Glossary

This page covers the hardware concepts, communication libraries, deployment terminology, and common abbreviations you will encounter throughout the Ascend NPU documentation. Refer back here when you run into unfamiliar terms.

Hardware

Supported devices

SGLang supports the following Ascend inference hardware:

Hardware	Devices	Dies per card	Memory configuration
Ascend A3 Series Products	16	2	8(cards) × 2(dies/card) × 64(GB/die)
Ascend 950PR/DT Series	8	1	8(cards) × 1(die/card) — HBM depends on the chip

Throughout these docs, A3 Series and 950PR/DT Series are used as shorthand for the hardware above. Docker image tags use a3 for the A3 Series. For example, main-cann9.1.0-a3.

Tags for the 950PR/DT Series use 950. For example, main-cann9.1.0-950.

On the A3 Series, each card has 2 dies, giving 16 devices per node. Benchmark pages use “Cards” to refer to physical cards, so Cards: 4 on the A3 Series means --tp-size 8. On the 950PR/DT Series, each card is a single die, so card count, device count, and --tp-size match one-to-one.

NPU

NPU stands for Neural Processing Unit. Each NPU device is a single davinci core. The terms “NPU” and “davinci” are used interchangeably in commands and error logs.

On the A3 Series, devices are numbered /dev/davinci0 through /dev/davinci15 (16 devices). On the 950PR/DT Series, each card exposes a single device, numbered /dev/davinci0 through /dev/davinci7 (8 devices).

On an A3 Series server, run npu-smi info to view NPU information such as device health, memory usage, and chip status. If the command is not found or reports no devices, the driver is likely not installed. Follow the Ascend driver installation guide to install it.

Communication libraries

Library	Description
HCCL (Huawei Collective Communication Library)	The primary communication backend for multi-card data transfer on Ascend NPUs. Equivalent to NVIDIA NCCL. Used in --nnodes, --tp-size, and all distributed scenarios.
GLOO	Meta’s collective communications library. Used alongside HCCL for distributed initialization and coordination.
DeepEP (Deep Expert Parallelism)	A communication library optimized for Mixture-of-Experts (MoE) all-to-all dispatch and combine operations. Used with --moe-a2a-backend deepep.
RDMA (Remote Direct Memory Access)	Enables direct memory access between nodes over InfiniBand or RoCE networks. Required for multi-node PD disaggregation.

Quantization and precision

Notation	Meaning
W8A8	8-bit weights, 8-bit activations
W4A8	4-bit weights, 8-bit activations
W4A16	4-bit weights, 16-bit activations
BF16	Brain Floating Point 16 — 16-bit format optimized for ML workloads
FP8	8-bit Floating Point — not supported on the A3 Series (on Ascend, FP8-class quantization goes through MXFP8 below)
MXFP8	Microscaling FP8 (OCP MX block-scaled format) — requires the 950PR/DT Series
MXFP4	Microscaling FP4 (OCP MX block-scaled format) — requires the 950PR/DT Series
INT8	8-bit Integer quantization

To apply quantization, use --quantization modelslim for W8A8 INT8, or load a pre-quantized checkpoint directly from a model hub.

Deployment terminology

Prefill-Decode (PD) disaggregation

PD disaggregation separates inference into two stages running on different nodes:

• Prefill (P): Processes the entire input prompt at once. Compute-bound.
• Decode (D): Generates tokens one at a time. Memory-bandwidth-bound.

PD Mixed: Both stages run on the same set of nodes.

Shorthand	Meaning
1P1D	1 prefill node + 1 decode node
2P1D	2 prefill nodes + 1 decode node
1P2D	1 prefill node + 2 decode nodes

You will see these in Best Practice section headings, e.g., W8A8 2P1D 32P means “W8A8 quantization, 2 prefill nodes + 1 decode node, 32 cards total.”

Parallelism strategies

Strategy	Flag	Description
Tensor Parallelism (TP)	--tp-size	Splits model weights across NPUs within a node
Data Parallelism (DP)	--dp-size	Replicates the model across nodes for higher throughput
Expert Parallelism (EP)	--ep-size	Distributes MoE experts across devices; requires --moe-a2a-backend
Context Parallelism (CP)	--attn-cp-size	Splits long context windows across devices for extended sequence length
Pipeline Parallelism (PP)	--pp-size	Splits model layers across devices sequentially

Speculative decoding

Algorithm	Description
EAGLE3	Uses an external draft model (specified via --speculative-draft-model-path) to propose candidate tokens. Supports top-k sampling.
NEXTN	Uses the model’s built-in Multi-Token Prediction (MTP) heads — no separate draft model needed. Available for models with native MTP support.
MTP (Multi-Token Prediction)	A model architecture feature where the model predicts multiple future tokens per step. The foundation for NEXTN speculative decoding.

Performance metrics

Metric	Description
TPOT (Time Per Output Token)	Average time to generate each output token. Lower is better.
TTFT (Time To First Token)	Latency from request arrival to first generated token. Critical for interactive use.

Model architecture terms

Term	Description
MoE (Mixture of Experts)	Model architecture where only a subset of parameters (experts) is activated per token, reducing compute. Common in DeepSeek, Qwen3-30B-A3B, and MiMo models.
MLA (Multi-head Latent Attention)	Attention variant that compresses key-value representations into a lower-dimensional latent space. Used by DeepSeek models.
GQA (Grouped-Query Attention)	Attention variant where multiple query heads share a single key-value head. Used by Qwen3 dense models.
DSA (DeepSeek Sparse Attention)	DeepSeek’s sparse attention mechanism; reduces KV cache overhead for long contexts.
FFN (Feed-Forward Network)	The non-attention component of each transformer layer. In MoE models, the FFN is replaced by multiple expert FFN layers selected by a router.

Other common abbreviations

Abbreviation	Expansion
ACL (Ascend Computing Language)	Low-level Ascend compute API; seen in error logs
DVFS (Dynamic Voltage and Frequency Scaling)	Hardware frequency scaling to maintain performance stability
NUMA (Non-Uniform Memory Access)	Memory architecture affecting multi-socket performance
KV Cache (Key-Value Cache)	Cached attention key-value tensors to avoid recomputation during decode
LoRA (Low-Rank Adaptation)	Parameter-efficient fine-tuning method
HF (Hugging Face)	Model hub; HF_TOKEN / HF_ENDPOINT control model download access
UB (Unified Buffer)	On-chip NPU memory; referenced in operator optimization
DMA (Direct Memory Access)	Data transfer mechanism between host and device memory

Where to go next

• Quickstart — launch your first server
• Installation Guide — full installation with component version mapping
• Supported Features — per-parameter Ascend support status
• Supported Models — models verified on Ascend NPU
• Model Tutorials — step-by-step deployment guides
• Best Practice — benchmark configurations and results