Tagged “llm-systems”

7 posts.

All Model Architecture19 Training & Alignment22 Inference & Serving18 Agents & Orchestration11 Reasoning & Evaluation26 Safety, Security & Governance7 Platforms & Practice20

Platforms & Practice 24 min

A Model Version Is No Longer a File: Registries for Compound AI Systems

Model registries were built to version a trained artefact you own. An LLM application's behaviour comes from a hosted snapshot that retires on someone else's calendar, plus prompts, an index and tools you change weekly. The relea…

model-registry-and-versioning mlops llm-systems evaluation ∑ ◫

Inference & Serving 21 min

FlashAttention and the Memory Wall: Why Attention Was Never Compute-Bound

An A100 can execute 312 trillion half-precision operations per second but can only pull about 2 terabytes from memory in that same second. FlashAttention made attention fast not by computing less, but by refusing to touch memory.

flashattention gpu attention transformers ∑ ◫

Inference & Serving 21 min

Four Bits Per Weight: How Low-Precision Quantization Stopped Hurting LLMs

A 70B model in FP16 needs 140 GB of memory it spends most of its time waiting to read. Dropping each weight to four bits cuts that to 35 GB, and for years that cut also broke the model. Here is what changed.

quantization inference gptq awq ∑ ◫

Agents & Orchestration 19 min

Multi-Agent Orchestration Patterns: When Coordination Beats One Agent, and When It Just Multiplies Cost

A single agent burns about 4x the tokens of a chat. A multi-agent system burns about 15x. The whole question of orchestration is whether the extra agents buy you accuracy worth more than that bill, and four named patterns decide …

multi-agent agents orchestration llm-systems ∑ ◫

Inference & Serving 4 min

Prompt caching is not just a cost optimisation - it changes what apps you can build

Most teams treat prompt caching as a billing hack. The deeper consequence is that you can now ship product patterns - persistent agents, large-context tools, system-prompt-heavy UX - that were latency-prohibitive eighteen months ago.

caching inference latency economics

Inference & Serving 21 min

Sleep-Time Compute: Thinking About the Context Before the Question Arrives

Test-time compute makes models smarter but slower and pricier. Sleep-time compute moves the thinking to before the user asks, cutting the compute needed for the same accuracy by roughly 5x and the cost per query by 2.5x when cont…

inference test-time-compute agents reasoning ∑ ◫

Platforms & Practice 24 min

What Structure Buys You: Knowledge Graphs in the Age of Language Models

In 2019 a masked language model recalled facts almost as well as a relation extractor with an oracle entity linker, and people asked whether knowledge graphs were finished. They were not, but the reason is narrower than their adv…

knowledge-graphs rag retrieval hybrid-retrieval ∑ ◫