Llm Inference

10 articles on llm inference.

Inference as a Service Economics: Why Fast AI APIs Can Still Win

Inference as a service is a brutal margin game. Learn how caching, batching, hardware, and pricing turn open AI models into viable APIs.

DeepSeek V4.1 Flash uses a new asymmetric architecture and KV cache compression to lower long-context AI agent costs and latency.

DeepSeek V4.1 Flash pairs 1M context with major KV cache cuts. Here’s what its CED architecture means for AI agents, cost, and speed.

On-device AI for SaaS can turn variable inference bills into predictable costs while adding offline functionality and a real privacy advantage.

GLM-5.3 Flash pairs 320B parameters with 18B active, hybrid attention and 1M context. What its efficiency means for AI builders.

DeepSeek V4 Pro 0813 brings stronger agent performance, MIT-licensed weights, and DSpark decoding. Here is what builders should know.

DSpark speculative decoding pairs smarter drafting with load-aware verification to make AI inference faster without changing model outputs.

DeepSeek DSpark combines smarter speculative decoding and load-aware scheduling to speed LLM serving. Here’s what DeepSpec means for builders.

DeepSeek DualPath tackles AI agent latency by rerouting KV-cache reads, unlocking more throughput from existing GPU infrastructure.

DSpark speculative decoding promises 60–85% faster LLM generation. Here’s how DeepSeek reduces verification waste and where it works best.