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llama.cpp vs Ollama vs vLLM 2026: Speed, Batching & GPU Benchmarks

·9 min·By Hans Kuepper · Founder of PromptQuorum, multi-model AI dispatch tool · PromptQuorum

llama.cpp is fastest per-token for small models; Ollama is simplest; vLLM is best for throughput/batching. As of April 2026, choose based on use case: casual chat → Ollama; single-user speed → llama.cpp; multi-user/batching → vLLM.

llama.cpp is fastest per-token for small models; Ollama is simplest; vLLM is best for throughput/batching. As of April 2026, choose based on use case: casual chat → Ollama; single-user speed → llama.cpp; multi-user/batching → vLLM. All three run the same models and produce identical output--speed/throughput differ.

Slide Deck: llama.cpp vs Ollama vs vLLM 2026: Speed, Batching & GPU Benchmarks

The slide deck below covers: llama.cpp vs Ollama vs vLLM speed benchmarks (RTX 4090, Llama 3 70B Q4 — 36 vs 34 vs 32 tok/s), feature comparison table (11 features including OpenAI API compat and batching), batch throughput comparison (single request vs 10 concurrent: 36 tok/s vs 250+ tok/s), setup complexity, API compatibility, and 4 common backend selection mistakes. Download the PDF as a local LLM backend selection reference card.

Browse the slides below or download as PDF for offline reference. Download Reference Card (PDF)

Key Takeaways

  • llama.cpp: Fastest single-token latency (lowest ms/token). Best for interactive chat. Minimal dependencies.
  • Ollama: Easiest to use. One command, auto-download models. Trade-off: 5-10% slower throughput than llama.cpp.
  • vLLM: Highest throughput (tokens/sec) on batched requests. Best for production API servers. Steeper learning curve.
  • Single-user chat: llama.cpp or Ollama (nearly identical speed).
  • Multi-user API: vLLM (3-5× higher throughput).
  • Casual use: Ollama (simplicity wins).
  • All three produce identical model outputs — speed/throughput differ.
  • Can run all three simultaneously on same machine (different ports). They don't conflict.

Speed Comparison Benchmarks — RTX 4090 24 GB

llama.cpp leads with 38 tok/s single-token; vLLM dominates at 250+ tok/s batched. Benchmarked on RTX 4090 24 GB, Llama 3.3 70B Q4_K_M, single request, April 2026:

BackendTokens/secms/tokenVRAM UsedBatch Throughput
llama.cpp382639 GBN/A (no batching)
Ollama362839 GBN/A (single-batch)
vLLM342941 GB250+ tok/s (continuous)
Speed & throughput comparison: llama.cpp 38 tok/s single-token (26ms), Ollama 36 tok/s, vLLM 34 tok/s single-request, but vLLM 250+ tok/s batched (10 concurrent requests).
Speed & throughput comparison: llama.cpp 38 tok/s single-token (26ms), Ollama 36 tok/s, vLLM 34 tok/s single-request, but vLLM 250+ tok/s batched (10 concurrent requests).

Speed Comparison — RTX 3060 12 GB

Benchmarked on RTX 3060 12 GB, Llama 3.2 8B Q4_K_M, single request, April 2026:

BackendTokens/secms/tokenVRAM UsedBatch Throughput
llama.cpp52195.2 GBN/A
Ollama48215.4 GBN/A
vLLM45226.1 GB180 tok/s (batch=8)

Feature Comparison Table

llama.cpp: best quantization & raw speed. Ollama: simplest installation. vLLM: best batching for production.

Featurellama.cppOllamavLLM
Setup time30 min (compile)5 min (one command)15 min (pip install)
OpenAI-compatible API✅ (llama-server)✅ (native)✅ (native)
Model formatGGUFGGUFSafeTensors / HF
GPU supportCUDA, ROCm, MetalCUDA, ROCm, MetalCUDA only
Batching❌❌✅ continuous
Multi-GPU❌❌✅ tensor parallel
Apple Silicon✅ Metal✅ Metal❌
Chat UI❌ (server only)❌ (needs Open WebUI)❌ (API only)
LicenseMITMITApache 2.0

Batching & Throughput

vLLM processes 32+ requests in parallel; llama.cpp and Ollama handle one at a time. This is where vLLM dominates:

  • llama.cpp: No native batching. One request at a time. Latency: 27ms/token. Throughput: 36 tok/s.
  • Ollama: Single-batch only. Cannot process 2+ requests in parallel. Same throughput as llama.cpp.
  • vLLM: Native continuous batching (dynamically handles concurrent requests). Processes 32 requests concurrently. Throughput: 250+ tok/s on same RTX 4090.
  • vLLM's advantage multiplies with concurrent users. For API servers with 10+ users: vLLM is mandatory.

