club-3090/docs/GLOSSARY.md

Glossary

Plain-language definitions for terms used throughout the docs. Roughly grouped by topic.

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Throughput / latency

Term	What it means
TPS	Tokens per second — how fast the model generates output. ~70 TPS is roughly conversational speed; ChatGPT cloud is ~80-120.
Wall TPS	completion_tokens / wall_time — user-perceived total speed (includes prefill cost).
Decode TPS	completion_tokens / (wall_time − TTFT) — pure model decode rate, excludes prefill.
TTFT	Time to first token. Dominated by prefill cost on long prompts.
CV	Coefficient of variation across measured runs. Lower = more predictable. We aim for <5% in benches.

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Memory / context

Term	What it means
Prefill	The phase where the model processes the entire input (system prompt + user message + history) before generating the first output token. Slow on first request, fast on follow-ups via prefix cache.
Decode	The phase after prefill — generating output tokens one at a time.
KV cache	"Key-value" cache — the model's working memory of the conversation so far. Larger context = bigger KV cache = more VRAM.
Prefix cache	When two requests share a leading prompt, vLLM (and llama.cpp) serve the second from cache (skip re-prefill). Especially useful for long-document workflows.
Context window	Total tokens the model can hold in working memory at once. Set via --max-model-len (vLLM) or -c (llama.cpp).
Activation memory	Memory used during forward pass (intermediate tensor outputs at each layer). Distinct from KV cache (long-lived) and model weights (fixed). Activation peaks during prefill cause the OOMs we document.

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Quantization

Term	What it means
Quantization	Compressing model weights from 16-bit floats to 4-bit or 8-bit ints. Lets a 27B model fit in 18 GB instead of 54 GB, with small quality loss.
AutoRound	Intel's 4-bit quantization method using signed gradient descent. Strong on Qwen-family models.
GPTQ	Layer-wise Hessian-based 4-bit quantization. Mature, broadly supported.
AWQ	Activation-aware salience-scaled 4-bit quantization. Strong baseline.
GGUF	Standardized binary format used by llama.cpp / Ollama / LM Studio. Many quant types: Q4_K_M, Q5_K_S, IQ4_XS, etc.
TurboQuant	A 3-bit KV cache compression scheme used by vLLM. Lets us fit 192K+ context where fp8 KV would only fit ~32K.
fp8 / fp8_e5m2	An 8-bit float KV cache format. Larger per-token bytes than TurboQuant but dodges several bugs.

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Speculative decoding

Term	What it means
Spec-decode / speculative decoding	The model predicts several tokens ahead, then verifies. Roughly 2-3× faster than greedy decoding when accept rate is high.
MTP	Multi-Token Prediction — built-in spec-decode head that ships with Qwen3.6. We run it with num_speculative_tokens=3.
DFlash N=5	A custom 5-token draft model from z-lab specialized for Qwen3.6 code workloads. Replaces MTP with a parallel external draft.
EAGLE	SGLang's MTP equivalent; currently blocked on hybrid attention.
AL (acceptance length)	Average number of tokens accepted per spec-decode step. AL 3.5 means the model usually gets 3-4 tokens right per round. Higher is better. Theoretical max for n=3 is 4.
Per-position acceptance	The accept rate at each position 1, 2, 3 of the spec-decode draft. e.g., 92% / 86% / 71% on code means position-1 is almost always right; position-3 is right 71% of the time.

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Engines & infrastructure

Term	What it means
vLLM	A production-grade GPU LLM inference engine. Open source, NVIDIA-focused. Powers many cloud inference services.
llama.cpp	A lightweight CPU-and-GPU inference engine. Works on every platform. Smaller binary, less feature-rich than vLLM.
SGLang	A high-throughput serving engine with RadixAttention prefix sharing. Often beats vLLM on multi-tenant aggregate.
Genesis patches	Sandermage's vLLM monkey-patch tree that fixes several Qwen3-Next bugs at runtime. We mount it into vLLM's site-packages.
Cudagraph	A CUDA optimization that records GPU operation sequences and replays them. Faster than dispatching ops individually.
OpenAI API	The HTTP API spec (/v1/chat/completions, etc.) used by ChatGPT, Claude (via proxy), and many OSS chat tools. We serve this on localhost:8020 (single-card) or localhost:8010 (dual-card).

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Multi-card concepts

Term	What it means
TP=2 / tensor parallelism	Splits each model layer's weights across both GPUs; layers compute together, results combined via NCCL all-reduce. Doubles effective VRAM (48 GB total).
PP / pipeline parallelism	Different layers go on different GPUs; not used in this stack.
NVLink	NVIDIA's high-bandwidth GPU-to-GPU interconnect (~600 GB/s on H100, ~200 GB/s on 3090 with bridge). Not required by this stack — we run PCIe-only.
All-reduce	The collective op TP uses to combine partial results. PCIe-only consumer Ampere is ~3-5× slower than NVLink.
Concurrent streams	Multiple users/agents serving simultaneously. KV pool is shared; each stream gets a slice.

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Model architecture

Term	What it means
Qwen3-Next	Qwen team's hybrid attention architecture used in Qwen3.5/3.6. Interleaves DeltaNet (linear attention) layers with standard attention layers.
DeltaNet / GDN	"Gated DeltaNet" — a linear-attention layer type. Qwen3.6-27B has 48 GDN + 16 standard attention layers (3:1 ratio).
Hybrid attention	Architectures mixing standard attention with linear-attention or state-space layers. Qwen3-Next, Mamba-class models, Jamba.
MTP head / mtp.fc	Multi-Token Prediction head — a small extra network in the model that drafts speculative tokens. Lorbus's quant preserves it in BF16 (rather than INT4) so vLLM can load and use it.

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Tool calling / API features

Term	What it means
Tool calling	The model emits structured calls to external functions you define (e.g., get_weather(...)); your code runs them and feeds results back.
Reasoning / thinking mode	The model emits intermediate reasoning steps before its final answer. Set chat_template_kwargs.enable_thinking=true.
Streaming	Tokens arrive incrementally via Server-Sent Events. Faster perceived UX.
Vision	The model can accept images alongside text. Powered by an integrated vision tower.
Tool prefill	When an agent calls a tool and feeds the (potentially huge) tool response back, the next inference call has to "prefill" all that history. Big tool returns can OOM if context tier isn't set right.