llama.cpp vs strata
Two of the top projects in Model serving, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
llama.cpp
C/C++ inference engine serving GGUF models over an OpenAI-compatible API
strata
Runs a 125B-parameter MoE model on one consumer GPU plus system RAM
Verdict
llama.cpp ranks higher, 81 to 77. Of the 5 score parts we measure for both, llama.cpp leads on 2, strata on 2 and 1 is even.
- Adoptionllama.cpp leads by 36 points, 97 to 61: a wide gap.
- FreshnessEven at 100.
- Maintenancestrata leads by 8 points, 98 to 90.
- Easy to runstrata leads by 17 points, 67 to 50: a wide gap.
- Agent-readyllama.cpp leads by 40 points, 70 to 30: a wide gap.
Pick llama.cpp if you are
Engineers who want a lean local inference server
- Plain C/C++ with no runtime dependencies; prebuilt binaries and Docker
- Backends for NVIDIA, AMD, Apple Metal, Intel SYCL, Vulkan, Ascend, Moore Threads
Pick strata if you are
Self-hosters with a 12 GB+ gaming GPU and 32 GB+ RAM wanting a large local model
- Serves OpenAI /v1, Anthropic /v1/messages and Responses /v1/responses on 127.0.0.1:8080
- Reports 53-94 tokens/s decode on an RTX 5070 12 GB, depending on quantization
| What we compare | llama.cpp | strata |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 97, widely used | 61, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 90, healthy | 98, healthy |
| Easy to run | 50, easy | 67, easy |
| Agent-ready | 70, partly | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 131.7k | 28.3k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | C++ | C++ |
| Docker | Yes | Yes |
| GPU | Optional | Required |
| arm64 or Apple Silicon | Mentioned | Mentioned |
llama.cpp
llama.cpp is a C/C++ inference engine for LLMs and VLMs with no dependencies, built on ggml. llama serve starts an OpenAI-compatible API server with a built-in web UI, pulling GGUF models straight from Hugging Face, with 1.5 to 8-bit quantization and CPU+GPU hybrid offload for models larger than VRAM. Backends cover CUDA, HIP, Metal, Vulkan, SYCL, OpenCL, CANN, MUSA and WebGPU.
Who it is for: engineers who want a lean local inference server
Strengths
- Plain C/C++ with no runtime dependencies; prebuilt binaries and Docker
- Backends for NVIDIA, AMD, Apple Metal, Intel SYCL, Vulkan, Ascend, Moore Threads
- Hybrid CPU+GPU offload runs models larger than available VRAM
- Built-in web UI and OpenAI-compatible server via llama serve
Weaknesses
- GGUF model format only
- README gives no port, auth or sizing guidance; see tools/server docs
- Install script is curl piped to sh; otherwise build from source
- OpenVINO backend still in progress
- GPU optional
- Docker
- Models: GGUF models from Hugging Face (e.g. Qwen3.5-0.8B-GGUF)
strata
Strata is a C++ inference engine, built on parts of llama.cpp/ggml, that runs Qwen3.8-Flash-Next on a PC with a 12 GB or larger NVIDIA or AMD card. It keeps the most-used experts in VRAM, holds all of them in RAM and uses the CPU for the rest. It serves a browser chat app plus OpenAI-, Anthropic- and Responses-style APIs on port 8080.
Who it is for: Self-hosters with a 12 GB+ gaming GPU and 32 GB+ RAM wanting a large local model
Strengths
- Serves OpenAI /v1, Anthropic /v1/messages and Responses /v1/responses on 127.0.0.1:8080
- Reports 53-94 tokens/s decode on an RTX 5070 12 GB, depending on quantization
- Installer picks a model size by RAM; interrupted downloads resume
- Ships a BENCH script, an MCP server for setup, and multi-GPU support
Weaknesses
- Built around one model family (Qwen3.8-Flash-Next), not arbitrary GGUFs
- Needs 32 GB or more RAM and about 80 GB disk; PC may freeze 1-3 minutes at load
- Answers one request at a time by default; parallel mode slows each answer on 12 GB cards
- Intel Arc, Mac, older GPUs and Strix Halo support is experimental and community-tested
- RAM ≥ 32 GB
- GPU required
- Docker + Compose
- Needs NVIDIA or AMD GPU driver, Qwen3.8-Flash-Next model download (about 70 GB)
- Models: Qwen3.8-Flash-Next GGUF (Q2_0, IQ2_XS, IQ3_XXS, IQ3_S, Coder, Swift 1.5), Unsloth UD-IQ4_XS to Q8_0
- port 8080
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