Technology
LLaMA Development — Self-Hosted Open Models for Private AI
Self-host Meta’s LLaMA family for private, controllable, and cost-predictable AI — on your VPC or our managed infrastructure.
What we build with LLaMA
- LLaMA 3.x and successor deployment on AWS, GCP, Azure, or on-prem
- LoRA, QLoRA, and full fine-tuning on domain data
- vLLM, TGI, and Triton serving with PagedAttention
- Quantization (GPTQ, AWQ, GGUF) for cost and latency trims
- RAG pipelines that keep all data inside your VPC
- Multi-tenant serving with rate limiting and per-tenant quotas
Why DiveScale
Built by engineers who ship LLaMA in production
Open-weight models like LLaMA shine when data must stay private, when costs at scale beat hosted APIs, or when regulators require on-prem deployment. DiveScale operates LLaMA in production for clients who need control without giving up output quality.
We handle the hard parts: choosing the right LLaMA size for the budget, fine-tuning when it’s worth it (we benchmark vs. RAG first), serving with vLLM or TGI for throughput, and quantization for cost. We measure quality on your data — not generic benchmarks.
And we keep optionality alive: every LLaMA system we ship sits behind a model abstraction so you can fall back to Claude, GPT, or Gemini when an edge case demands it.
LLaMA use cases we deliver
How we deliver
Our LLaMA delivery process
- 01
Workload + sizing audit
We profile your traffic, latency SLO, and quality bar, then size the right LLaMA variant and GPU footprint.
- 02
Fine-tune vs. RAG decision
We benchmark prompt + RAG first; fine-tune only when the data shows it pays off.
- 03
Serving infrastructure
vLLM or TGI on Kubernetes/EKS, autoscaling, observability, and per-tenant rate limits.
- 04
Operate & evolve
Model upgrades, eval regressions, capacity planning, and gradual rollouts.
Related technologies
Ollama
Ship private, offline-capable AI features with Ollama — local LLM serving for desktops, edge servers, and air-gapped enterprises.
Learn moreDeepseek
Production deployment of Deepseek-V3 and Deepseek-Coder for reasoning, coding, and high-volume workloads at a fraction of frontier-model cost.
Learn moreMLOps
MLOps platform engineering — pipelines, model registries, evaluation, monitoring, and incident response for ML and LLM systems.
Learn moreKubernetes
Production Kubernetes engineering — cluster design, GitOps, observability, CIS hardening, multi-tenancy, internal developer platforms, and the day-2 operations the demos skip.
Learn moreLLaMA — Frequently Asked Questions
It depends on task complexity, latency SLO, and budget. For most enterprise chat we start with the 70B class; for classification or extraction, fine-tuned 8B often beats 70B prompted. We benchmark before committing GPU spend.
