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[main][Docs] Fix typos across documentation (#6728)

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## Summary

Fix typos and improve grammar consistency across 50 documentation files.
 
### Changes include:
- Spelling corrections (e.g., "Facotory" → "Factory", "certainty" →
"determinism")
- Grammar improvements (e.g., "multi-thread" → "multi-threaded",
"re-routed" → "re-run")
- Punctuation fixes (semicolon consistency in filter parameters)
- Code style fixes (correct flag name `--num-prompts` instead of
`--num-prompt`)
- Capitalization consistency (e.g., "python" → "Python", "ascend" →
"Ascend")
- vLLM version: v0.15.0
- vLLM main:
9562912cea

---------

Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
This commit is contained in:
Cao Yi
2026-02-13 15:50:05 +08:00
committed by GitHub
parent b6bc3d2f9d
commit 6de207de88
50 changed files with 273 additions and 272 deletions
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18
docs/source/user_guide/deployment_guide/using_volcano_kthena.md
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@@ -1,6 +1,6 @@
# Using Volcano Kthena
This guide shows how to run **prefill–decode (PD) disaggregation** on Huawei Ascend NPUs using **vLLM-Ascend**, with [**Kthena**](https://kthena.volcano.sh/) handling orchestration on Kubernetes. About vLLM support with kthena, please refer to [Deploy vLLM with Kthena](https://docs.vllm.ai/en/latest/deployment/integrations/kthena/).
This guide shows how to run **prefill–decode (PD) disaggregation** on Huawei Ascend NPUs using **vLLM-Ascend**, with [**Kthena**](https://kthena.volcano.sh/) handling orchestration on Kubernetes. About vLLM support with Kthena, please refer to [Deploy vLLM with Kthena](https://docs.vllm.ai/en/latest/deployment/integrations/kthena/).
---
@@ -10,18 +10,18 @@ Large language model inference naturally splits into two phases:
- **Prefill**
- Processes input tokens and builds the key–value (KV) cache.
- Batch‑friendly, high throughput, well suited to parallel NPU execution.
- Batch-friendly, high-throughput, well-suited to parallel NPU execution.
- **Decode**
- Consumes the KV cache to generate output tokens.
- Latency‑sensitive, memory‑intensive, more sequential.
- Latency-sensitive, memory-intensive, more sequential.
From the client’s perspective, this still looks like a single Chat / Completions endpoint.
From the client's perspective, this still looks like a single Chat / Completions endpoint.
---
## 2. Deploy on Kubernetes with Kthena
[Kthena](https://kthena.volcano.sh/) is a Kubernetes-native LLM inference platform that transforms how organizations deploy and manage Large Language Models in production. Built with declarative model lifecycle management and intelligent request routing, it provides high performance and enterprise-grade scalability for LLM inference workloads. In this example, we use three key Custom Resource Definitions (CRDs):
[Kthena](https://kthena.volcano.sh/) is a Kubernetes-native LLM inference platform that transforms how organizations deploy and manage Large Language Models in production. Built with declarative model lifecycle management and intelligent request routing, it provides high-performance and enterprise-grade scalability for LLM inference workloads. In this example, we use three key Custom Resource Definitions (CRDs):
- `ModelServing` — defines the workloads (prefill and decode roles).
- `ModelServer` — manages PD groupings and internal routing.
@@ -33,7 +33,7 @@ This section uses the `deepseek-ai/DeepSeek-V2-Lite` example, but you can swap i
- Kubernetes cluster with Ascend NPU nodes:
The Resources corresponding to different NPU Drivers may vary slightly. For example:
The resources corresponding to different NPU Drivers may vary slightly. For example:
- If using [MindCluster](https://gitee.com/ascend/mind-cluster#https://gitee.com/link?target=https%3A%2F%2Fgitcode.com%2FAscend%2Fmind-cluster), please use `huawei.com/Ascend310P` or `huawei.com/Ascend910`.
@@ -45,7 +45,7 @@ This section uses the `deepseek-ai/DeepSeek-V2-Lite` example, but you can swap i
A concrete example is provided in Kthena as <https://github.com/volcano-sh/kthena/blob/main/examples/model-serving/prefill-decode-disaggregation.yaml>
Deploy it with below command:
Deploy it with the command below:
```bash
kubectl apply -f https://raw.githubusercontent.com/volcano-sh/kthena/refs/heads/main/examples/model-serving/prefill-decode-disaggregation.yaml
@@ -295,7 +295,7 @@ You should see Pods such as:
- `deepseek-v2-lite-0-prefill-0-0`
- `deepseek-v2-lite-0-decode-0-0`
To enable the llm access, we still need to configure the routing layer with `ModelServer` and `ModelRoute`.
To enable the LLM access, we still need to configure the routing layer with `ModelServer` and `ModelRoute`.
### 2.3 ModelServer: PD Group Management
@@ -306,7 +306,7 @@ The `ModelServer` resource:
- Configures KV connector details and timeouts.
- Exposes an internal gRPC/HTTP interface.
Create modelServer with below command:
Create ModelServer with the command below:
```bash
kubectl apply -f https://raw.githubusercontent.com/volcano-sh/kthena/refs/heads/main/examples/kthena-router/ModelServer-prefill-decode-disaggregation.yaml