You've trained your model and now you need to serve it. vLLM, TensorRT-LLM, SGLang, TGI, llama.cpp, Triton—each claims to be the best. The benchmarks compare different scenarios. The documentation assumes you already know what you need. Choosing wrong means either leaving performance on the table or rebuilding your inference stack after deployment.
NeoSignal Serving Engine Advisor showing engine recommendations
NeoSignal Serving Engine Advisor cuts through the noise. Enter your requirements: model, latency target, throughput target, GPU configuration. The advisor analyzes your workload against six major inference engines and recommends the best fit. The screenshot shows vLLM recommended with a score breakdown—latency score, throughput score, feature score. You see predicted P95 latency, estimated throughput, whether your targets are achievable. Below that: batching configuration, deployment commands, autoscaling guidance. One tool, one decision, deployment-ready output.
The benefit: you choose inference infrastructure with confidence. No more trial-and-error benchmarking across engines. The advisor encodes engine characteristics, performance profiles, and compatibility constraints so you can deploy the first time.
Detailed Walkthrough
The Inference Engine Landscape
The LLM inference ecosystem has matured rapidly. What started with basic HuggingFace transformers serving has evolved into specialized engines optimized for different workload characteristics:
vLLM pioneered PagedAttention for efficient KV cache management, enabling dramatically higher throughput for batched workloads. It's the default choice for high-throughput scenarios where you're batching many concurrent requests.
TensorRT-LLM leverages NVIDIA's TensorRT for kernel-level optimization, achieving the lowest latencies on NVIDIA hardware. It's the choice when every millisecond matters and you're willing to invest in compilation time.
SGLang introduced RadixAttention for efficient prefix caching and excels at structured generation—JSON outputs, code completion, constrained decoding where the output format matters.
Text Generation Inference (TGI) from HuggingFace offers production-ready serving with Flash Attention and continuous batching, optimized for the HuggingFace model ecosystem.
llama.cpp enables CPU and Apple Silicon deployment with GGUF quantization, perfect for edge deployment or when you don't have access to datacenter GPUs.
Triton Inference Server provides enterprise-grade multi-model serving with dynamic batching, metrics, and Kubernetes-native deployment patterns.
Each engine has strengths and weaknesses. NeoSignal Serving Engine Advisor encodes this knowledge into an automated recommendation system.
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Input Configuration
The advisor collects the information needed to make accurate recommendations:
Model Selection: Choose from NeoSignal's model database. The advisor extracts architecture information (Llama, Mistral, Qwen, Gemma, etc.) to filter compatible engines. Some engines support specific architectures better than others.
Latency Target (P95): Your maximum acceptable latency at the 95th percentile, in milliseconds. Latency-sensitive applications (chatbots, real-time assistance) might target 100ms. Batch processing can tolerate higher latencies.
Throughput Target: Required requests per second. This shapes whether you need an engine optimized for batching (vLLM) or one optimized for single-request latency (TensorRT-LLM).
GPU Configuration: Select from NeoSignal's accelerator database. The advisor considers GPU memory capacity, compute capability, and engine compatibility. TensorRT-LLM requires NVIDIA GPUs; llama.cpp works on CPUs and Apple Silicon.
Batch Strategy: Choose dynamic batching (requests batched as they arrive) or fixed batching (predetermined batch sizes). Dynamic batching works better for variable traffic patterns.
GPU Count: Number of GPUs for tensor parallelism. Affects memory distribution and throughput scaling. Not all engines support all parallelism configurations.
Scoring Methodology
NeoSignal scores each engine against your requirements across three dimensions:
Latency Score (40% weight): How well does the engine's expected P95 latency meet your target? Engines with latency profiles below your target score higher. TensorRT-LLM (P95: 100ms baseline) scores better than llama.cpp (P95: 1000ms baseline) for latency-sensitive workloads.
Throughput Score (40% weight): Can the engine achieve your required requests per second? This considers the engine's throughput multiplier—vLLM at 1.0x baseline vs llama.cpp at 0.15x. High-throughput requirements favor engines with efficient batching.
Feature Score (20% weight): Does the engine support features you'll need? Continuous batching, PagedAttention, tensor parallelism, speculative decoding, quantization formats. More feature support means more optimization options.
The total score combines these dimensions, with the highest-scoring engine recommended. Alternative engines are ranked below for comparison.
Engine Database
NeoSignal maintains detailed profiles for each inference engine:
| Engine | Max Batch | Cont. Batching | PagedAttention | Tensor Parallel | Spec. Decoding | P95 Latency |
|---|---|---|---|---|---|---|
| vLLM | 256 | Yes | Yes | Yes | Yes | 150ms |
| TensorRT-LLM | 128 | Yes | Yes | Yes | Yes | 100ms |
| SGLang | 128 | Yes | Yes | Yes | No | 130ms |
| TGI | 64 | Yes | Yes | Yes | No | 170ms |
| llama.cpp | 32 | No | No | No | Yes | 1000ms |
| Triton | 64 | Yes | No | Yes | No | 200ms |
Each engine also lists supported architectures, hardware requirements, and quantization formats. vLLM supports AWQ, GPTQ, and SqueezeLLM; TensorRT-LLM supports INT4 and FP8 natively; llama.cpp specializes in GGUF quantization.
Performance Predictions
Based on your inputs and engine characteristics, the advisor predicts:
Estimated P95 Latency: The expected 95th percentile latency in milliseconds, accounting for your model size, GPU configuration, and batch settings. A Llama 70B on 4x H100s with vLLM might show 180ms P95.
Estimated Throughput: Expected requests per second achievable with your configuration. The advisor scales baseline engine throughput by your GPU count and model efficiency factors.
