Llama
Private, Customizable AI That You Fully Own
Software Pro, headquartered in NYC, helps enterprises deploy Meta's Llama models privately on their own infrastructure. The Llama family gives enterprises the power of frontier AI without the cloud dependency. We deploy, fine-tune, and scale Llama models on your infrastructure, enabling HIPAA-compliant, air-gapped, and cost-efficient generative AI for any use case.
from vllm import LLM, SamplingParams
llm = LLM(
model="meta-llama/Llama-3.1-70B-Instruct",
tensor_parallel_size=2,
)
params = SamplingParams(temperature=0.7, max_tokens=1024)
output = llm.generate(
["Summarize the Q3 report."],
params,
)
print(output[0].outputs[0].text)What Llama Can Do for Your Business
Production Llama systems shipped on customer-controlled hardware where data residency, cost at scale, and air-gapped operation matter.
Private On-Premises Deployment
Run Llama entirely on your own infrastructure, whether that is an AWS VPC, on-prem GPU cluster, or air-gapped environment. Zero data ever leaves your network.
Fine-Tuning & Domain Adaptation
Fine-tune Llama on your proprietary data using LoRA, QLoRA, and full fine-tuning techniques. Build models that understand your domain, terminology, and workflows.
Retrieval-Augmented Generation
Combine Llama with your knowledge base via RAG pipelines using vector search, hybrid retrieval, and re-ranking for accurate, grounded responses.
Cost-Efficient Inference
Eliminate per-token API costs at scale. Quantized Llama models running on commodity hardware can deliver inference costs 10 to 50 times lower than cloud APIs.
HIPAA & Air-Gap Ready
Deploy in environments with strict data sovereignty requirements. Full audit trails, no external API calls, and compliance-ready architecture out of the box.
Multi-Model Orchestration
Route tasks intelligently between Llama model sizes, using smaller quantized models for speed-critical tasks and larger models for complex reasoning.
Your Open-Weight Model Questions, Answered.
Honest answers on how Llama and other open-weight models actually compare to frontier models on real production workloads.
How do open-weight models like Llama compare to frontier models on real production tasks?
Get an open-weight versus frontier comparison for your workload.
Talk to a Llama engineerIndustry Use Cases
How regulated and cost-conscious teams deploy Llama for private document AI, on-prem chat, and high-volume workloads.
HIPAA-Compliant Clinical AI
Deploy medical AI that never sends PHI to external APIs. Private Llama deployments for clinical note summarization, prior auth automation, and EHR data extraction.
On-Premises Legal Intelligence
Fine-tune Llama on your firm's case history and legal corpus. Build AI that understands your practice areas with no privileged data ever leaving the firm.
Air-Gapped AI for Classified Environments
Deploy generative AI in classified and sensitive environments where no external internet connectivity is permitted. Full operational control with zero cloud dependency.
Proprietary Financial Model Intelligence
Fine-tune on internal research, earnings calls, and analyst reports. Build private AI that speaks your firm's language without model training agreements with cloud vendors.
How We Build With Llama
A proven Llama deployment process from hardware sizing to vLLM serving, fine-tuning, and production observability.
Infrastructure Assessment
Evaluate your compute environment including GPU inventory, networking, and storage, then design the optimal Llama deployment configuration.
Model Selection & Quantization
Select the right Llama variant (8B, 70B, 405B) and quantization strategy (INT4, INT8, FP16) for your latency and hardware constraints.
Fine-Tuning Pipeline
Prepare training datasets, implement LoRA/QLoRA fine-tuning, and run evaluation benchmarks against your target tasks.
RAG & Knowledge Integration
Build the retrieval pipeline with your document corpus, embedding model, and vector store for grounded, accurate responses.
Production Serving & Scaling
Deploy with vLLM or TGI inference servers, autoscaling, load balancing, and full monitoring dashboards.
Works With Your Existing Stack
We integrate Llama with your VPC, your inference cluster, and the observability and security layers your team already runs.
NYC's Leading Llama Development Team
Why regulated and on-prem teams pick our engineers when self-hosted Llama needs to behave like a managed product.