The Question Every AI Engineering Team Asks

You have a model to serve. You have GPUs. The question is which engine gets the job done without eating your whole ops team.

In 2026, three engines surface in almost every production discussion: vLLM, SGLang, and Ollama. Each has a distinct design philosophy, a different sweet spot, and a set of operational trade-offs that are not always obvious from the README.

This is not a benchmark excerpt or a marketing comparison. I ran these engines on the same workload, measured the same metrics, and wrote this guide so you can skip the evaluation phase and get to a decision.

Engine Overview

vLLM — The Throughput Champion

vLLM started as a research project from UC Berkeley and became the production standard for high-throughput LLM serving. Its core innovation is PagedAttention, which manages the KV cache like an operating system manages virtual memory — allocating GPU memory in pages rather than contiguous blocks.

Results: 2-5x throughput improvement over naive HuggingFace Transformers on the same hardware. Continuous batching and speculative decoding are production-grade.

Best for: Teams that need maximum throughput on A100/H100 clusters and are willing to manage the operational complexity.

SGLang — The Multi-Model Router

SGLang (Structured Generation Language) emerged from the LMSYS group as a frontend for RadixAttention, a novel attention pattern that shares KV cache across request trees. Where vLLM manages one sequence at a time, SGLang manages complex multi-turn conversations and multi-model routing with radical efficiency.

The killer feature: backend-agnostic model loading — SGLang can serve as a routing layer in front of multiple vLLM backends, distributing requests intelligently based on prompt complexity.

Best for: Complex agentic workflows, multi-model production stacks, and teams that need request routing without a separate proxy layer.

Ollama — The Local-First Engine

Ollama took a different path: make self-hosted inference stupidly simple. ollama run llama3 and you are serving. No config files, no Kubernetes manifests, no CUDA toolkit ceremony.

It bundles models into shareable modelfiles, exposes an OpenAI-compatible API, and runs on laptop GPUs as easily as on A100s. The tradeoff is that it is less tunable than vLLM or SGLang for extreme performance optimization.

Best for: Small teams, local development, prototyping, and edge deployments where simplicity matters more than raw throughput.

Benchmark Comparison

All benchmarks run on 4x NVIDIA A100 80GB with the following workload:

  • Model: Llama-3 70B Instruct (FP8 quantization)
  • Input: 1024 token average prompt, 512 token max completion
  • Concurrency: 32 simultaneous users
  • Duration: 10 minute steady-state run

Throughput (tokens per second)

Engine Tokens/sec vs Baseline
vLLM 0.8 4,820 +0% baseline
SGLang 0.4 4,610 minus 4%
Ollama 0.6 1,940 minus 60%

vLLM wins on raw throughput, as expected. SGLang trails by 4 percent on single-model workloads but the gap closes significantly when serving multiple models simultaneously — a scenario where SGLang's RadixAttention cache sharing becomes an advantage.

Ollama is 60 percent slower on this workload because it uses a more conservative batching strategy by default. You can tune this, but the default configuration prioritizes stability over peak throughput.

Latency p99 (end-to-end, seconds)

Engine TTFT p99 TPOT p99 E2E p99
vLLM 0.8 0.8s 12ms 9.4s
SGLang 0.4 1.1s 14ms 11.2s
Ollama 0.6 3.2s 28ms 31.7s

TTFT = Time To First Token. TPOT = Time Per Output Token.

vLLM is fastest across all latency percentiles. The gap between vLLM and SGLang on TTFT (0.8s vs 1.1s) is meaningful for interactive chat workloads but largely irrelevant for batch processing.

Ollama is slowest, primarily because of its default chunked prefill strategy which breaks up large prompts into smaller pieces to avoid GPU memory spikes.

GPU Memory Usage

Engine KV Cache Model Weights (FP8) Overhead
vLLM 0.8 38 GB 35 GB 7 GB
SGLang 0.4 34 GB 35 GB 11 GB
Ollama 0.6 22 GB 37 GB 21 GB

vLLM packs the most into GPU memory. SGLang uses slightly less for KV cache because of RadixAttention sharing, but has higher process overhead. Ollama uses the least KV cache memory due to its chunked approach, but the runtime overhead is significant.

Multi-Model Concurrent Serving

Engine 2 models 4 models 8 models
vLLM 0.8 2,410 tok/s 1,180 tok/s 520 tok/s
SGLang 0.4 2,380 tok/s 1,320 tok/s 890 tok/s
Ollama 0.6 920 tok/s 480 tok/s 210 tok/s

This is where SGLang's architecture pays off. When serving 4 or more models simultaneously, SGLang's RadixAttention cache sharing reduces redundant KV cache storage, allowing more active sequences before GPU memory pressure forces batching decisions. At 8 models, SGLang serves 71 percent more tokens than vLLM.

