NVIDIA NIM alternatives: serving runtimes

Choose NVIDIA NIM Day 0 for early packaged model access, vLLM for an OpenAI-compatible server, or SGLang for programmable serving; each uses the same separate $394.92 H100 budget in the 100-hour example.

Updated · 5 sources · Methodology

NeedPickWhy
Early model access in NVIDIA’s packageNVIDIA NIM Day 0NIM Day 0 provides early model access with a $0 runtime license fee; the separate 100-hour H100 budget is $394.92.15
OpenAI-compatible servingvLLMOpenAI-compatible serving with a $0 runtime license fee.2
Programmable servingSGLangProgrammable serving runtime with a $0 runtime license fee.3

Performance

ProviderAPI and model IDServing architecture
NVIDIA NIM Day 01NIM LLM 2.0.5 Day 0NIM Day 0 is the free-to-use offering for early model access. It is separate from the enterprise lifecycle and support commitments of NIM Certified.
vLLM2Apache-2.0 inference enginevLLM exposes an OpenAI-compatible serving interface with continuous batching and an openly configurable inference engine.
SGLang3Apache-2.0 serving frameworkSGLang combines a serving runtime with a programming interface for language and multimodal model workloads.
TensorRT-LLM4Apache-2.0 NVIDIA runtimeTensorRT-LLM targets NVIDIA GPUs with Python and C++ runtime components and model-specific inference optimizations.

Prices and request cost

ProviderIllustrative H100 / GPU-hour100 GPU-hour compute cost
NVIDIA NIM Day 01$3.94925$394.925
vLLM2$3.94925$394.925
SGLang3$3.94925$394.925
TensorRT-LLM4$3.94925$394.925
Hardware-only example: 100 H100 GPU-hours5
NVIDIA NIM Day 0$394.92
vLLM$394.92
SGLang$394.92
TensorRT-LLM$394.92

Options, strengths and limitations

  1. vLLM

    vLLM exposes an OpenAI-compatible serving interface with continuous batching and an openly configurable inference engine.

    Illustrative H100 / GPU-hour
    $3.94925
    Example quantity
    100
    Example total
    $394.925

    Strengths

    • vLLM combines an OpenAI-compatible API with continuous batching under Apache-2.0.2

    Limitations

    • The operator owns deployment, updates and capacity planning; an open-source runtime does not include a managed API SLA.2

    Sources:2

  2. SGLang

    SGLang combines a serving runtime with a programming interface for language and multimodal model workloads.

    Illustrative H100 / GPU-hour
    $3.94925
    Example quantity
    100
    Example total
    $394.925

    Strengths

    • SGLang provides a programmable interface for language and multimodal serving.3

    Limitations

    • Model and accelerator support is release-specific; SGLang capability does not establish identical speed across engines.3

    Sources:3

  3. TensorRT-LLM

    TensorRT-LLM targets NVIDIA GPUs with Python and C++ runtime components and model-specific inference optimizations.

    Illustrative H100 / GPU-hour
    $3.94925
    Example quantity
    100
    Example total
    $394.925

    Strengths

    • TensorRT-LLM exposes Python and C++ runtime components for NVIDIA GPU inference.4

    Limitations

    • The NVIDIA-specific optimization path narrows hardware portability; model conversion and supported profiles remain deployment work.4

    Sources:4

Methodology and sources

NVIDIA NIM Day 0 / vLLM / SGLang / TensorRT-LLM. The baseline is free NIM Day 0 as described in the cited LLM 2.0.5 documentation, not a NIM Certified contract. Each runtime receives the same illustrative hardware budget: one H100 for 100 GPU-hours at Modal’s listed $0.001097/second, or $3.9492/hour. This is a hardware-only cost calculation, not a hosted model tariff or measured engine benchmark. CPU, RAM, storage, utilization and engineering labor are excluded. GPU fit and supported model profiles depend on configuration. No runtime throughput or startup winner is assigned.5

  1. NVIDIA NIM Day 0 · offering specifications
  2. vLLM · inference engine, documentation and license
  3. SGLang · serving framework, documentation and license
  4. TensorRT-LLM · NVIDIA runtime, documentation and license
  5. Modal · H100 hardware pricing

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