Ollama 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.
Ollama
Local model runner with CLI and REST API for open-weight LLMs
strata
Runs a 125B-parameter MoE model on one consumer GPU plus system RAM
Verdict
Ollama ranks higher, 80 to 77. Of the 5 score parts we measure for both, Ollama leads on 2, strata on 2 and 1 is even.
- AdoptionOllama leads by 38 points, 99 to 61: a wide gap.
- FreshnessEven at 100.
- Maintenancestrata leads by 16 points, 98 to 82: a wide gap.
- Easy to runstrata leads by 17 points, 67 to 50: a wide gap.
- Agent-readyOllama leads by 40 points, 70 to 30: a wide gap.
Pick Ollama if you are
Developers and self-hosters who want a local model API behind their apps
- One-line installers for macOS, Windows and Linux, plus an official Docker image
- REST API at /api/chat with official Python and JavaScript clients
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 | Ollama | strata |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 99, widely used | 61, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 82, healthy | 98, healthy |
| Easy to run | 50, easy | 67, easy |
| Agent-ready | 70, partly | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 183.9k | 28.3k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Go | C++ |
| Docker | Yes | Yes |
| GPU | Not stated | Required |
| arm64 or Apple Silicon | Mentioned | Mentioned |
Ollama
Ollama downloads and runs open models from its library (for example Gemma 4) through a CLI and a REST API on localhost:11434. It uses llama.cpp as its backend and ships official Python and JavaScript client libraries. The `ollama launch` command connects it to tools such as Claude Code, Codex, OpenCode and OpenClaw.
Who it is for: Developers and self-hosters who want a local model API behind their apps
Strengths
- One-line installers for macOS, Windows and Linux, plus an official Docker image
- REST API at /api/chat with official Python and JavaScript clients
- Large community integration list: chat UIs, editors, RAG tools and SDKs in many languages
- Modelfile and model import let you customize or bring your own weights
Weaknesses
- README gives no RAM, VRAM or GPU requirements per model
- Backend is llama.cpp only, per the README's supported backends list
- No built-in web chat UI or multi-user accounts mentioned; needs a third-party front-end
- README mentions cloud models but does not detail how they work or what they cost
- Docker
- Needs llama.cpp
- Models: Ollama model library, Gemma 4, Modelfile, model import
- port 11434
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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