Choosing Between vLLM SGLang and TensorRT

Car buyers choose between Toyota, Honda, and BMW. Each serves different needs. The "best" car depends on what you optimize for—reliability, fuel economy, or performance.

LLM serving frameworks work similarly. vLLM, SGLang, and TensorRT-LLM each make different tradeoffs. The right choice depends on your constraints.

The Decision Matrix

def recommend_framework(requirements: dict) -> str:
    # TensorRT-LLM: Maximum performance, willing to invest
    if requirements.get("squeeze_last_10_percent"):
        if requirements.get("dedicated_ml_team"):
            return "TensorRT-LLM"
        else:
            return "vLLM"  # Not worth the complexity

    # SGLang: Complex prompt workflows
    if requirements.get("multi_turn_with_branching"):
        if requirements.get("prefix_caching_critical"):
            return "SGLang"

    # vLLM: Default for most production use cases
    if requirements.get("production_stability"):
        return "vLLM"

    return "vLLM"  # Default recommendation

vLLM: The Production Default

class VLLMProfile:
    strengths = [
        "Battle-tested in production",
        "Easy deployment",
        "Good documentation",
        "Active community",
        "Continuous batching + PagedAttention",
    ]

    weaknesses = [
        "Not the absolute fastest",
        "Less optimization flexibility",
        "Some advanced features lag behind",
    ]

    best_for = [
        "First production deployment",
        "Teams without ML infra specialists",
        "Standard serving patterns",
        "When time-to-production matters",
    ]

    typical_config = """
    # vllm serve meta-llama/Llama-2-70b-hf \\
    #   --tensor-parallel-size 4 \\
    #   --max-num-seqs 256 \\
    #   --max-model-len 4096
    """

TensorRT-LLM: Maximum Performance

class TensorRTLLMProfile:
    strengths = [
        "Fastest inference (10-20% over vLLM)",
        "Deep NVIDIA optimization",
        "FP8 support on H100",
        "Custom kernel support",
    ]

    weaknesses = [
        "Steep learning curve",
        "Model conversion required",
        "Debugging is harder",
        "Community smaller than vLLM",
    ]

    best_for = [
        "When 10% latency reduction = real money",
        "Teams with CUDA expertise",
        "NVIDIA-only deployments",
        "High-volume, stable workloads",
    ]

    conversion_reality = """
    # What the docs say: Run conversion script
    # Reality:
    # - 2-5 days for standard models
    # - 1-2 weeks for custom architectures
    # - Ongoing maintenance as models update
    """

SGLang: Structured Generation

class SGLangProfile:
    strengths = [
        "Optimized prefix caching",
        "RadixAttention for branching",
        "Constrained decoding",
        "Multi-turn optimization",
    ]

    weaknesses = [
        "Newer, less battle-tested",
        "Smaller deployment base",
        "Some features experimental",
    ]

    best_for = [
        "Complex multi-turn conversations",
        "When same prefix used repeatedly",
        "Structured output generation",
        "Research and experimentation",
    ]

    prefix_caching_example = """
    # SGLang shines when:
    # Request 1: [System prompt] + [User A question]
    # Request 2: [System prompt] + [User B question]
    # Request 3: [System prompt] + [User C question]

    # System prompt KV cache reused across all
    # Can be 2-5x faster for shared prefixes
    """

Real-World Comparison

def benchmark_reality() -> dict:
    # Typical production numbers (vary by workload)
    return {
        "vLLM": {
            "throughput": "baseline",
            "latency": "baseline",
            "setup_time": "1 day",
            "maintenance": "low",
        },
        "TensorRT-LLM": {
            "throughput": "+15-20%",
            "latency": "-10-15%",
            "setup_time": "1-2 weeks",
            "maintenance": "high",
        },
        "SGLang": {
            "throughput": "+10-30% (prefix heavy)",
            "latency": "similar to vLLM",
            "setup_time": "2-3 days",
            "maintenance": "medium",
        },
    }

The Decision Flowchart

def choose_framework(context: dict) -> str:
    # Question 1: Team expertise
    if context["ml_infra_engineers"] < 1:
        return "vLLM - minimal ops overhead"

    # Question 2: Performance criticality
    if context["latency_is_revenue_critical"]:
        if context["nvidia_gpus"] and context["can_invest_weeks"]:
            return "TensorRT-LLM - squeeze every ms"

    # Question 3: Workload pattern
    if context["heavy_prefix_sharing"]:
        if context["same_system_prompt_millions_of_times"]:
            return "SGLang - RadixAttention saves compute"

    # Default
    return "vLLM - right choice for most teams"

Migration Path

Most teams should:

1. Start with vLLM (production in days)
2. Measure actual bottlenecks
3. Consider SGLang if prefix caching shows promise
4. Consider TensorRT-LLM only if 10% matters and you have the team

The best framework is the one your team can operate reliably. A perfectly tuned TensorRT setup that crashes on weekends costs more than a "slower" vLLM setup that just works.