What Criteria Should Be Used to Select an AI Inference Engine for ML Deployment?

Most teams pick an inference engine based on benchmark scores alone, and that's a mistake. The engine that tops a throughput chart on a clean test rig may fall apart when it hits your actual production constraints: hardware variability, concurrency patterns, memory limits, and integration requirements.

The right selection criteria depend on who's running the model, where it's running, and what trade-offs you can accept.

This guide breaks inference engines into three deployment categories (local, multi-tenant, and distributed), maps the core selection dimensions for each, identifies the best-fit open-source engines per category, and shows how GMI Cloud's GPU infrastructure (H100/H200 clusters with pre-configured vLLM, TensorRT-LLM, and Triton) fits into each deployment path.

If you're evaluating engines for production ML deployment, this is the framework to start with.

Two Deployment Modes, Two Sets of Criteria

Why Deployment Context Changes Everything

LLM inference engines split into two fundamentally different deployment modes: local (single-user, device-level) and multi-tenant (server-side, API-serving). Each mode has different hardware assumptions, optimization targets, and selection criteria.

Picking a multi-tenant engine for a local use case (or vice versa) wastes either performance or resources.

Local Inference: Single User, Consumer Hardware

Local engines run models on a single device for a single user. Think: a developer running a coding assistant on a laptop, or a chatbot embedded in a mobile app. The hardware is low-power and unpredictable (varying GPU VRAM, CPU-only fallback, limited memory).

The optimization target is fast decode speed for one user, not throughput for thousands.

Multi-Tenant Inference: Servers, APIs, Data Centers

Multi-tenant engines serve models to many concurrent users from high-performance GPU clusters. The hardware is known and optimized (NVIDIA H100/H200 with NVLink, InfiniBand networking). The optimization target is maximum throughput per GPU dollar, with tight P99 latency bounds.

This is where enterprise production inference lives.

Open-Source Engines by Deployment Mode

Mode (Key Engines / Optimization Target / Hardware Assumption)

• Local — Key Engines: llama.cpp, Mistral.rs, Ollama — Optimization Target: Fast single-user decode, low memory — Hardware Assumption: Consumer GPU/CPU, variable VRAM
• Multi-Tenant — Key Engines: vLLM, SGLang, TensorRT-LLM — Optimization Target: Max throughput, min cost per token — Hardware Assumption: Data-center GPUs (H100/H200)
• Distributed — Key Engines: NVIDIA Dynamo — Optimization Target: Million-request scale, cross-node — Hardware Assumption: Multi-node GPU clusters

Local Inference Engines: Selection Criteria and Recommendations

What to Optimize For

Local engines need four things: portability (runs on Linux, macOS, Windows without CUDA dependency), lightweight binaries (single executable, no complex dependency tree), memory efficiency (fits 7B-13B models in 8-16 GB VRAM or system RAM via quantization), and fast single-stream decoding (tokens per second for one user, not batch throughput).

Engine Recommendations

llama.cpp is the go-to for local inference. It's a pure C/C++ implementation that runs on CPU, CUDA, Metal, and Vulkan backends. It uses the GGUF format with aggressive weight quantization (Q4, Q5, Q8), letting you run 7B models on 8 GB VRAM or even pure CPU.

If you're building a personal chatbot, a coding assistant, or running LLMs on consumer hardware, llama.cpp is the default starting point.

Mistral.rs is a Rust-based alternative that focuses on fast decode speed with ISQ (in-situ quantization). It's particularly strong on Apple Silicon, and its memory-mapped model loading reduces startup time.

If you're deploying on macOS devices or need Rust ecosystem integration, it's worth benchmarking against llama.cpp for your specific model.

GMI Cloud Compatibility Note

Local engines aren't the primary use case for GMI Cloud's data-center GPU infrastructure. But if you're using llama.cpp or Mistral.rs to prototype locally and then need to scale to production, GMI Cloud's Deploy endpoints support the same models in multi-tenant configurations running on H100/H200 GPUs.

The transition path is: prototype locally with quantized models, then deploy full-precision or FP8 versions on GMI Cloud for production throughput.

Multi-Tenant Inference Engines: Selection Criteria and Recommendations

What to Optimize For

Multi-tenant engines need high throughput (requests per second across concurrent users), optimized latency (time-to-first-token under load), efficient GPU utilization (continuous batching, PagedAttention, KV-cache management), and compatibility with your GPU infrastructure.

For H100/H200 deployments, FP8 support is critical since it doubles effective throughput compared to FP16 with minimal quality degradation.

Engine Recommendations

vLLM is the most widely adopted multi-tenant engine. Its PagedAttention mechanism virtually eliminates KV-cache memory waste, enabling higher concurrent batch sizes. It supports continuous batching, tensor parallelism, and OpenAI-compatible API out of the box.

On GMI Cloud's H100 clusters, vLLM is pre-configured and optimized for the NVLink 4.0 topology (900 GB/s bidirectional aggregate per GPU on HGX/DGX platforms).

SGLang takes a different approach with RadixAttention, which caches and reuses KV-cache prefixes across requests that share common prompt structures. If your workload has high prefix overlap (RAG pipelines, templated prompts, chat with system messages), SGLang can deliver 2-5x throughput gains over naive serving.

It also supports constrained decoding and structured output natively.

TensorRT-LLM is NVIDIA's optimization layer that compiles models into highly optimized CUDA kernels. It delivers the highest peak throughput on NVIDIA hardware, especially with FP8 on H100/H200. The trade-off is less flexibility: model compilation takes time, and not every architecture is supported immediately.

