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llama cpp prebuilt binaries
Every llama.cpp build is published on GitHub as a release tagged b plus a number, with
ready-made archives for each operating system and backend, so you can run llama.cpp without a
compiler by downloading the archive that matches your machine. For a service, the useful habit
is to pin one release, record the sha256 of the archive you use, and report the release at
runtime. jev itself does not load llama.cpp's runtime (it runs 8-bit OpenVINO weights and
compiles in only llama.cpp's tokenizer), but the jevos-v2 release ships the model as GGUF files,
and those are what you would run on a prebuilt llama.cpp.
Why it matters: a model's answers depend on two things, the GGUF file and the runtime that executes it. Most teams pin the first and let the second float. Pinning the runtime too turns "the model changed its answer" into a question you can actually investigate.
This page is what the releases offer, how to pick an archive, why and how to pin one, a pattern for verifying it, and when building from source is still the better choice.
llama.cpp's README lists "Download pre-built binaries from the releases page" among its install options, next to Docker and building from source. Each release carries archives grouped by platform and backend. On 2026-09-29 the list covered, among others:
- macOS: Apple Silicon (arm64) and Intel (x64).
- Linux (Ubuntu builds): CPU for x64, arm64 and s390x; Vulkan; CUDA 12 and 13; ROCm; SYCL; OpenVINO.
- Windows: CPU for x64 and arm64; CUDA 12 and 13; Vulkan; SYCL; ROCm; OpenCL for Adreno on arm64.
- Android, and an iOS XCFramework.
CUDA builds come with a separate archive of CUDA runtime libraries, so the machine needs an
NVIDIA driver but not a CUDA toolkit. The newest tag listed that day was b11240; releases are
frequent, which is exactly why pinning matters.
Match three things: operating system, processor (x64 or arm64) and backend.
- No GPU, or you want the CPU on purpose: the plain CPU archive. It is the smallest and has no driver dependencies; see running llama.cpp CPU only.
-
NVIDIA GPU: the CUDA archive plus its
cudartarchive, unpacked into the same folder. - Any other GPU, or unsure: Vulkan covers AMD, Intel and NVIDIA GPUs.
- Apple Silicon: the macOS arm64 archive, which uses Metal.
The Windows walk-through is on llama.cpp on Windows without compiling.
-
The API moves. llama.cpp keeps a changelog issue for the public
libllamaAPI that lists new functions, changed parameter structs, removals and renames. Anything that links against the library, including bindings, is written for a particular release. - The arithmetic changes. Releases change the code that computes the model. Nothing promises that the same GGUF gives identical probabilities on two releases, and we have not measured how far they move, so a threshold tuned on one release should be re-checked on the next.
- Audits need an answer. If a probability is questioned months later, "model file X on llama.cpp release Y" is a complete answer; "model file X on whatever was installed" is not. What else to record is on logging LLM decisions for audit.
A short pattern for a service:
-
One release, one commit. Keep the release tag (for example
b11081) and its commit as constants in your deployment. Code that binds to the library's structs, such as ctypes bindings, is transcribed from that release's headers, so for it the pin is not optional. - A hash per archive. Write down the sha256 of every archive you install, per operating system, processor and family. A download with a different hash is deleted and the install fails; nothing unverified is unpacked.
- Robust download. Resume interrupted transfers from the bytes already on disk, unpack into a staging folder, move into place only when the library is found, and refuse archive entries that would land outside the target folder.
- Reported at runtime. Have the service report the llama.cpp release, the commit, the device and the sha256 of the GGUF, so two servers can be shown to run the same model on the same runtime setup.
Because the archive hashes are known in advance, the same install works in an offline or air-gapped environment: download once, carry the runtime folder and the GGUF, check the hashes.
Being straight about it: prebuilt archives are the right default, not the only answer.
-
No archive for your platform. The archives cover the common operating systems and
processors. Elsewhere, build the commit you pinned, with
-DBUILD_SHARED_LIBS=ONif your own code loads the library. -
A backend the archives do not ship. llama.cpp supports more backends than it publishes
archives for, and can build several at once or as dynamically loaded plug-ins
(
GGML_BACKEND_DL). - A policy that requires source builds. Some organizations only run binaries they built. Then build the pinned commit and record your own hash.
In CI the same logic applies: cache the runtime folder keyed on the release, as sketched in running LLM yes/no checks in GitHub Actions.
Where do I download llama.cpp binaries? From the releases page of the ggml-org/llama.cpp repository on GitHub. Each tag has archives per platform and backend.
Which llama.cpp release should I use? For a new project, a recent one. For a running service, the one you tested with, pinned, until you have re-tested on a newer one.
Do the CUDA binaries need the CUDA toolkit? The releases ship the CUDA runtime as a separate archive; you need the NVIDIA driver.
Does jev use a prebuilt llama.cpp? No. jev runs 8-bit OpenVINO weights and compiles in llama.cpp's tokenizer; the prebuilt archives are for running the jevos GGUF files with llama.cpp directly.
See also: llama-cpp-python vs calling llama.cpp through ctypes, what is GGUF and self-hosted AI for decisions.
- Our own facts: what jev runs, and the GGUF files in the jevos-v2 release, from the jev repository.
- llama.cpp README and releases page, fetched 2026-09-29: install options, archive list, newest tag that day.
- llama.cpp release b11081, fetched 2026-09-29: tag, commit and asset names.
- libllama API changelog, issue 9289 and llama.cpp build guide, fetched 2026-09-29.
From the notes of jev, whose release ships the model as GGUF files for whichever llama.cpp release you pin.
