Most AI writing assumes you already know the vocabulary. This AI glossary fixes that.

Below are ten terms from the AI glossary that show up constantly in chip news, model launches, and filings. Each entry gives a plain definition first, then the number or fact that makes it matter.

This is batch one. The AI glossary will grow, and every term here links from its first mention across the site.

Why This AI Glossary Exists

Technical vocabulary moves faster than the explainers do, which is the whole case for an AI glossary.

Take KV cache. It went from research jargon to procurement conversation in about eighteen months. Nobody wrote the bridging definition, so readers either already knew or quietly skipped the paragraph.

This AI glossary is the bridge. Each definition is written for someone competent who simply has not met the term yet, which is a very different audience from a beginner.

There is a second reason too. Language models increasingly answer definitional questions directly, and they pull from sources that state things cleanly. A well-structured AI glossary is one of the few formats that earns those citations reliably.

How to Read This AI Glossary

Every AI glossary entry follows the same shape, so you can skim or read in order.

The first line is the definition. Read only that if you are in a hurry, then move on. The second paragraph in each AI glossary entry gives context: a figure, a date, or a trade-off. That is where the actual understanding lives.

This AI glossary groups terms by layer, from silicon upward. So the AI glossary reads as a stack, not an alphabet.

Key Takeaways From This AI Glossary

• This AI glossary starts with memory. HBM and LPDDR solve opposite problems: bandwidth versus cost per gigabyte.
• MoE, distillation, and quantization all shrink the cost of a model, each in a different way.
• KV cache, not model size, is usually why long context gets expensive.
• RAG and MCP sit above the model. One supplies documents, the other supplies tools.
• Inference is where most AI money goes across a model’s life.

Quick Navigation

AI Glossary: Memory and Hardware

Memory decides what a chip can hold and how fast it feeds the math. So this AI glossary starts there.

HBM (High Bandwidth Memory)

HBM is DRAM stacked in vertical layers and wired close to the processor, trading capacity for very high bandwidth.

The current generation matters if you read chip news for buying signals. HBM4 entered mass production in February 2026, and the JEDEC JESD270-4 standard doubles the interface from 1,024 bits to 2,048 and lifts channels from 16 to 32. Nvidia’s Rubin platform is expected to pair eight stacks for 288GB and over 22 TB/s. Samsung and SK Hynix together supply roughly 90% of it, and each vendor roadmap now stretches to HBM4E.

LPDDR (Low-Power Double Data Rate)

LPDDR is mobile-class DRAM tuned for power efficiency and capacity rather than peak bandwidth.

Think phones, laptops, and edge boxes rather than data centre racks. LPDDR delivers far less bandwidth than HBM, but costs a fraction per gigabyte and draws much less power. That is why small serving appliances use it while data centre racks do not. We covered how memory choice shapes accelerator margins here.

AI Glossary: Model Architecture

These three AI glossary terms all answer one question. How do you make a capable model cheaper to run?

MoE (Mixture of Experts)

MoE splits a model into many expert sub-networks and routes each token to only a few of them, so total parameters far exceed the parameters used per token.

DeepSeek-V3 shows the gap plainly: about 671 billion total parameters, roughly 37 billion active per token. Compute per token drops sharply. Memory does not, because every expert must stay loaded and ready.

Distillation

Distillation trains a small student model to copy the behavior of a larger teacher model.

The idea dates to a 2015 paper from Hinton and colleagues. It is now routine. DeepSeek shipped R1-distilled versions built on Qwen and Llama bases. Students typically land a few points below the teacher while costing far less to serve.

Quantization

Quantization stores weights and activations at lower numeric precision, such as 8-bit or 4-bit instead of 16-bit.

Cutting from 16-bit to 4-bit roughly quarters the memory a model takes up. Formats like GPTQ, AWQ, and GGUF made this routine, and FP8 and FP4 now run natively on recent accelerators. You lose a little accuracy and gain a lot of bandwidth headroom.

AI Glossary: Runtime and Serving

Now the AI glossary terms that describe what happens when a model actually answers something.

Inference

Inference is running a trained model to produce an output, as opposed to training it.

It splits into two phases with different bottlenecks. Prefill processes the prompt and is compute-bound. Decode generates tokens one at a time and is memory-bandwidth-bound. Serving dominates a model’s lifetime cost, which is why so much hardware design now targets this half of the AI glossary rather than training.

