Load Testing LLMs: Tools, Metrics & Realistic Traffic Simulation (2026)
LLM performance testing goes beyond basic API benchmarks. Learn to measure TTFT, tokens per second, p99 latency, and throughput under realistic concurrent load.
Arnav Jalan
Semantic Caching for LLMs: How to Cut API Bills by 60% Without Hurting Quality
Learn how semantic caching cuts LLM API costs by 40-70%. Covers embedding similarity, similarity thresholds, GPTCache, Redis, invalidation strategies, and real cost math.
LLM Batching: Static vs Continuous and Why It Matters for Throughput
Static batching wastes GPU cycles waiting for slow requests. Continuous batching fixes this by scheduling per-iteration. Benchmarks and implementation inside.
Fine-Tuning vs RAG: A Decision Framework for Custom LLM Applications
When to fine-tune, when to use RAG, and when to combine both. Covers knowledge vs behavior problems, cost per 1,000 queries, latency tradeoffs, RAFT, and real production examples.
Which LLM Alignment Method? RLHF vs DPO vs KTO Tradeoffs Explained
RLHF needs a reward model. DPO skips it. KTO only needs thumbs up/down. Which alignment method fits your data, compute, and timeline? Practical comparison inside.
Serverless LLM Deployment: RunPod vs Modal vs Lambda (2026)
Cold starts: 5-120 seconds. Break-even: 40% GPU utilization. Lambda doesn't even offer serverless anymore.
If you're evaluating serverless GPU inference in 2026, here's the short version:
If You Need
Use
Fastest setup (Hugging Face → endpoint in minutes)
RunPod
Lowest per-request cost
Modal
Lowest always-on
8 Best LLM Fine-Tuning Platforms in 2026 (Compared)
Compare the 8 best LLM fine-tuning platforms in 2026. Pricing, supported models, data sovereignty, ease of use, and honest limitations for each.
GPU Buying Guide for LLMs: RTX 5090 vs H100 vs H200 Complete Comparison (2026)
Which GPU should you buy for running LLMs? From $250 budget cards to $40K datacenter GPUs. Covers VRAM needs, tokens per second benchmarks, and total cost of ownership.
How to Generate Synthetic Training Data for LLM Fine-Tuning (2026 Guide)
Every method for generating synthetic training data for LLM fine-tuning: distillation, Self-Instruct, Evol-Instruct, Magpie, persona-based. Plus quality filtering, model collapse prevention, and tools
LLM Cost Optimization: 8 Strategies That Cut API Spend by 80% (2026 Guide)
Reduce LLM spending from $10K to $2K monthly. Covers prompt optimization (immediate wins), semantic caching (68% hit rates), model cascading, and open-source migration paths.