configurations without manually testing every possibility.

Applying Performance Constraints

Not all benchmark results meet practical requirements. llm-optimizer allows you to apply performance constraints directly, returning only configurations that meet your service level objectives (SLOs).

# Latency-optimized configuration
llm-optimizer \
  --framework vllm \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --server-args "tensor_parallel_size*data_parallel_size=[(1,2),(2,1)];max_num_seqs=[16,32,64]" \
  --client-args "max_concurrency=[8,16,32];num_prompts=500" \
  --constraints "ttft<200ms;itl:p99<10ms" \
  --output-json latency_optimized.json

Currently supported constraint syntax includes:

# First-token latency constraints
--constraints "ttft<300ms"                    # Mean first-token latency below 300ms
--constraints "ttft:median<200ms"             # Median below 200ms
--constraints "ttft:p95<500ms"                # 95th percentile below 500ms

# Inter-token latency constraints
--constraints "itl:mean<20ms"                 # Mean ITL below 20ms
--constraints "itl:p99<50ms"                  # 99th percentile below 50ms

# End-to-end latency constraints
--constraints "e2e_latency:p95<2s"            # 95th percentile below 2 seconds

# Combined constraints
--constraints "ttft:median<300ms;itl:p95<10ms;e2e_latency:p95<2s"

Reflection: The constraint-driven optimization approach effectively translates business requirements directly into technical parameters, a process that previously required senior engineers with extensive experience. llm-optimizer makes this process repeatable and standardized, significantly lowering the barrier to performance tuning.

Visualizing Benchmark Results

llm-optimizer saves benchmark results in JSON format, containing key metrics such as TTFT (time to first token), ITL (inter-token latency), and concurrency capabilities. To make this data more intuitive, the tool provides interactive visualization capabilities.

# Visualize results with Pareto frontier analysis
llm-optimizer visualize --data-file results.json --port 8080

# Compare multiple result files
llm-optimizer visualize --data-file "sglang_results.json,vllm_results.json" --port 8080

Access http://localhost:8080/pareto_llm_dashboard.html in your browser to:

• Compare results from multiple runs side by side
• Explore trade-offs between different settings (e.g., latency vs. throughput)
• Identify optimal performance configurations for specific workloads

Note: This feature is still experimental, and the BentoML team continues to improve it. You can also use the LLM Performance Explorer to view precomputed benchmark data.

Using Custom Service Commands

While llm-optimizer defaults to managing service startup for supported frameworks, you can also provide custom service commands for greater control.

# Custom SGLang service
llm-optimizer \
  --server-cmd "python3 -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --host 0.0.0.0 --port 30000" \
  --client-args "max_concurrency=[25,50,100];num_prompts=1000" \
  --host 0.0.0.0 \
  --port 30000

# Custom vLLM service with specific GPU allocation
llm-optimizer \
  --server-cmd "vllm serve meta-llama/Llama-3.1-8B-Instruct --tensor-parallel-size 4" \
  --client-args "max_concurrency=[64,128];num_prompts=2000" \
  --port 8000

Deep Tuning of Inference Parameters

llm-optimizer exposes various parameters on both the server and client sides, allowing you to experiment with different settings and evaluate their impact on performance. The tool natively supports parameters for both vLLM and SGLang frameworks.

SGLang Parameters

• tp_size*dp_size: Tensor/data parallelism combinations
• chunked_prefill_size: Prefill chunk size (affects throughput)
• schedule_conservativeness: Request scheduling aggressiveness
• schedule_policy: Scheduling policy (FCFS, priority)

vLLM Parameters

• tensor_parallel_size: Tensor parallelism degree
• max_num_batched_tokens: Maximum batch tokens
• max_num_seqs: Maximum concurrent sequences

Client Parameters

• max_concurrency: Maximum concurrent requests
• num_prompts: Total requests to send
• dataset_name: Request generation dataset (sharegpt, random)
• random_input/random_output: Random sequence lengths

Supported GPU Types

H100, H200, A100, L20, L40, B100, and B200, with accurate TFLOPS specification data built into the tool.

