AI Workflow Automation

Llama 4 in Production: A Real-World Deployment Walkthrough

Vishal V Jun 3, 2026 4 min read

Hardware sizing, vLLM configuration, throughput benchmarks, and total cost of ownership against API providers.

01GPU sizing: 4× H100 for 70B at 50 RPS

vLLM tensor parallel = 4. p95 latency 480ms.
02Use FP8 quantization

~30% throughput uplift; minimal quality loss.
03Continuous batching with paged attention

vLLM defaults are good; tune max_num_seqs to 256.
04Run behind an inference gateway

Rate limiting, observability, fallback to API on capacity exhaustion.

The 3-layer architecture pattern Ohveda uses to ship reliable, auditable enterprise AI agents to production.

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