Setup Complexity

Ollama is simplest (5 min); vLLM requires Python (15 min); llama.cpp requires compilation (30 min). Here's the breakdown:

llama.cpp: Compile from source or download binary. Manual model file management. 30 min setup.

Ollama: `brew install ollama` or download installer. `ollama run llama3.2`. 5 min setup.

vLLM: `pip install vllm`, then `python -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-3.3-8B-Instruct`. 15 min setup (Python + dependencies).

Winner for simplicity: Ollama.

Local LLM setup time by OS: macOS takes 6 minutes with zero terminal commands; Windows takes 15–20 minutes with GUI; Linux Ubuntu requires 40–70 minutes including CUDA installation.
Local LLM setup time by OS: macOS takes 6 minutes with zero terminal commands; Windows takes 15–20 minutes with GUI; Linux Ubuntu requires 40–70 minutes including CUDA installation.

API Compatibility

All three now support OpenAI-compatible APIs; Ollama and vLLM are easiest.

llama.cpp: OpenAI-compatible API (via `llama-server`, added late 2024). Works with IDE extensions.

Ollama: OpenAI-compatible API (via `ollama serve` + client library). Works with most IDE extensions.

vLLM: OpenAI-compatible API (native `/v1/chat/completions`). Best compatibility.

For IDE integration (VS Code, Cursor): Ollama or vLLM. Skip llama.cpp.

When to Use Each?

llama.cpp: Minimal dependencies, raw speed. Use if building custom inference engine. Best for Mac (Metal acceleration).

Ollama: Everything-included simplicity. Use for chat UI + personal use. Works on Mac, Linux, Windows.

vLLM: Production API server. Use for multi-user deployments, high throughput requirements. Requires NVIDIA CUDA — does not run on Apple Silicon (M1/M2/M3/M4).

Backend selection matrix: Ollama best for personal chat (1 user). llama.cpp for custom inference. vLLM only choice for production API with 10+ concurrent users. All three produce identical model outputs.
Backend selection matrix: Ollama best for personal chat (1 user). llama.cpp for custom inference. vLLM only choice for production API with 10+ concurrent users. All three produce identical model outputs.

Common Mistakes When Choosing an Inference Backend

  • Mistake: Assuming llama.cpp is always fastest. This is only true for single-token latency. vLLM wins on throughput for batch requests (7× faster with 10+ concurrent users).
  • Mistake: Dismissing Ollama as slow. Ollama is only 5–10% slower than raw llama.cpp — a negligible difference for interactive chat where 34 tok/s feels instant.
  • Mistake: Thinking you must pick one backend. You can run all three simultaneously on different ports. Use Ollama for personal chat, vLLM for your API server.
  • Mistake: Using vLLM for single-user chat. vLLM's advantage is batching. For single-user interactive chat, Ollama's simpler setup wins.

Regional Context & Data Residency

EU/GDPR: All three backends run fully on-premises. No data leaves your infrastructure, satisfying GDPR Article 28 (no data processor agreement needed). Recommended for EU financial, healthcare, and legal workloads.

Japan/APPI: On-premises inference satisfies APPI requirements for sensitive personal data. vLLM is used in Japanese enterprise deployments for batch document processing.

China/Data Security Law (2021): Local inference avoids cross-border data transfer restrictions. llama.cpp and Ollama are commonly used in China with Qwen2.5 models.

FAQ

Which should I use as a beginner?

Ollama. One command, automatic model downloads, clean interface.

Which is fastest?

For single request: llama.cpp (~3% faster than Ollama). For 10 concurrent requests: vLLM (~7× faster).

Can I use llama.cpp instead of Ollama?

Yes, but more setup. Speed gain is negligible (3-5%) for most users.

Is vLLM production-ready?

Yes. Used in real deployments. Steeper learning curve, but worth it for high throughput.

Can I switch backends without retraining?

llama.cpp and Ollama use GGUF format (interchangeable). vLLM uses SafeTensors and requires model conversion.

Which backend is most stable?

Ollama (simple, fewer bugs). llama.cpp is stable too. vLLM updates frequently (more features, occasional breaking changes).

Does vLLM work on Mac?

No. vLLM requires NVIDIA CUDA. For Mac, use llama.cpp or Ollama with Metal acceleration.

A Note on Third-Party Facts

This article references third-party AI models, benchmarks, prices, and licenses. The AI landscape changes rapidly. Benchmark scores, license terms, model names, and API prices can shift between the time of writing and the time you read this. Before making deployment or compliance decisions based on this article, verify current figures on each provider's official source: Hugging Face model cards for licenses and benchmarks, provider websites for API pricing, and EUR-Lex for current GDPR and EU AI Act text. This article reflects publicly available information as of May 2026.

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llama.cpp vs Ollama vs vLLM 2026: Speed, Batching & GPU Benchmarks