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Best LLM Inference Engines 2026: vLLM vs SGLang vs TGI vs llama.cpp

Deploybase · February 23, 2026 · AI Tools

Contents

Best LLM Inference Engine: Overview

Best LLM Inference Engine is the focus of this guide. Inference engine choice matters more than model choice. Pick the wrong engine and developers're bottlenecked hard. Five production-grade engines rule the market as of March 2026. Each optimized for different workloads.

Teams look at throughput (tokens/sec), latency (time to first token), memory use, deployment friction, and compatibility. Pick based on what the app needs, not bragging rights.

vLLM: Highest Throughput Leader

Architecture and Optimization

vLLM invented PagedAttention. The trick: treat attention caches like OS page tables. Most engines waste memory on short requests by allocating full context per request.

PagedAttention uses fixed-size pages. Short context? Fewer pages. Same hardware can now batch 10-50 requests instead of 2-5. Throughput jumps dramatically.

Performance Characteristics

vLLM hits about 3,500 tokens/sec on A100 80GB for Llama 70B. That's 40% better than baseline.

TTFT stays at 150-200ms, same as competitors. vLLM optimizes total throughput, not per-request speed.

Queued workloads? vLLM wins. Real-time apps that care about latency? Less impressive.

Deployment and Ecosystem

Deploy via Python API or HTTP. LangChain and LlamaIndex just work.

Speaks OpenAI API. Swap in vLLM without rewriting the code. Migration is smooth.

Memory-efficient. Llama 70B runs on a single 40GB GPU. No need to burn A100/H100 money.

Model Support

Supports Llama, Mistral, Qwen, Falcon, everything. New models usually arrive weeks after release.

Built-in quantization (AWQ, GPTQ). Load 4-bit models without conversion scripts. Just works.

SGLang: Fastest Latency Engine

Radix Attention Innovation

SGLang built on PagedAttention but adds Radix Attention. Stores computations in tries. Requests with identical prefixes (same system prompt, shared context) reuse cached attention. Multi-turn conversations, multi-stage workflows-computation drops hard.

TTFT Advantages

SGLang hits 80-120ms TTFT on single requests. 30-40% faster than vLLM. For interactive chat or tools, that matters.

Batch jobs? Throughput difference is marginal. Real-time? Speed is noticeable.

Stateful Computation Model

SGLang has function abstractions. Chain multi-stage inference in one call instead of multiple API hops. RAG, multi-step reasoning, structured output-all consolidated. Latency overhead drops, throughput climbs.

Current Maturity Status

Newer than vLLM. Smaller production footprint. Ecosystem lags slightly. But core stuff works.

Want latency over stability? SGLang. Need maximum ecosystem backing? vLLM.

TGI: HuggingFace's Accessible Solution

Text Generation Inference Design

TGI trades raw performance for ease. Developers get production inference without vLLM's operational overhead.

Auto-handles tensor parallelism across GPUs. Zero config needed. GPU optimization knowledge? Not required.

Production-Ready Features

Built-in safety: sampling distributions, repetition penalties, stop tokens. Saves developers from writing it yourself.

Custom stop sequences, logit biasing, detailed logging. See what's happening. Control it easily.

Performance Characteristics

TGI hits 2,500 tokens/sec on A100 for Llama 70B. Slower than vLLM, faster than baseline. Middle ground, good for most apps.

TTFT: 200-300ms. Slightly slower. Users won't notice.

Deployment Integration

Container or standalone service. Works with Kubernetes and cloud platforms. HuggingFace runs it in production, so it's proven stable.

Integrates smoothly with Transformers. If developers're already on HuggingFace, TGI feels native.

llama.cpp: CPU and Edge Inference

CPU-Optimized Design

llama.cpp runs on CPUs. Quantization makes it practical. Deploy on devices with no GPU. Edge boxes, embedded systems, cost-constrained servers.

Uses AVX2 and NEON SIMD. Single-threaded performance approaches GPUs for smaller models.

Quantization and Compression

Pioneered practical int4 and int8 quantization. Llama 7B/13B run acceptably on CPU. Size drops 75-90%, quality stays fine.

GGUF format came from llama.cpp. Now it's the standard for quantized models. Most HuggingFace models speak it.

