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llama.cpp
Pricing
- Model
- Free
Summary
Cloud inference bills arrive at the end of the month — privacy violations arrive before you notice them. llama.cpp exists for the teams who cannot afford either.
llama.cpp is a C/C++ inference engine that runs quantized LLMs entirely on local hardware, from an Apple Silicon laptop to an H100 cluster to a Jetson edge device, using the same binary and the same hand-tuned kernels across all of them. No API keys, no telemetry, no requests leaving the machine. It exposes an OpenAI-compatible server via `llama serve`, which means drop-in compatibility with tooling already pointed at OpenAI endpoints. The ceiling appears when you need the inference engine to do more than infer — there is no planning loop, no tool-calling orchestration, no agent layer built in. Teams building autonomous workflows bolt on a framework on top, which means they are maintaining two systems.
Bottom line: The right call for a privacy-gated deployment on CPUs or edge hardware — the wrong expectation if you want the inference layer to also run your agent loop.
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Pros
Sign in to edit- OpenAI-compatible server endpoint via `llama serve`, so existing client code pointed at the OpenAI API redirects to localhost without rewriting integration logic.
- GGUF quantization support across 4-bit to full precision, which means a 27B-parameter model runs on a single consumer GPU — without it, that model requires data-center hardware or a paid API.
- Single binary with hand-tuned kernels for Apple Silicon, NVIDIA, AMD, Intel Arc, and CPU, so a heterogeneous hardware fleet runs the same inference stack without per-target build pipelines.
- Zero telemetry and zero outbound requests by design, which means organizations with data-residency or compliance requirements can run frontier models without a legal review of what leaves the network.
- MIT license with no paid tier or hosted service, so there is no usage ceiling, no rate limit, and no cost that scales with inference volume.
Cons
Sign in to edit- llama.cpp provides no agent orchestration — no planning loop, no tool-use management, no branching on model output. Teams building agents must add a separate framework on top, which means debugging inference failures and orchestration failures in two different systems.
- Quantization introduces accuracy degradation that is model- and task-specific and requires empirical validation per deployment. Teams shipping to production benchmark every quantization level against their specific task — there is no general answer, and the work is not reusable across model updates.
- When inference throughput at scale becomes the primary constraint — high-concurrency production APIs serving hundreds of simultaneous requests — teams move to dedicated serving infrastructure such as vLLM or TGI, which implement continuous batching and paged attention optimizations that llama.cpp does not provide. At that point, llama.cpp remains useful in development but is no longer the production inference layer.
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About
- Platforms
- Linux, macOS, Windows, Android, ChromeOS, iOS, Web (WebGPU)
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- 2026-06-09T12:11:40.723Z
Best For
Who it's for
- Developers needing low-latency, local model inference
- Organizations with privacy or compliance requirements
- Edge deployment on CPUs, older GPUs, or mobile devices
- Quantization-aware workflows and model optimization
- Multi-hardware environments requiring portable binaries
What it does well
- Running proprietary LLMs locally without cloud inference costs
- Building offline AI assistants on personal devices or edge hardware
- Fine-grained control over model inference parameters and memory trade-offs
- Integrating LLM inference into applications requiring data privacy
- Leveraging quantized models on resource-constrained hardware
Integrations
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Frequently Asked Questions
- Is llama.cpp free?
- Yes — llama.cpp is fully free to use. There is no paid tier.
- Is llama.cpp open source?
- Yes. llama.cpp is open source.
- Does llama.cpp have an API?
- Yes. llama.cpp exposes a developer API. See the official documentation at https://llama.app for details.
- Can I self-host llama.cpp?
- Yes. llama.cpp supports self-hosting on your own infrastructure.
- When was llama.cpp released?
- llama.cpp was first released in 2023.
- What platforms does llama.cpp support?
- llama.cpp is available on: Linux, macOS, Windows, Android, ChromeOS, iOS, Web (WebGPU).
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Curated lists that include this category
Cloud inference costs money per token and ships your data to someone else’s server. llama.cpp removes both constraints by running quantized large language models directly on your hardware, with no runtime dependency on any external service. The core workflow is a single binary: `llama serve` starts an OpenAI-compatible HTTP server, your existing client code points at localhost instead of api.openai.com, and the model runs on whatever compute you have available. Quantized weights — GGUF format — let you trade a controlled amount of accuracy for dramatically reduced memory footprint, which is what makes running a 27B-parameter model on a single consumer GPU practical.
The differentiating feature is hardware portability without recompilation. The same binary runs hand-tuned kernels on Apple Silicon, NVIDIA RTX and A100, AMD Radeon, Intel Arc, and plain CPU — including edge boards like the NVIDIA Jetson. The vendor’s page lists the full matrix explicitly. Teams deploying across a heterogeneous fleet do not need per-target build pipelines or driver-specific inference libraries. For compliance environments where the entire inference chain must stay on-premises, this is the architectural property that matters most.
llama.cpp fits anywhere inference itself is the bottleneck concern: air-gapped networks, HIPAA-adjacent workloads, cost-sensitive edge deployments, and local developer environments where spinning up a cloud sandbox is slower than running the model. It does not fit when the thing you are building needs the inference engine to also manage task planning, tool-use loops, or multi-step agent coordination. That layer is not here. Frameworks like LangChain, LlamaIndex, or custom orchestration code sit on top of llama.cpp — llama.cpp is the engine, not the driver.
The docs describe a plugin path pairing `llama serve` with Pi, a local coding agent that auto-discovers the running model with no additional configuration. Model discovery on Hugging Face is supported directly, with featured models ranging from sub-5B options designed for phones and low-end laptops up to 198B MoE models with small active-parameter counts that fit on modest hardware.