Speculative Decoding Explained: How EAGLE-3 Makes LLMs 2-3x Faster Without Changing Outputs
Autoregressive decoding is the reason large language models feel slow: every token is generated by a full forward pass, and
Tag: AI
LLM Routing and Cascading: Send Every Query to the Cheapest Model That Can Handle It
If you run a large language model behind a product, you have probably internalized an uncomfortable trade-off. The big frontier
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LLM Quantization Explained: GPTQ, AWQ, GGUF, and When Each One Wins
Serving a 7B model in fp16 takes roughly 14 GB of VRAM. A 70B model takes around 140 GB —
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Speculative Decoding: Getting 2-3x More Out of Every GPU Pass
Watch a GPU while a large language model generates text and you’ll see something strange: for most of every forward
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vLLM Prefix Caching: The Prefill Optimization You’re Already Running
Every request that hits an LLM server pays the same tax before the first generated token appears: the prompt must
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Constrained Decoding: How Structured LLM Output Actually Works
Ask an LLM for JSON and you will usually get JSON. “Usually” is the word that ruins your week. One
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Harness Scaling: How a State Machine Runtime Pushed Agents to 95.3% on Terminal-Bench
The default answer to “make the agent better” is a better model. But a growing pile of evidence says that
AGENTS.md: Teaching Coding Agents to Work in Your Repository
Two years into the coding-agent era, a plain Markdown file has quietly become standard infrastructure. AGENTS.md is the place where
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The Post-Training Era: GLM-5.3, Gemini 3.7 Flash, and the Week the Base Model Stopped Mattering
Three model releases in ten days have made one thing clear: the frontier story of late 2026 is not bigger
Beyond Math and Code: How SpyRL Turns Party Games Into Verifiable Training Signals for LLMs
Reinforcement Learning with Verifiable Rewards (RLVR) has become the engine behind modern reasoning models. The recipe is straightforward: let a