We build with LLaMA
We use LLaMA to build and fine-tune open-weight language models for custom, private AI applications that support your business goals. From fine-tuning and prompt design to inference optimization and deployment, we create products designed to evolve with your needs.
DISCUSS YOUR PROJECTOpen-Weight Models
Downloadable weights you can self-host and fine-tune freely.
Full Data Control
Keep sensitive data on-premise instead of third-party APIs.
Cost-Efficient Scale
Choose model sizes that balance capability against inference cost.
BENEFITS OF LLAMA technology
We use Llama to self-host and fine-tune models, keep data private, and control inference costs at scale.
BUILD
[01]- Fine-tune model weights
- Select parameter sizes
- Configure quantization settings
- Set up local inference
ENGAGE
[02]- Serve models on-premise
- Power private AI features
- Handle sensitive workloads
- Support offline inference
GROW
[03]- Scale across model sizes
- Optimize inference costs
- Extend with fine-tuned variants
- Reduce third-party API costs
Our Llama Technology Stack
We combine Llama with Hugging Face Transformers, vLLM, Ollama, and quantization tooling for fine-tuning, efficient serving, GPU resource planning, and on-premise deployment across your infrastructure.
Custom Llama development company
With our Llama development services, we build private, cost-efficient generative AI applications powered by Meta's open-weight language models, tailored to your boldest business goals. Having years of experience with Llama, our engineers harness its full potential to fine-tune, self-host, and deploy models across on-premise and private-cloud environments, for industries and company sizes that need full control over their data and infrastructure. Whether it's building a new AI-powered application from scratch or migrating an existing product off third-party APIs, we design deployments that balance capability, latency, and cost. Our range of Llama development services spans consulting, fine-tuning, model deployment, and ongoing optimization. We work as your technical partner and ensure complete transparency and comfortable communication throughout the process, bringing deep machine learning expertise and a pragmatic approach to model selection and infrastructure design. All this to make sure your AI features stay fast, private, and cost-effective as usage grows, and move your business forward.
OUR LLAMA SERVICES
We build, fine-tune, and support Llama models around your product goals.
Your roadmap is growing faster than your team. Add senior engineering capacity and deliver more without sacrificing quality.
Fine-tune Llama on your own data and use cases for domain-specific accuracy, tone, and behavior.
Private, on-premise or private-cloud inference infrastructure built around your latency and compliance needs.
Right-size model parameters and quantization to balance inference cost against capability.
Migrate AI features off third-party APIs onto self-hosted, fine-tuned Llama models.
We Turn Technology Into Results
Partner with a team that blends technical precision, creative design, and business insight. We’ll help you launch, scale, and dominate your digital niche.
Frequently Asked Questions
Common questions about how we use Llama and what it can bring to your project. Have a specific requirement?
How does SoftDoes use Llama?
We use Llama to build private, cost-efficient generative AI applications, fine-tuning and self-hosting Meta's open-weight models around your data residency, latency, and infrastructure requirements.
What types of applications do you build with Llama?
We build private chat assistants, internal knowledge tools, domain-specific copilots, and on-premise generative AI features for teams that need control over their data and models.
Can Llama run on our own infrastructure?
Yes. Because Llama's weights are openly downloadable, we can deploy it on-premise or in your private cloud, using tools like vLLM or Ollama for efficient serving.
Do you fine-tune Llama on our data?
Yes. We fine-tune Llama on your own datasets using Hugging Face Transformers and related tooling, so the model reflects your domain, tone, and use cases.
How do you choose the right Llama model size?
We weigh inference cost and hardware requirements against the capability your use case needs, then select a parameter size and quantization approach accordingly.
When should we use Llama instead of a hosted API like OpenAI?
Llama fits well when data residency, customization, or cost at scale matter more than convenience. If you need the fastest path to a general-purpose model with no infrastructure, a hosted API may still be the better fit.
How do you decide whether Llama fits a project?
We look at your data sensitivity, latency requirements, expected usage volume, and team's infrastructure experience, then confirm Llama is the right fit before starting development.