Target Achievement: Green checkmarks for targets met, red warnings for targets at risk. If your 50ms latency target can't be met by any engine with your current configuration, the advisor shows this clearly.
Confidence Percentage: How confident the advisor is in these predictions. Higher confidence when your configuration matches well-benchmarked scenarios; lower confidence for edge cases.
Batching Configuration
Optimal batching varies by engine and workload. The advisor recommends:
Recommended Batch Size: The batch size balancing latency and throughput for your targets. Larger batches improve throughput but increase latency.
Max Concurrent Requests: How many requests the engine can handle simultaneously with your GPU memory budget.
Wait Time: For dynamic batching, how long to wait for additional requests before processing a batch. Shorter wait times reduce latency but may reduce batching efficiency.
Continuous Batching: Whether to enable continuous batching (processing new requests while previous requests are still generating). Recommended when supported.
Deployment Configuration
The advisor provides ready-to-use deployment artifacts:
Engine Configuration: Engine-specific configuration flags and environment variables. For vLLM: tensor parallelism settings, max model length, GPU memory utilization. For TensorRT-LLM: engine build commands, quantization settings.
Docker Command: A complete docker run command to launch the inference server with your configuration:
docker run --gpus all -p 8000:8000 \
vllm/vllm-openai:latest \
--model meta-llama/Llama-3.1-70B \
--tensor-parallel-size 4 \
--max-model-len 8192 \
--gpu-memory-utilization 0.9
Environment Variables: Required environment variables like CUDA_VISIBLE_DEVICES, model paths, and API configuration.
Autoscaling Guidance
Production deployments need scaling strategies. The advisor suggests:
Scaling Triggers: Metrics to watch for scaling decisions—queue depth, latency percentiles, GPU utilization. Different engines expose different metrics.
Min/Max Replicas: Recommended replica bounds based on your throughput targets and cost tolerance.
Scale-Up Sensitivity: How aggressively to add capacity. Latency-sensitive workloads benefit from proactive scaling; batch workloads can tolerate queuing.
Pod Disruption Budgets: For Kubernetes deployments, recommended PDB settings to maintain availability during updates.
Recommendations
Beyond the primary recommendation, the advisor provides actionable guidance:
Optimization Opportunities: "Enable speculative decoding to reduce latency by 20-30%" when your engine supports it and your model has a draft model available.
Alternative Configurations: "Consider TensorRT-LLM if you can accept longer initial compilation time for 40% lower latency."
Resource Warnings: "Your throughput target may require additional GPUs" when predictions show targets at risk.
Compatibility Notes: "This model architecture has limited support in TensorRT-LLM; consider vLLM for broader compatibility."
Chat Integration
Like all NeoSignal tools, the Serving Engine Advisor integrates with AI chat:
Context Sharing: Your advisor configuration and results are available to the chat. Ask "Why is vLLM recommended over TensorRT-LLM?" and the response references your specific latency/throughput targets.
Follow-Up Questions: After getting a recommendation, ask clarifying questions: "What happens if I reduce my latency target to 50ms?" or "How do I enable speculative decoding with this configuration?"
Deployment Help: Ask for guidance on the generated deployment configuration: "How do I monitor this vLLM deployment in production?" or "What Kubernetes resources do I need for this setup?"
Artifact Saving
Save advisor results for future reference:
Saved Configurations: Your inputs and recommendations save as artifacts. Return later to see what you configured and what was recommended.
Comparison: Save multiple configurations (different latency targets, different GPUs) to compare recommendations side-by-side.
Sharing: Artifact URLs can be shared with teammates for deployment planning discussions.
Real-World Usage Patterns
New Deployment: You're deploying Llama 3.1 70B for a customer-facing chatbot. Requirements: P95 latency under 200ms, 100 requests per second, 8x H100 SXM available. Enter these into the advisor. vLLM scores highest with continuous batching enabled. Copy the Docker command, deploy to your cluster, and you're serving.
Engine Migration: You're currently using TGI but hitting throughput limits. Enter your current model and targets. The advisor shows vLLM achieving 30% higher throughput with similar latency. The recommendation includes migration notes and configuration differences.
Hardware Evaluation: You're deciding between A100s and H100s for inference. Run the advisor twice with each GPU type. Compare predicted performance and cost-efficiency to inform procurement.
Latency Optimization: Your current deployment meets throughput but latency is too high. Enter your actual targets and see which engines could achieve lower latency. TensorRT-LLM recommendation comes with kernel optimization guidance.
Technical Foundation
The Serving Engine Advisor is built on:
Engine Database: Comprehensive profiles in src/lib/tools/serving/engines.ts with capabilities, performance characteristics, and compatibility information.
Scoring Algorithm: Multi-dimensional scoring in src/lib/tools/serving/calculate.ts that weighs latency, throughput, and features against your requirements.
Performance Models: Empirical models for predicting latency and throughput based on model size, GPU configuration, and engine characteristics.
Template Generation: Deployment configuration templates that fill in engine-specific flags based on your inputs.
From Advisor to Production
NeoSignal Serving Engine Advisor compresses weeks of benchmarking into minutes of configuration. You don't need to deploy each engine, run load tests, and compare results. The advisor encodes this knowledge—engine characteristics, performance profiles, compatibility constraints—into an automated recommendation.
The output isn't just "use vLLM." It's a complete deployment package: configuration, Docker commands, batching parameters, autoscaling guidance. Copy the commands, deploy, and serve. When requirements change, return to the advisor with new targets and get updated recommendations.
That's the NeoSignal approach to AI infrastructure tooling: expert knowledge encoded in precise calculations, delivered through interfaces that make complex decisions actionable. The Serving Engine Advisor is one tool in the suite. Memory Calculator, Spot Instance Advisor, and TCO Calculator apply the same philosophy to memory planning, cost optimization, and build-vs-buy decisions.