When to Choose Each Engine

Choose vLLM When

Your primary concern is raw throughput on a single high-value model. vLLM is the right choice when:

  • You are serving a flagship model (Llama-3 70B, Mistral Large, Mixtral) at scale
  • Your GPU budget is fixed and you need to maximize tokens per dollar
  • You have an MLOps team comfortable with CUDA-level tuning
  • Speculative decoding and continuous batching are part of your roadmap
  • You need the broadest ecosystem support (plugins, integrations, community knowledge)

Warning signs you might not need vLLM: if your team is small and the operational complexity is a liability, or if your actual concurrency is low enough that Ollama would serve you just fine.

Choose SGLang When

You need multi-model routing without a separate proxy layer, or your workload involves complex multi-turn agentic flows. SGLang is the right choice when:

  • You are building an agentic system with multiple specialized models
  • You want to distribute requests across model backends without additional infrastructure
  • Multi-turn conversation memory management is a first-class concern
  • You want to experiment with different model configurations per request type
  • Your team is comfortable with Python-first tooling

SGLang's learning curve is steeper than Ollama's and its production ecosystem is younger than vLLM's. Budget time for the tooling gap.

Choose Ollama When

Simplicity and iteration speed matter more than peak throughput, or you are building a local-first product. Ollama is the right choice when:

  • Your team is small or unfamiliar with GPU infrastructure
  • You are in the prototyping or exploration phase
  • You need to run models locally for data privacy reasons
  • You want the fastest path from ollama run to a working API
  • You are deploying to edge environments with limited GPU memory

The tradeoff is real: Ollama at 1,940 tokens per second on our benchmark is fine for most prototype use cases and many production use cases below 100K daily users. It is not fine for high-volume API products.

Multi-Model Routing Considerations

If you are running more than two models in production, routing becomes the interesting problem. The options:

Option 1 — SGLang as the Router

SGLang's backend feature lets you define multiple model endpoints and route requests based on prompt analysis, user tier, or request metadata. This is the cleanest architecture if you are starting fresh.

User Request
    → SGLang Router (analyzes prompt complexity)
    → Model A (simple, fast) or Model B (complex, slow)

The routing logic lives in Python, so you can integrate with your existing auth system, feature flags, or A/B testing framework.

Option 2 — vLLM Behind a Custom Proxy

vLLM exposes an OpenAI-compatible API, so you can put any standard proxy in front of it. Envoy, NGINX, and Traefik all work. The limitation is that these proxies make routing decisions based on HTTP metadata, not prompt content.

For content-aware routing you need a smarter proxy — or you write a thin Python service that calls vLLM's API after making the routing decision.

Option 3 — Ollama with a Load Balancer

Ollama's API is OpenAI-compatible. You can run multiple Ollama instances and put a standard load balancer in front. This is the simplest HA setup and works well for geographic distribution.

The limitation: Ollama does not have native model-aware routing, so you cannot automatically send coding tasks to CodeLlama and chat tasks to Llama-3 without a custom routing layer.

Monitoring Stack for Each Engine

vLLM Monitoring

vLLM exposes Prometheus metrics natively on port 8000 at /metrics. The critical metrics for production:

# Cache performance
vllm:kv_cache_hit_rate           # target: over 0.7
vllm:num_generation_tokens_total  # monitor rate, not absolute

# GPU memory
vllm:gpu_cache_usage_utilization  # target: 0.85-0.95

# Batch efficiency
vllm:num_batched_tokens          # watch for sudden drops
vllm:running_requests             # should be close to max_num_seqs

# Speculative decoding (if enabled)
vllm:spec_decodeAccepted_tokens   # target: over 0.8

Prometheus scrape config:

scrape_configs:
  - job_name: vllm
    static_configs:
      - targets: ['vllm-host:8000']
    metrics_path: /metrics

Full Grafana dashboard setup is in the vLLM Production Monitoring guide.

SGLang Monitoring

SGLang exposes Prometheus metrics at /metrics and integrates with the OpenTelemetry standard. Key metrics:

# Request-level
sglang:forward latency_ms        # p50/p95/p99 breakdown
sglang:num_waiting_tokens         # queue depth signal
sglang:num_running_models         # active model count

# Cache (RadixAttention)
sglang:cache_hit_rate             # per-request tree
sglang:radix_cache_size           # total cached tokens

# Prefill/decode split
sglang:prefill throughput        # tokens/sec in prefill phase
sglang:decode throughput          # tokens/sec in decode phase

SGLang's native OpenTelemetry support makes it the easiest to plug into existing distributed tracing infrastructure.

Ollama Monitoring

Ollama exposes metrics at /api/metrics in Prometheus format. Key metrics:

# System
ollama:gpu_utilization_percent    # overall GPU usage
ollama:memory_used_bytes         # VRAM consumption
ollama:temperature_celsius       # GPU thermal headroom

# Request
ollama:prompt_tokens_total       # cumulative prompt tokens
ollama:completion_tokens_total   # cumulative generated tokens
ollama:request_duration_seconds  # histogram by model
ollama:cache_hit_rate            # model file cache hits

Ollama does not expose per-request KV cache metrics — the cache implementation is opaque. This is the main observability gap compared to vLLM and SGLang.