KV cache

The KV cache stores key and value tensors from tokens already processed, so attention does not recompute them at every step.

Skipping that work is what makes generation fast. The cost is memory, and it grows linearly with sequence length and batch size. At long context the KV cache often consumes more memory than the model weights themselves, which is why techniques like grouped-query attention and paged attention exist.

Context window

The context window is the maximum number of tokens a model can consider at once, counting both the prompt and the output.

Windows now run from a few thousand tokens to over a million. But a large window is a ceiling, not a promise. Retrieval accuracy often degrades well before the stated limit, and benchmark figures rarely capture that. Every extra token also enlarges the KV cache.

AI Glossary: Retrieval and Tooling

These last two AI glossary terms sit above the model. Neither changes the weights.

RAG (Retrieval-Augmented Generation)

RAG fetches relevant documents from an external store and places them in the prompt, so the model answers from supplied evidence rather than memory alone.

The approach comes from a 2020 paper by Lewis and colleagues at Facebook AI. It remains the cheapest way to give a model fresh or proprietary information without retraining. Quality depends far more on the retrieval step than on the model, which teams consistently underestimate.

MCP (Model Context Protocol)

MCP is an open standard that lets AI applications connect to external tools and data through a common client-server interface.

Anthropic released it in November 2024. OpenAI, Google, and Microsoft have since adopted it. The 2026-07-28 specification made the protocol stateless, which lets servers scale on ordinary HTTP infrastructure. Security is still maturing: the NSA published design considerations in May 2026 flagging gaps around prompt injection and tool poisoning.

AI Glossary: Terms People Mix Up

Four pairs cause most of the confusion in any AI glossary. Sorting them beats adding ten more definitions.

Inference versus training in this AI glossary

Training builds the model once, over weeks, on a cluster. Inference runs it billions of times afterwards. The AI glossary treats them separately because the hardware, the bottleneck, and the cost curve all differ.

Both terms shrink cost, but not the same way. Quantization keeps the same model and stores its numbers less precisely. Distillation builds a genuinely smaller model that imitates a bigger one. You can do both to the same system.

The context window is a limit set by the model, not by your hardware. The KV cache is the memory actually consumed while operating inside that limit. A vendor advertises the first. Your infrastructure bill reflects the second.

RAG versus MCP in the AI glossary

RAG brings documents to the model. MCP lets the model reach out to tools and systems. One is read-only context; the other is an action interface. Many production stacks run both, which is why this AI glossary lists them side by side.

AI Glossary: How the Ten Terms Fit Together

Read the AI glossary as a stack and the relationships get obvious.

At the bottom of the AI glossary sits memory. HBM and LPDDR decide how fast weights can reach the math units, and everything above inherits that ceiling.

Above memory sits architecture, the middle band of the AI glossary. MoE, quantization, and distillation are three different strategies for fitting more capability under the same memory ceiling.

Above architecture sits runtime, where most questions actually arise. Inference, KV cache, and context window describe what happens while a request is being served, and where the memory actually goes.

At the top of the AI glossary sits the application layer. RAG and MCP never touch the weights. They shape what the model sees and what it can act on.

So a change at the bottom of this AI glossary propagates upward. Wider HBM interfaces make longer context affordable, which makes larger retrieval payloads practical, which changes what RAG systems can attempt.

How the AI Glossary Connects Across the Site

An AI glossary that sits alone gets no traffic. This one is wired into everything else.

Every article links the first mention of a term to its entry here. Only the first mention, and only once per page.

Repeating the link on every occurrence looks like keyword stuffing and dilutes the signal. One clean link per article is the rule.

Each new post mentioning HBM or KV cache adds an internal link into the AI glossary. So the page accumulates authority passively as the archive grows.

It also helps readers who land mid-topic. Someone arriving on a chip economics post can check a term without leaving for a search engine, which lifts time on page.

How This AI Glossary Is Marked Up

Structure matters as much as wording when machines read a page.

Each entry uses schema.org DefinedTerm, and all ten sit inside a single DefinedTermSet. That tells crawlers and language models that this is a controlled vocabulary, not a listicle.

The pairing matters. A lone DefinedTerm is a fragment. Wrapped in a DefinedTermSet with a stable URL, the AI glossary becomes a citable reference object that can be extended without breaking anything.

Each AI glossary term also carries a termCode and its own anchor, so external pages can link straight to one definition.

