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cllm vs llama.cpp
C/C++ inference engine — host-OS
llama.cpp is the engine. cllm is the kernel that will run it. We borrow the v1 HTTP API shape and the model loading interface; we replace the host OS underneath.
| Feature | cllm | llama.cpp | Advantage |
|---|---|---|---|
| Host operating system | None (unikernel) | Linux, macOS, Windows | Comparable |
| Inference path | Skeleton; engine integration on roadmap | Mature C/C++ engine with ggml backend | llama.cpp |
| HTTP API | llama.cpp-shaped v1 endpoints | Native llama-server with v1 endpoints | Comparable |
| Model formats | GGUF (planned via llama.cpp path) | GGUF + legacy formats | llama.cpp |
| Target architecture | i386 (Multiboot), QEMU + bare-metal | x86_64, ARM64, more | llama.cpp |
| GPU support | Roadmap (CUDA design only) | CUDA, Metal, Vulkan, ROCm, SYCL | llama.cpp |
| Attack surface | One ELF, no userspace, no shell | Whatever the host OS exposes | cllm |
| Boot time | Milliseconds from Multiboot | Seconds to bring up the OS first | cllm |
| Memory footprint | 4 MB heap arena today | Tens of MB minimum process RSS | cllm |
| Debuggability | GDB on :1234 via make run-debug | Mature debugger and profiler ecosystem | llama.cpp |
Pick cllm when
- ▸You want a minimal serving substrate with no host OS to maintain
- ▸You care about boot time, attack surface, and image size — not portability across desktop OSes
- ▸You are building an inference appliance and would rather ship one ELF than a Linux distribution
- ▸You want to be able to read every line of code between the NIC and the model
Pick llama.cpp when
- ▸You need inference running today, on real weights, against real benchmarks
- ▸You want broad model coverage and active upstream contribution
- ▸You run on macOS, Windows, or non-x86 Linux where cllm has no target yet
- ▸You depend on community-built quantizations, GGUF tooling, and downstream wrappers
Still deciding?
cllm and llama.cpp solve different layers of the same problem. Reading the source for both is the fastest way to know which one belongs in your stack.