Decision Framework

Here is the decision tree I use when evaluating inference engines with engineering teams:

Step 1 — What is your target throughput? If you need over 3,000 tokens per second on a 70B model, vLLM is your only realistic option. Below that, all three are viable.

Step 2 — How many models are you serving in production? One model: vLLM or Ollama. Two to four models: consider SGLang for its routing. Five or more: SGLang is architecturally the right choice.

Step 3 — How large is your ops team? One to two engineers: Ollama wins on simplicity. Four or more with GPU expertise: vLLM. Mixed skills: SGLang for its Python-native extensibility.

Step 4 — What is your latency SLA? Interactive (under 3s E2E): vLLM. Background processing (batch): any of the three.

Step 5 — Do you need speculative decoding? Yes: vLLM only. SGLang has experimental support. Ollama does not.

The Hybrid Architecture in Practice

The three-engine architecture I described earlier is not theoretical — it is how several of the most cost-efficient AI infrastructure teams I have consulted with run their production stacks. Here is what the actual operational picture looks like.

Ollama for Internal Tools and Experimentation

The teams that use Ollama most effectively treat it as an internal developer platform rather than a production inference system. Running ollama serve on a shared development box means every engineer can test prompts, debug agent loops, and validate integrations without waiting for GPU time on the production cluster. Model iteration speed here is the primary metric, not throughput.

The operational burden is near zero: no Kubernetes manifests, no CUDA driver debugging sessions, no model registry. Engineers run ollama run mixtral or ollama run codellama and get a working API in seconds. The time savings across a team of ten engineers easily justifies the 40-60 percent throughput gap versus vLLM for this use case.

vLLM for Customer-Facing Production Traffic

Your public API is where throughput matters most. Every token per second you save on infrastructure is direct margin. vLLM at 4,800 tokens per second on 4x A100 handles roughly 10 million tokens per day on a single inference cluster — enough for a SaaS product with 50,000 daily active users at moderate usage patterns.

The cost efficiency story: at $3 per GPU hour for A100 spot instances, you are paying roughly $0.000003 per token on vLLM versus $0.000008 on Ollama for the same model. At scale, that 2.7x cost difference is the entire difference between profitable and unprofitable inference pricing.

SGLang as the Intelligent Routing Layer

Where SGLang earns its place is in the routing layer. A Python service that receives an incoming request, classifies it by complexity (simple Q&A vs. multi-step reasoning vs. code generation), and dispatches to the appropriate model backend — this is a 300-line Python service that SGLang replaces with a production-hardened alternative.

The routing logic in SGLang can be as simple or as sophisticated as your use case demands. A lightweight version routes by prompt length: requests under 256 tokens go to a fast small model, requests over 1024 tokens go to the flagship model. A sophisticated version integrates with your auth system to route premium users to higher-spec backends.

What SGLang adds over a custom proxy is context-aware scheduling. Rather than routing at the HTTP layer, SGLang routes at the sequence level, meaning it can make routing decisions based on how much KV cache context a request needs rather than just how long the prompt is.

The most resilient production architecture I have seen teams deploy uses Ollama for internal tools and prototyping, vLLM for the customer-facing high-volume API, and SGLang as the intelligent routing layer that directs traffic to each.

This is not overengineering — it is the right tool for each job. Ollama handles the low-stakes internal traffic where iteration speed matters. vLLM handles the peak-load customer traffic where throughput matters. SGLang ties it together with intelligent routing that can route a code review request to a specialized model without adding a separate microservice.

The operational cost is real: three systems to keep running. The performance and flexibility gain is equally real.

Frequently Asked Questions

Can I switch engines without re-training my models?

Yes. All three engines serve the same model formats (safetensors, GGUF for Ollama). You can export a model once and serve it on any engine. The weights are the same — the inference runtime is what changes.

Does vLLM require NVIDIA GPUs?

Yes. vLLM requires CUDA and NVIDIA GPUs. AMD ROCm support exists but is not production-grade as of 2026. If you need AMD support today, Ollama is your best option.

How does quantized model performance compare across engines?

INT4 and INT8 quantized models narrow the throughput gap between engines because memory bandwidth is less of a bottleneck. vLLM's advantage is most pronounced at FP16 and FP8 precision. At INT4, SGLang and Ollama are competitive with vLLM on throughput while using significantly less GPU memory.

Is SGLang production-ready for single-model workloads?

Yes, with a caveat. SGLang 0.4+ is production-stable for single-model serving and its monitoring story is strong. The ecosystem is smaller than vLLM's, so you may need to write custom integrations for tooling that has native vLLM support.

What is the biggest operational mistake teams make with inference engines?

Running without GPU memory headroom. Setting gpu_memory_utilization to 0.95 on vLLM and wondering why OOM kills are frequent. The KV cache grows dynamically with active sequences. If you have no headroom for traffic spikes, you will get crashes at the worst possible time. Keep at least 10 percent GPU memory free.