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Most teams still evaluate LLMs using tokens per second and cost per million tokens, but these metrics fail to predict production behavior. This article reveals the real trade-offs among speed, cost, and quality, introduces the Pareto frontier as an evaluation framework, and highlights critical production metrics like TTFT and p99 latency.
This guide details six production-tested optimization strategies for LLM inference, helping teams match specific bottlenecks with the highest-impact methods, including batching, prefill/decode optimizations, KV cache optimizations, attention/memory optimizations, parallelism, and offline batch inference.
This article reviews the top open-source small language models (SLMs) in 2026, including Qwen3.5-0.8B, Gemma-3n-E2B-IT, Phi-4-mini-instruct, SmolLM3-3B, and Ministral-3-3B-Instruct-2512. It discusses their suitability for production in resource-constrained environments, pros and cons, and answers common FAQs about SLMs.
This article explores the top open-source image generation models in 2026, including FLUX.2, Stable Diffusion, GLM-Image, and Z-Image-Turbo, highlighting their strengths, considerations, and use cases.
This article explains GPU memory (VRAM) in the context of LLM inference, covering how memory is used for model weights, KV cache, and overhead. It provides formulas for memory estimation, discusses common pitfalls like OOM errors, and presents optimization strategies such as quantization, distributed inference, and KV cache optimizations. The post also highlights how the BentoML Inference Platform simplifies these optimizations.
This guide explains the differences among DeepSeek-V3, R1, V3.1, and their variants, including performance benchmarks, use cases, and deployment tips.
This article covers the best open-source large language models in 2026, including DeepSeek-V4, MiMo-V2.5-Pro, and Kimi-K2.6, and answers common FAQs about performance, inference optimization, and self-hosted deployment.
This guide details ChatGPT usage limits as of April 2026 across Free, Go, Plus, Business, and Pro plans, explaining message caps, model selection, and context windows. It covers why limits exist (infrastructure load, cost control, fairness, abuse prevention) and other limitations like unpredictable performance, data privacy, lack of customization, and spiraling costs. The solution proposed is self-hosting open-source LLMs to remove all restrictions.