LLM Deployment Infrastructure
Running your own LLMs means controlling costs, data privacy, and latency. But deploying a 7B+ parameter model requires careful GPU memory management, efficient batching, and proper scaling. We build LLM serving infrastructure that actually handles production traffic.
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We implement, you ship. Async, documented, done in days.
Serving Engine Selection
vLLM for maximum throughput with PagedAttention and continuous batching. Text Generation Inference (TGI) for Hugging Face model compatibility. Ollama for simpler setups on single-node deployments. We match the serving engine to your model size, traffic pattern, and GPU hardware. A 7B model on an A10G has different optimal settings than a 70B model across 4x A100s.
GPU Memory Optimization
Quantization (GPTQ, AWQ, or GGUF) reduces memory requirements by 2-4x with minimal quality loss. KV cache management via PagedAttention eliminates memory fragmentation. Tensor parallelism splits large models across multiple GPUs. We benchmark perplexity and generation quality after quantization — you see exactly what quality you trade for cost savings.
Request Management & Caching
Continuous batching serves multiple concurrent requests efficiently. Prompt caching (prefix caching in vLLM) avoids recomputing shared system prompts. Rate limiting and queue management prevent GPU OOM under traffic spikes. For RAG workloads, we separate the embedding and generation endpoints so each scales independently based on its resource profile.
Cost & Performance Monitoring
Dashboards track tokens per second, time-to-first-token, GPU utilization, and cost per 1K tokens. You see exactly how your self-hosted deployment compares to API pricing. Autoscaling rules add GPU instances during peak hours and scale down during off-hours. Spot instances for batch inference workloads cut costs by 60-70%.
Why Anubiz Engineering
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Skip the research. Tell us what you need, and we'll scope it, implement it, and hand it back — fully documented and production-ready.