Enterprise LLM Deployment
-
LLM Inference Non-Determinism: Why Temperature 0 Isn't Enough
Identical prompts produce different outputs even at temperature 0. The real cause — dynamic batching
-
How to Calculate GPU Memory for LLM Inference
A component-by-component method for calculating LLM inference VRAM: model weights, KV cache, activat
-
GPU Memory Planning for Long-Context LLM Inference
Long-context LLM inference is often HBM-bound, not SM-bound. Plan GPU memory from prompt tail, KV ca
-
How to Size GPUs for Embedding Backfills in Enterprise Data
Size GPUs for embedding backfills from corpus tokens, chunk rate, and deadline, then isolate the job
-
Prefill vs Decode GPU Capacity Planning for Inference SLAs
Prefill burns compute on the prompt. Decode burns compute per output token. Plan GPU capacity for ea
-
AWQ vs GPTQ for Production Inference Cost
AWQ and GPTQ both shrink weights for cheaper inference. Compare calibration, memory, quality risk, a
-
vLLM vs TensorRT-LLM for Production Inference Serving
vLLM favors flexible serving; TensorRT-LLM favors compiled NVIDIA peak. Compare ops burden, compile
-
Prefill vs Decode Compute Costs for LLM Inference
Prefill is prompt-bound compute; decode is memory-bound per token. Compare cost shape, when each dom
-
Autoscaling for LLM Inference Serving and Cold Starts
LLM autoscaling should watch queue depth and KV-cache pressure, not GPU busy percent. Plan warm pool
-
LoRA vs Full Fine-Tuning GPU Memory for Enterprise Models
Full fine-tuning stores gradients and optimizer state for every parameter. LoRA trains small adapter
Recommended Reading
-
Google Cloud GPU Pricing: What Enterprise AI Teams Should Evaluate Before Provisioning
-
Paperspace Pricing 2026: GPU Cost Breakdown
-
CoreWeave Enterprise GPU Cloud: Evaluation for AI Teams
-
AI Infrastructure Costs: Controlling Enterprise GPU Spending
-
CoreWeave vs Lambda Labs: GPU Cloud Provider Comparison
latest articles
-
Model Deployment Secret Management for Enterprise AI
-
Lineage vs Model Card vs SBOM for Enterprise AI
-
How to Plan Inference GPU Headroom for Production
-
Model Serving SLO Design for Enterprise LLM Traffic
-
How to Measure Quantization Quality Loss for Inference
-
What Is Data Center PUE for AI GPU Clusters
-
LLM Inference Failover Capacity Planning for Production
-
How to Sanitize GPUs After Enterprise AI Training
-
Service Quota vs Cluster Quota for Enterprise GPUs
-
Fractional GPU Allocation Across Enterprise AI Teams