llama.cpp
The C++ inference engine powering most local LLMs.
Open SourceCPU-capable
Actually FreeNo SignupOpen SourceWatermark-Free
vLLM
High-throughput LLM serving for GPUs.
Open Source 24–80 GB VRAMSelf-hosted server
Actually FreeNo SignupOpen SourceWatermark-FreeAPI
Visit vLLMUpdated 2026-05-14 · Direct link
Hardware requirements
Self-hosted server · Datacenter GPU (80 GB+)
Min VRAM
24 GB
Rec. VRAM
80 GB
Min RAM
64 GB
Rec. RAM
256 GB
Disk
500 GB
GPU class
Datacenter GPU
CUDA 12.xNo Apple SiliconGPU RequiredQuant: FP16, BF16, FP8 +3
For desktops you usually want Ollama / llama.cpp instead.
Screenshot placeholder · vLLM
What is vLLM?
Production-grade inference server: PagedAttention, continuous batching, tensor parallelism. The standard when you need to serve thousands of requests/sec on H100s/A100s/L40s.
Pros & cons
Pros
- Best throughput in OSS
- Tensor parallel across many GPUs
- OpenAI-compatible server
Cons
- CUDA-only realistically
- Aimed at servers, not desktops
What's actually free?
Free / OSS. Run it on your own datacenter GPUs.
✓ Actually FreeNo SignupOpen SourceWatermark-Free