It's best for production workloads where you've locked in a specific model and want maximum tokens per second.

GMI Cloud's Pre-Configured Stack

GMI Cloud's Deploy endpoints come pre-configured with all three engines: vLLM, TensorRT-LLM, and Triton Inference Server, running on H100 (~$2.10/GPU-hour) or H200 (~$2.50/GPU-hour) SXM clusters with CUDA 12.x. You don't need to build, compile, or tune these engines yourself.

For the 100+ models in GMI Cloud's library (including GLM-5 by Zhipu AI at $1.00/M input, $3.20/M output), the serving engine is already optimized. For custom models via Deploy, you can select the engine that matches your workload. Check gmicloud.ai/pricing for current GPU rates.

The Emerging Middle: Distributed Inference Engines

What Dynamo Adds

NVIDIA Dynamo is a distributed inference framework that sits on top of engines like vLLM and TensorRT-LLM. It's designed for million-request-scale serving across multi-node GPU clusters. Its key innovations: disaggregated prefill and decode (different GPU pools handle prompt processing vs.

token generation), KV-cache-aware load balancing (routes requests to nodes that already have relevant cache), and built-in orchestration for scaling across nodes without manual scheduling.

When You Need Distributed Inference

If you're serving fewer than 100K daily requests, vLLM or TensorRT-LLM on a single multi-GPU node handles it fine. Dynamo becomes relevant when you're scaling to millions of requests across 10+ nodes and need intelligent routing to maximize cache hit rates and minimize cross-node data transfer.

It's the infrastructure layer for hyperscale inference.

GMI Cloud Cluster Compatibility

GMI Cloud's multi-node infrastructure (8 GPUs per node, NVLink 4.0 intra-node, 3.2 Tbps InfiniBand inter-node) is architecturally aligned with Dynamo's requirements.

For enterprise teams that need distributed inference at scale, GMI Cloud provides the GPU cluster topology that Dynamo's disaggregated prefill/decode and KV-cache-aware routing are designed to exploit.

Engine Selection Decision Framework

Your Scenario (Recommended Engine / GMI Cloud Fit)

• Personal chatbot on laptop/mobile — Recommended Engine: llama.cpp (GGUF, Q4/Q5) — GMI Cloud Fit: Prototype locally, scale to Deploy
• macOS/Apple Silicon deployment — Recommended Engine: Mistral.rs (ISQ, Metal) — GMI Cloud Fit: Prototype locally, scale to Deploy
• Production API, <100K daily requests — Recommended Engine: vLLM (PagedAttention) — GMI Cloud Fit: Pre-configured on H100/H200
• High prefix overlap (RAG, templates) — Recommended Engine: SGLang (RadixAttention) — GMI Cloud Fit: Compatible with Deploy endpoints
• Maximum throughput, locked model — Recommended Engine: TensorRT-LLM (FP8 compiled) — GMI Cloud Fit: Pre-configured on H100/H200
• Million-scale, multi-node — Recommended Engine: Dynamo + vLLM/TRT-LLM — GMI Cloud Fit: NVLink + InfiniBand cluster
• Don't want to manage engines at all — Recommended Engine: GMI Cloud Model Library API — GMI Cloud Fit: 100+ models, engine handled for you

The last row is worth highlighting. If you don't want to select, configure, and maintain an inference engine, GMI Cloud's Model Library provides 100+ pre-optimized models (45+ LLMs including GLM-5, GPT-5, Claude, DeepSeek, Qwen) via a unified OpenAI-compatible API.

GLM-5 output at $3.20/M is 68% cheaper than GPT-5 ($10.00/M) and 79% cheaper than Claude Sonnet 4.6 ($15.00/M). For high-volume workloads, GLM-4.7-Flash at $0.40/M output is 33% cheaper than GPT-4o-mini ($0.60/M). The engine selection is handled behind the scenes.

Check console.gmicloud.ai for current model availability and pricing.

FAQ

Q: Should I pick an engine based on benchmark scores?

Not exclusively. Benchmarks test specific model sizes, batch sizes, sequence lengths, and hardware configs. Your production workload likely differs on at least two of those dimensions. Use benchmarks to shortlist 2-3 engines, then run your own tests with your actual prompts, concurrency patterns, and hardware.

Q: Can I switch inference engines on GMI Cloud without rewriting code?

Yes. GMI Cloud's Deploy endpoints abstract the engine layer behind an OpenAI-compatible API. Whether the backend runs vLLM, TensorRT-LLM, or Triton, your integration code stays the same. For pre-built models in the Model Library, the engine is already selected and optimized.

Q: What's the cost difference between self-managed engines and GMI Cloud's API?

Self-managed engines on raw GPU instances (H100 at ~$2.10/GPU-hour) give you maximum control but require engineering time for setup, tuning, and maintenance. GMI Cloud's Model Library API (e.g., GLM-5 at $1.00/M input, $3.20/M output) handles all of that. The break-even depends on your volume and engineering costs.

Q: Which engine works best with FP8 on H100?

TensorRT-LLM delivers the highest FP8 throughput on H100/H200 because it compiles models into hardware-optimized CUDA kernels. vLLM also supports FP8 with good results and offers more flexibility for model swaps. Both are pre-configured on GMI Cloud's Deploy endpoints.

What GPU Hardware Provides Optimal Performance