Performance and Limitations

Llama 7B hits 15-30 tokens/sec on modern CPUs (i9, M2). Fine for non-interactive work. Scales linearly with cores.

70B+? Impractical. Sub-token-per-second. Interactive apps can't use it.

Deployment Advantages

Runs offline. No cloud needed. Privacy apps, disconnected environments. Download executable, run. No Python to configure.

iOS, Android, embedded systems-llama.cpp is the only practical choice. Lowest cost.

TensorRT-LLM: NVIDIA Optimization

GPU-Specific Optimization

NVIDIA's proprietary engine. Generates code for their GPUs (A100, H100, L4, L40S). Maximum performance on target hardware.

Builds execution graphs for inference only. Removes dead ops, fuses kernels. 2-3x better throughput than generic engines.

Compilation and Deployment

Must compile before running. 30-60 minute compile time. Trades complexity for speed.

Compiled models lock to specific GPU types and CUDA versions. Update the model? Recompile. Slower deployment cycle than Python engines.

Performance Benchmarks

4,500 tokens/sec on H100 for Llama 70B. Best throughput of all engines. Cost per token approaches theoretical limits.

TTFT: 150-200ms, same as vLLM. No latency advantage.

Adoption and Ecosystem

Used by teams squeezing max performance. Specialized, not general-purpose.

LangChain and LlamaIndex need custom adapters. Integration burden is real. Check before committing.

Performance Benchmarks

Throughput (Llama 70B on A100)

  1. vLLM: 3,500 tokens/sec
  2. SGLang: 2,800 tokens/sec
  3. TGI: 2,500 tokens/sec
  4. Baseline (transformers): 1,800 tokens/sec
  5. llama.cpp (CPU): 20 tokens/sec

Time-to-First-Token

  1. SGLang: 80ms
  2. vLLM/TensorRT-LLM: 150ms
  3. TGI: 250ms
  4. llama.cpp: 800ms

Memory Efficiency

vLLM and SGLang: similar efficiency via caching. TGI: slightly higher due to features. TensorRT-LLM: slight advantage from compilation.

llama.cpp: lowest footprint via quantization. Works on constrained hardware.

Deployment Guide Section

vLLM Deployment on GCP

Provision an A100 instance on Google Cloud Platform:

gcloud compute instances create vllm-server \
  --image-family="torch-xla" \
  --image-project=deeplearning-platform-release \
  --machine-type=a2-highgpu-1g \
  --accelerator=type=nvidia-tesla-a100,count=1 \
  --zone=us-central1-a

Install vLLM via pip:

pip install vllm

Launch server:

python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Llama-2-70b-hf \
  --tensor-parallel-size 2 \
  --dtype float16 \
  --gpu-memory-utilization 0.90 \
  --port 8000

SGLang Deployment with LangChain

Install SGLang and dependencies:

pip install sglang[all]

Launch the SGLang server:

python -m sglang.launch_server \
  --model-path meta-llama/Llama-2-70b-hf \
  --port 30000

Query via OpenAI-compatible API:

import openai

client = openai.Client(base_url="http://localhost:30000/v1", api_key="EMPTY")
response = client.chat.completions.create(
    model="meta-llama/Llama-2-70b-hf",
    messages=[{"role": "user", "content": "Summarize the following document: " + your_document}],
    max_tokens=200,
)
print(response.choices[0].message.content)

TensorRT-LLM Compilation

Download model:

huggingface-cli download meta-llama/Llama-2-70b-hf \
  --local-dir ./llama70b

Compile with TensorRT-LLM:

trtllm-build --checkpoint_dir ./llama70b \
  --output_dir ./llama70b-engine \
  --gemm_plugin=auto \
  --max_batch_size=256

Start inference server via Triton or the built-in server:

python -m tensorrt_llm.serve \
  --engine_dir ./llama70b-engine \
  --port 8000

Optimization Tips Per Engine

vLLM Optimization

Enable prefix caching for repeated prompts:

llm = LLM(
    model="meta-llama/Llama-2-70b-hf",
    enable_prefix_caching=True,
)

Gains 15-25% throughput on multi-turn by reusing cached attention.

Tune gpu_memory_utilization:

  • 7B: 0.95
  • 13B: 0.85
  • 70B: 0.90

SGLang Optimization

Use state graphs for multi-stage work:

sgl.gen(
  name="reasoning",
  max_tokens=500
)
sgl.gen(
  name="final_answer",
  max_tokens=200
)

One call instead of two. Latency drops.