Exploring Results Without Local Execution

In addition to the optimization tool itself, BentoML has released the browser-based LLM Performance Explorer, powered by llm-optimizer. This interface provides precomputed benchmark data for popular open-source models, enabling users to:

• Compare frameworks and configurations side by side
• Filter by latency, throughput, or resource thresholds
• Explore trade-offs interactively without configuring hardware

Practical Application Scenario: When you need to quickly evaluate the feasibility of different model and hardware combinations, you can use the online tool for preliminary screening first, then conduct detailed local testing on the most promising configurations, significantly improving decision-making efficiency.

The Impact of llm-optimizer on LLM Deployment Practices

As LLM applications become increasingly widespread, maximizing deployment efficiency depends more and more on fine-tuning inference parameters. llm-optimizer significantly reduces the complexity of this process, enabling small and medium-sized teams to achieve optimization results that previously required large-scale infrastructure and deep expertise.

By providing standardized benchmarks and reproducible results, the framework adds much-needed transparency to the LLM field, making comparisons across models and frameworks more consistent and filling a long-standing gap in the community.

Ultimately, BentoML’s llm-optimizer brings a constraint-driven, benchmark-first methodology to self-hosted LLM optimization, replacing ad hoc trial and error with a systematic, repeatable workflow.

Personal Insight: In my view, llm-optimizer represents a significant advancement in ML engineering maturity. It transforms performance optimization from an “art” into a more systematic “science,” not only improving efficiency but also making the optimization process more repeatable and verifiable, which is crucial for production environment reliability and maintainability.

Practical Summary and Operation Checklist

Quick Start Guide

1. Install llm-optimizer using pip install -e .
2. Use llm-optimizer estimate for quick performance estimation
3. Select target framework (vLLM or SGLang) to run benchmarks
4. Apply business-relevant performance constraints
5. Use visualization tools to analyze results and select optimal configurations

One-Page Summary

• Problem: LLM inference tuning is complex and time-consuming, relying on manual trial and error
• Solution: llm-optimizer provides automated, systematic benchmarking and optimization
• Core Features: Performance estimation, parameter scanning, constraint-driven optimization, result visualization
• Applicable Scenarios: Self-hosted LLM deployment, performance-cost trade-off analysis, framework comparison
• Supported Environments: Local development machines, GPU clusters, cloud environments
• Output Deliverables: Optimal configuration parameters, performance metrics, visualization reports

Frequently Asked Questions (FAQ)

Which large language models does llm-optimizer support?

llm-optimizer supports all open-source large language models, including Llama, Mistral, Phi series, and both original versions and fine-tuned variants.

Is it necessary to actually run models for optimization?

No. llm-optimizer provides performance estimation functionality that can predict performance metrics without running full benchmark tests.

Does the tool support multi-GPU configurations?

Yes, llm-optimizer fully supports multi-GPU configurations, including tensor parallelism and data parallelism strategies.

How does the tool ensure the practical effectiveness of optimization results?

The tool ensures effectiveness by actually running benchmark tests and providing detailed performance metrics, while allowing users to define business-relevant constraints to ensure results meet practical needs.

Can optimized configurations be used in production environments?

Yes, the configuration parameters provided by llm-optimizer are native parameters for production-level inference frameworks (vLLM, SGLang) and can be directly used in production environments.

Does the tool support custom performance metrics?

Currently supports key metrics such as first-token latency, inter-token latency, end-to-end latency, and throughput. Future versions may expand to include more metric types.

How are models requiring authorized access handled?

For models requiring authorization, users need to obtain access permissions on Hugging Face first, then set the HF_TOKEN environment variable.

Does the visualization functionality require additional dependencies?

No, visualization functionality is included in the main package and can be used simply by starting it via the command line.