llama.cpp Experts in Germany
matched in minutes with vetted freelancers and the power of AIHire experts who can run local inference, convert models to GGUF, tune quantization, and wire llama.cpp into private assistants or edge apps. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Germany, who have recently used llama.cpp
Thomas Langer
Last position:
Consultant for AI-driven process automation at Lumiz
AI-driven automation of purchasing on a printing company's website, including selecting delivery times, order options, ordering, payment, and uploading print data from the Lumiz Cloud.
Minh Doan
Last position:
Project Manager / Business Analyst / Application Manager at Finance and Insurance
Introducing 5 different process applications for various teams
Release planning: scope and time management
Resource/capacity planning
Conducting sprint planning / retrospectives
Increment planning (multiple sprints)
Preparing steering committee meetings / reporting to the executive board
Coordinating / aligning with external suppliers / deliveries
Multi-project resource planning
Aligning with the business unit and development team
Identifying best practices with IBM BAW
Cost control and planning for the project team and external service providers
Collecting KPIs using LogScale
Analyzing application errors with LogScale / queries
Defining user stories / aligning requirements with the business unit and development team
Testing and defect tracking
UI/UX design of the application
Preparing and facilitating brown-paper workshop
Test concept, test data, test organization, test execution
Recording team velocity / metrics
Executing tests
Scripts for automated testing
Organizing tests with the business unit and IT
Recording and prioritizing defects
Setting up and operating the application
Setting up application monitoring with LogScale dashboards
Checking health endpoints with PowerShell
Post mortem analysis
Setting up incident management
Setting up problem management
Analyzing errors using LogScale queries and dashboard
Pre-processing data for AI
Conducting evaluation with AI language models (Meta Llama 3.3 LLM and deepset Haystack) and RAG
Installing runtime environments for LLMs (large language model)
Evaluating various LLMs
Installing RAG (retrieval augmented generation) and integrating with LLM
Extracting unstructured data with LLM and RAG
Project based on IBM BAW (Business Automation Workflow), WebSphere Liberty, Domea, d.3, REST, LogScale (formerly Humio), Swagger, PowerShell, JIRA, Confluence, Lucom Interaction Platform (LIP), Mattermost, Jabber
Lazaros Koutsianos
Last position:
RAG Webinar: Deep Dive and Use Cases at SHI GmbH
- Design, preparation and delivery of a webinar on 'RAG in Practice: How publishers create real value with AI'
- Preparing technical and strategic content on Retrieval Augmented Generation (RAG) for a mixed audience from the publishing industry
- Presenting specific use cases, technical backgrounds, common challenges and solution approaches when using RAG
- Providing practical insights into data preparation, model selection and output optimization in the context of digital publishing portals
- Conceptual and technical preparation of the webinar
- Selecting and presenting practical use cases from the publishing environment
- Developing technical backgrounds for implementing RAG systems
- Presenting and explaining typical challenges and solution strategies
- Large Language Models (LLMs)
- Retrieval Augmented Generation (RAG)
Jad Nohra
Last position:
Software Developer at Side Project
- Vram.run: Rust, TypeScript, HF Inference API with 19 providers, 220+ HW configs, and 30+ cloud GPUs. Search a model to see which API providers serve it, which GPUs can run it locally (and how fast), and what cloud rental would cost. Or search your hardware and see what fits. Also includes a Rust CLI.
- Psychotron: JavaScript, Web Audio API, AudioWorklet, Canvas 2D. Front-end for flash fiction audiobook with Web Audio DSP chain featuring pitch-shifting, 12-voice chorus, flanger, 13-band EQ, and convolver reverb. Includes a 2D canvas effect morphing engine and synchronized teleprompter.
- RecentWork: Swift, macOS, FSEvents, launchd. macOS daemon that watches project directories and maintains a flat folder of symlinks to recently modified files. Homebrew installable.
- Mini-llm: Bash, macOS, launchd, Ollama, llama.cpp, MLX, Open WebUI. Single command that turns a Mac Mini into a headless AI server.
- ThatSlop: JavaScript. Chrome/Firefox extension for AI content detection on LinkedIn and Twitter.
- Smux: Bash, tmux. Human-friendly tmux wrapper that is Homebrew installable.
- Learn Rust Course: Rust. Course on Rust’s memory model for C++ programmers, written from experience of transitioning from C++ to Rust at Irreducible.
Stephan Martin
Last position:
Sabbatical, professional development at Self-employed
- Further training in Snowflake and Google Looker
- Working with LLMs: local models (Llama, Mistral, Gemma, Phi, Qwen, DeepSeek, Bitnet, Flux, Whisper), OpenAI API, frontends (ollama, openwebui, loacalai)
- Inference methods: llama.cpp, vLLM, transformer
- Quantization, benchmarking, prompting
- LLM Agents (Tool/Function Calling, LangChain, LangGraph, MCP)
- Topics: attention, reasoning, chain of thoughts, RAG, GraphRAG, mlflow
- Cloud hosted: ChatGPT, Claude, Gemini
Markus Binder
Last position:
Technical Co-Founder at Loka AI
- Software development of a B2B SaaS for AI-based search in internal candidate pools of recruitment agencies
- Design of a multi-tenant, hybrid architecture with dedicated GPU servers and secure cloud integration
- AI-Engineering
- LLMOps
- Python
- FastAPI
Adithya Balaji
Last position:
Edge AI Software Engineer at Neura Robotics GmbH
- Deployed and optimized Vision-Language-Action (VLA) and diffusion policy models on NVIDIA Jetson Orin and Jetson Thor, meeting real-time inference latency targets for humanoid robot control loops.