Enable schedule caching:

backend.init_batch_state = True

TGI Optimization

Enable bfloat16 on capable hardware:

docker run -e HF_MODEL_QUANTIZE=bfloat16 ...

10-15% throughput gain on A100/H100. Quality barely changes.

llama.cpp Optimization

Enable GPU offloading for mixed CPU/GPU inference:

./main -m model.gguf -ngl 80 -p "Your prompt"

For CPU-only systems, enable multi-threading:

./main -m model.gguf -t 16 -p "Your prompt"

Benchmark Methodology

Setup

  • Llama 70B model
  • A100 80GB GPU(s)
  • Batch sizes: 1, 8, 32
  • Input context: 512 tokens
  • Output: 128 tokens
  • 10 runs per config

Throughput

Tokens/second across batches. Higher = better hardware use, lower cost.

Includes initialization, caching, tensor ops. Real workloads vary by context and sequence length.

Latency

TTFT: wall-clock time from request to first token. Lower = more responsive.

Per-token generation latency shows consistency under load.

Memory

Peak GPU memory during inference. Lower peak = smaller GPUs, higher batch sizes.

Includes model weights, activations, KV cache. Longer contexts = linear memory increase.

Selection Criteria

Throughput Priority

vLLM. Best for high request volumes. Queuing is fine.

Latency Priority

SGLang. Individual request speed matters. Real-time chat, interactive apps.

Ease of Deployment

TGI. Simple beats performance. Safety features, auto parallelism built in.

Edge and Offline

llama.cpp only. No other CPU option exists.

Maximum Performance

TensorRT-LLM. Compilation overhead worth it for high-volume work.

FAQ

Q: Throughput gap between vLLM and SGLang? vLLM is 15-20% faster in batch. For single-request interactive work, SGLang's lower latency wins perception-wise despite lower throughput.

Q: Run multiple engines together? Yes. Load-balance across them for migrations or A/B testing. Different models per engine is simpler.

Q: How much does engine choice affect cost? Engine impacts GPU utilization and throughput. Better throughput on same hardware = fewer GPU hours. Compounds at scale.

Q: TensorRT-LLM for production? Only if 20-30% throughput gain justifies compilation hassle. vLLM covers most teams.

Q: Does CPU inference scale? llama.cpp scales linearly with cores (65-75% efficiency). 16 cores = ~3x throughput of 4 cores.

Q: Which handles dynamic batch sizes? vLLM best. Handles variable arrivals without degradation. SGLang also good. TensorRT-LLM needs fixed batch at compile time.

Q: Switch engines without code changes? Most speak OpenAI API. vLLM, SGLang, TGI drop in.

Production Deployment Patterns

High-Availability Architecture

Production needs redundancy. Multi-region vLLM with load balancing. Survives infrastructure failures.

Typical setup:

  • Primary: 8x A100 with vLLM
  • Secondary: 4x A100 with vLLM on another cloud
  • Load balancer: 80/20 split

Cost: $19.44/hr + $9.72/hr = $29.16/hr, $21,287/month.

Gives you 99.9%+ uptime with failover.

Multi-Model Serving

Deploy multiple models. vLLM handles this via multiple instances or TensorRT-LLM model scheduling.

Common patterns:

  • 7B: fast, cheap
  • 70B: balanced
  • 405B: max capability

Route simple queries to small models, complex ones to large. Throughput climbs 40-60%.

Cost Optimization Through Scheduling

Batch at night and weekends. Spot instances drop 30-50%. Schedule non-urgent work (batch analysis, fine-tuning) for off-peak.

Example: 100M token summarization job nightly

  • Peak: $0.003/token = $300
  • Night: $0.0015/token = $150
  • Daily savings: $150

Annual savings: $54,750

Sources

  1. vLLM: Efficient Serving of LLMs (Zhou et al., 2024)
  2. SGLang: Efficient Execution of Structured Language Model Programs (Zheng et al., 2025)
  3. Text Generation Inference technical documentation
  4. llama.cpp implementation and benchmarks
  5. NVIDIA TensorRT-LLM documentation
  6. DeployBase.AI inference engine benchmarks