llama.cpp vs vLLM comparison
VERDICT
| Tool | Context window | Speed | Cost | Best for | Free tier |
|---|---|---|---|---|---|
| llama.cpp | Up to ~8K tokens (model-dependent) | CPU-optimized, moderate speed | Free (open-source) | Local CPU inference, low resource setups | Fully free |
| vLLM | Up to 32K+ tokens | GPU-accelerated, very fast | Free (open-source) | High-throughput GPU inference, large context | Fully free |
| OpenAI API (for reference) | Up to 32K tokens | Cloud GPU, scalable | Paid API | Production cloud LLM access | No free tier |
| Ollama (local alternative) | Up to 8K tokens | Local GPU/CPU | Free | Local inference with GUI | Fully free |
Key differences
llama.cpp is a CPU-focused inference library for running quantized LLaMA models locally with modest context windows, ideal for offline or low-resource environments. vLLM is a GPU-accelerated inference engine optimized for large context windows (up to 32K tokens or more) and high throughput, supporting batch requests and streaming. llama.cpp uses GGUF quantized models and is lightweight, while vLLM requires a CUDA-enabled GPU and supports advanced scheduling and sampling.
Side-by-side example with llama.cpp
Run a local LLaMA model with llama.cpp Python bindings using a GGUF model file and generate a chat completion.
from llama_cpp import Llama
import os
llm = Llama(model_path="./models/llama-3.1-8b.Q4_K_M.gguf", n_ctx=2048)
prompt = "Hello, how are you?"
output = llm(prompt, max_tokens=128)
print(output["choices"][0]["text"])Hello! I'm doing well, thank you. How can I assist you today?
Equivalent example with vLLM
Use vLLM Python API to run the same prompt with GPU acceleration and streaming support.
from vllm import LLM, SamplingParams
import os
llm = LLM(model="meta-llama/Llama-3.1-8B-Instruct")
prompt = "Hello, how are you?"
outputs = llm.generate([prompt], SamplingParams(temperature=0.7, max_tokens=128))
print(outputs[0].outputs[0].text)Hello! I'm doing well, thank you. How can I assist you today?
When to use each
Choose llama.cpp when you need local, offline inference on CPU with smaller models and moderate context windows. It is ideal for desktop apps, experimentation, or environments without GPUs. Choose vLLM when you require fast, scalable inference on GPUs with large context windows, batch processing, or streaming, suitable for production services and high-throughput applications.
| Use case | llama.cpp | vLLM |
|---|---|---|
| Local CPU inference | Excellent | Not applicable |
| Large context windows | Limited (~8K tokens) | Supports 32K+ tokens |
| GPU acceleration | No | Yes |
| Batch processing | No | Yes |
| Streaming output | Limited | Yes |
| Ease of setup | Simple, minimal dependencies | Requires CUDA GPU and drivers |
Pricing and access
Both llama.cpp and vLLM are open-source and free to use. llama.cpp runs locally without API keys or cloud costs. vLLM requires a CUDA GPU but no paid API. For cloud-hosted LLMs with large context windows, consider paid APIs like OpenAI or Anthropic.
| Option | Free | Paid | API access |
|---|---|---|---|
| llama.cpp | Yes (fully open-source) | No | Local only |
| vLLM | Yes (open-source) | No | Local GPU only |
| OpenAI API | No | Yes | Cloud API |
| Anthropic API | No | Yes | Cloud API |
Key Takeaways
- llama.cpp excels at local CPU inference with quantized LLaMA models and moderate context windows.
- vLLM provides fast, GPU-accelerated inference with large context and batch support for production use.
- Use llama.cpp for offline or low-resource setups; use vLLM for scalable, high-throughput GPU inference.
- Both tools are free and open-source but target different hardware and workloads.
- For cloud APIs with large context and managed infrastructure, consider OpenAI or Anthropic instead.