- Built TensorRT engine pipelines (PyTorch → ONNX → TensorRT) with INT8/FP8 post-training quantization, calibration dataset design, and quantization-aware validation, reducing inference memory footprint by over 3× on Jetson without accuracy regression.
- Developed custom CUDA C++ plugins and CUDA Graphs for latency-deterministic, real-time policy execution – meeting hard runtime and memory constraints on embedded GPU targets.
- Developed an inference engine for VLA models on top of llama.cpp bringing different VLA policies under single runtime, packaging each as a single self-contained GGUF that needs no Python or PyTorch.
- Profiled and tuned GPU execution using NVIDIA Nsight Systems and Nsight Compute, identifying CUDA kernel bottlenecks, memory bandwidth saturation, and SM occupancy issues across Jetson Orin and Thor compute profiles for cross-layer performance optimization.
Discover over 15,000 top freelancers
Statistics of experts using llama.cpp
Aggregated from the professional profiles of matched freelancers.
Experience
20 years
Position duration
1.4 years
Positions per freelancer
16
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Automotive, Education
Certification focus areas
Information Technology, Business Intelligence, Project Management
Bachelor's degree or higher
100%
Master's degree or higher
60%
Doctorate
40%
Certifications per freelancer
3
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Germany using llama.cpp
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Runtime basics
llama.cpp is a lightweight C/C++ runtime for running LLMs on local hardware. It is used for private chat tools, offline assistants, embedded apps, and server deployments where control over data and latency matters.
Common uses
- Local inference for chat and retrieval tools
- Quantized model deployment in GGUF format
- CPU-first setups and mixed GPU acceleration
- Desktop, edge, and internal business apps
It is often chosen when teams want to avoid a heavy serving stack and keep model execution close to the app.
Ecosystem and files
llama.cpp centers on GGUF models, quantization formats, context handling, and prompt templates. Strong specialists know how to work with tokenizers, model conversion, batching, and the build flags that affect speed and memory use.
When freelance help fits
Companies bring in freelance experts when a prototype needs to become stable, when a model must fit strict memory limits, or when existing tools need to be adapted to a custom workflow. This is common in product teams, internal IT, and regulated environments in Germany that need local control and clear documentation.
What strong experts do
- Measure latency and memory use on real hardware
- Choose the right quantization level for the model
- Convert and test models before rollout
- Integrate llama.cpp with search, tools, and APIs
- Debug build, prompt, and context issues
A strong professional knows where quality comes from: clean model assets, reproducible builds, and careful runtime tuning.
Hiring in Germany
For teams in Germany, language needs and deployment rules often shape the setup more than the model itself. Experienced specialists can work remotely or on-site, document decisions clearly, and align the runtime with local infrastructure, security reviews, and internal release processes.
Frequently asked questions
Need clarity? These are the questions we hear most often about llama.cpp.
llama.cpp is used to run LLMs locally with a small and controllable runtime. Companies use it for private chat assistants, document search, offline tools, and embedded use cases where data should stay inside their own systems.
llama.cpp is usually picked when teams want a lean runtime and strong CPU support. Ollama adds a more packaged experience, while vLLM is often chosen for larger serving setups and higher-throughput GPU deployment. The right choice depends on whether you need local control, simple operations, or a more scaled serving stack.
A strong llama.cpp specialist usually knows model conversion, GGUF, quantization, prompt formatting, and basic performance tuning. It also helps to understand C/C++ builds, GPU backends, and how to connect the runtime to retrieval or API layers.
A simple proof of concept can start with a generalist who understands local model execution. Production work with llama.cpp needs someone who can read memory profiles, test model quality after quantization, and handle build or deployment issues without guesswork.
Yes, most llama.cpp work can be done remotely if the specialist can access the target environment and test models on the right hardware. For teams in Germany, remote collaboration is often enough for runtime tuning, while on-site support can help with secure infrastructure or internal rollout.
The main format to know with llama.cpp is GGUF, along with the conversion and quantization tools that prepare models for local use. Many specialists also work with tokenizers, prompt templates, benchmarking scripts, and integration code around the runtime.
Look for evidence that the person has shipped llama.cpp work beyond a demo. Good signs are clear notes on quantization choices, reproducible builds, measured latency and memory use, and the ability to explain trade-offs in plain words.
Bring in a llama.cpp expert when a model must run on limited hardware, when privacy rules block cloud inference, or when a prototype needs to move into a stable product. Freelance help is also useful when the team needs targeted support for model conversion, debugging, or deployment reviews.
The average hourly rate of freelancers in Germany who have used llama.cpp in their recent projects is 99 €, which corresponds to a daily rate of about 790 € based on an 8-hour working day.
Of the freelancers in Germany who have used llama.cpp in their recent projects, 100% hold at least a Bachelor's degree, 60% hold at least a Master's degree, and 40% hold a doctorate.
On average, freelancers in Germany who have used llama.cpp in their recent projects have 20 years of professional experience, with a single engagement typically lasting around 1.4 years.
The most common languages among freelancers in Germany who have used llama.cpp in their recent projects are German (100%), English (100%), and French (29%).
The most common industries among freelancers in Germany who have used llama.cpp in their recent projects are Information Technology (100%), Automotive (57%), and Education (57%).
The most common business areas among freelancers in Germany who have used llama.cpp in their recent projects are Information Technology (100%), Product Development (100%), and Research and Development (100%).
Main locations of FRATCH Experts, who have recently used llama.cpp
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