- Ask a local LLM a yes/no question and get P(yes)
- Zero-shot text classification with yes/no questions
- LLM policy decisions: put the rule in the question
- LLM as a judge on a CPU
- Why a small LLM says yes when the answer is no
- Small LLMs and arithmetic in yes/no questions
- Our held-out benchmark said 0.855, new questions said 0.757
- jevos vs Jev vs Laya for yes/no decisions
- An open-source alternative to Jev for yes/no decisions
- jevos vs the OpenAI API for yes/no classification
- jevos vs Ollama for yes/no decisions
- jevos vs bart-large-mnli for zero-shot classification
- A yes/no LLM vs a fine-tuned BERT classifier
- jevos vs SetFit: zero-shot vs few-shot classification
- jevos vs Llama Guard for content safety checks
- jev serve vs llama.cpp server for classification
- jevos vs LM Studio: a decision server, not a chat app
- Local vs hosted LLM decisions: latency, cost, privacy
- A yes/no LLM vs a business rules engine
- LLM decisions vs keyword rules and regex
- The fastest AI model for yes/no decisions
- What makes a local LLM fast on a CPU
- Why one forward pass beats generating an answer
- Prefill vs decode: where LLM latency comes from
- Why LLM latency grows with the length of the text
- Why a hosted LLM API cannot answer in 50 ms
- Many questions about one text: why the extra ones are cheap
- CPU or GPU for a small LLM
- Latency budgets: where a 200 ms model fits
- Measuring LLM latency: median, p90 and warm-up
- Q4_K_M vs Q8_0: speed and size for a small model
- Throughput vs latency for a decision server
- What P(yes) means, and what it does not
- LLM calibration explained with yes/no answers
- Expected calibration error (ECE), explained
- Temperature scaling for LLM probabilities
- Platt scaling for a yes/no model
- Reading a reliability diagram
- How to choose a threshold for P(yes)
- Thresholds when a wrong yes costs more than a wrong no
- Human in the loop AI with a review band
- Precision and recall at a P(yes) threshold
- Base rates: why a 0.9 yes can still be wrong often
- Combining yes/no answers with AND, OR and NOT
- Logits, log-odds and P(yes)
- LLM confidence scores: probabilities vs self-reports
- How to write yes/no questions an LLM answers well
- Negation in yes/no questions for an LLM
- One condition per question: splitting compound questions
- Ask whether the text says it at all
- Scores as yes/no thresholds: is it at least high?
- Sending JSON as the text: designing the state
- Why wording changes an LLM's answer, and how to test it
- Mainly about: questions for messages with several topics
- Yes/no questions about tone and emotion
- Asking about intent: what does the writer want?
- Yes/no questions about long documents
- Using an English-only LLM with other languages
- Content moderation with a local LLM
- A Discord moderation bot with a local LLM
- Spam detection with yes/no questions
- Review moderation with a local LLM
- Email triage with a local LLM
- Support ticket routing with yes/no questions
- Urgency detection in customer messages
- Sentiment analysis with yes/no questions
- Intent detection with a local LLM
- Lead qualification with yes/no questions
- Fraud case triage with a local LLM
- Phishing email screening with a local LLM
- Log and alert triage with a local LLM
- Checking text for personal data with yes/no questions
- Prompt injection screening with a small model
- Document classification with a local LLM
- Product categorization with yes/no questions
- Contract clause detection with a local LLM
- Refund request triage with a local LLM
- Detecting cancellation intent in customer messages
- RAG evaluation with yes/no questions
- RAG faithfulness check with a local LLM
- Hallucination detection with a local LLM
- LLM regression tests in CI with yes/no checks
- Rubric design for an LLM judge
- Pairwise comparison with a yes/no judge
- LLM judge bias and how to control it
- Evaluation metrics for yes/no classifiers
- Building a yes/no test set for your own data
- Accuracy by kind of question: why one number hides failures
- Generating test questions with answers computed by code
- Benchmark contamination and truly held-out tests
- An LLM router with yes/no questions
- A model cascade: small model first, large model on doubt
- Semantic routing vs yes/no questions
- Gating AI agent tool calls with yes/no checks
- AI agent guardrails with yes/no questions
- Stop conditions for AI agents
- Logging LLM decisions for audit
- Reducing LLM cost with local yes/no decisions
- Replacing chat LLM calls with yes/no questions
- Structured output vs a probability
- A Python client for local LLM decisions
- Calling a local LLM decision server from JavaScript
- Local LLM yes/no decisions in n8n
- A Slack bot that uses local LLM decisions
- Home Assistant automations with local LLM decisions
- A LangChain tool for local yes/no decisions
- Batch decisions from files with jev decide
- Running LLM yes/no checks in GitHub Actions
- Securing a local LLM server with an API key
- curl examples for a local LLM decision API
- Self-hosted AI for decisions
- A private LLM for text classification
- On-premise LLM for business decisions
- GDPR and automated decision-making with an LLM
- Offline AI for decisions: no network needed
- Edge AI decisions on a CPU
- Run an LLM locally without a GPU
- Small language models explained
- When a small model is enough, and when it is not
- An LLM on a laptop: what it can do in real time
- What is GGUF, for someone deploying a classifier
- GGUF quantization types explained: Q4_K_M, Q8_0 and others
- GGUF vs safetensors
- llama.cpp vs Ollama for a classification service
- llama-cpp-python vs calling llama.cpp through ctypes
- llama.cpp on Windows without compiling
- Running llama.cpp CPU only
- Using llama.cpp prebuilt binaries instead of building