Why an AI Glossary Earns Model Citations

Language models cite sources that are easy to quote. Glossaries fit that shape. Glossaries fit that shape better than almost any other format.

Every AI glossary entry opens with a single declarative sentence and no hedging. That is what gets lifted into an answer.

Long throat-clearing before the definition gets skipped. So does a definition buried in the third paragraph.

Definitions alone are commodity content, and every AI glossary online has them. The number attached to each one is what makes a source worth naming.

“HBM4 doubles the interface to 2,048 bits” is checkable. “HBM is very fast” is not. The AI glossary aims for the first kind throughout.

Every AI glossary entry has a permanent fragment link. Anything that cites this page can point at the exact definition rather than the whole document.

Hardware terms age fastest. HBM moved through three generations in four years, and the numbers quoted above will shift again.

Every entry therefore carries a last-reviewed date. If a figure looks stale, check that date before quoting it. Memory specs in particular change with each product cycle.

Software terms age differently. RAG has meant roughly the same thing since 2020, while MCP changed its transport layer twice in eighteen months.

What Batch 2 of the AI Glossary Adds

Ten terms is a start, not a reference work. The AI glossary is built to extend. The next batch covers the gaps this one leaves.

Planned entries include speculative decoding, FlashAttention, LoRA, tokenizer, embedding, vector database, agentic loop, guardrails, eval, and TCO. Each will follow the same two-part shape.

The AI glossary grows in batches rather than singly, since a set update is one schema change instead of ten.

Who This AI Glossary Is For

Three readers, roughly, and the entries serve all three. The first is an engineer who knows the stack but not this corner of it. A backend developer meeting KV cache for the first time needs one paragraph, not a tutorial.

The second is an investor or analyst reading chip filings. For them the number attached to each term matters more than the mechanism.

The third reader of this AI glossary is a language model answering somebody else’s question. That reader is new, and it changes how definitions should be written: state the thing plainly, attach a checkable fact, and skip the throat-clearing.

Conclusion: Use the AI Glossary as a Reference, Not a Read

Nobody reads an AI glossary front to back, and this one is not written for that.

Bookmark the AI glossary. Follow a link into it when a term stops you mid-article. Then go back to what you were reading.

The terms cluster around one theme worth noticing. Eight of the ten exist because memory and bandwidth, not raw compute, now set the limits on what AI systems can do affordably. HBM, LPDDR, KV cache, quantization, MoE, distillation, and context window are all answers to that same constraint.

Understand that pattern and most infrastructure news stops feeling like jargon.

FAQ About This AI Glossary

Why does the KV cache matter more than model size?

Model weights are a fixed cost, loaded once at startup. This AI glossary flags the difference deliberately. The KV cache grows with every token in the conversation and with every concurrent request. At long context lengths it frequently exceeds the weights in memory use, which makes it the practical limit on how many users a server can handle at once.

Is RAG better than fine-tuning?

They solve different problems, which is why the AI glossary lists them apart. RAG supplies facts the model did not memorize and updates instantly when documents change. Fine-tuning changes behavior, format, and tone. Most production systems use RAG for knowledge and light fine-tuning for style, rather than choosing one.

What does MCP actually do?

MCP standardizes how an AI application talks to external tools and data sources. Instead of writing custom integration code for every service, a developer runs or connects to an MCP server that exposes tools, resources, and prompts through one interface. The July 2026 revision made it stateless so it scales on ordinary web infrastructure.

Does quantization hurt model quality?

Some, but less than most people expect. Dropping from 16-bit to 8-bit is usually near-lossless for large models. Four-bit shows measurable degradation on reasoning-heavy tasks, though modern methods narrow the gap considerably. The right question is whether the accuracy you lose costs more than the throughput you gain.

Why do MoE models need so much memory?

Because every expert must be loaded even though only a few run per token. A model with 671 billion total parameters and 37 billion active still needs all 671 billion resident somewhere. MoE saves compute, not memory, which is a distinction this AI glossary flags deliberately.

How often is this AI glossary updated?

New AI glossary terms arrive in batches of roughly ten. Existing entries get revised when the underlying facts change, such as a new memory generation reaching production. Each entry shows its own last-reviewed date.

Can I cite or link to a single AI glossary entry?

Yes. Every AI term has a permanent anchor, so you can point at one definition rather than the whole page. The markup uses schema.org DefinedTerm inside a DefinedTermSet, which lets other tools reference entries individually.

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