Choosing the Right Deployment Strategy for Large Language Models: QwQ‑32B vs DeepSeek‑R1

Deployment Plan Selection

1.1 Base Large Model

Dimension

QwQ‑32B Performance

DeepSeek‑R1 Performance

Highlights Comparison

Performance Benchmark

AIME24 Mathematics Competition: 79.74

LiveCodeBench: 73.54

LiveBench Comprehensive Reasoning: 82.1

IFEval Instruction Following: 85.6

BFCL Tool Invocation: 92.4

AIME24: 79.13

LiveCodeBench: 72.91

LiveBench: 81.3

Mathematics reasoning gap: 0.61

Code generation gap: 0.63

Significant advantage in instruction‑following ability

Technical Breakthrough

Stage‑wise reinforcement learning strategy:

1. Mathematics/Programming specialized RL training

2. General capability RL expansion based on Qwen2.5‑32B architecture

Mixture‑of‑Experts architecture:

671 billion parameters

Dynamic activation of 37 billion parameters

MLA multi‑head attention mechanism

QwQ uses a "specialist + generalist" training mode

DeepSeek relies on massive parameter stacking

Deployment Cost

24 GB VRAM single‑card operation

Token cost $0.25

Supports deployment on M4 Max laptops

Requires 16 × A100 GPU cluster

High operation‑maintenance cost

QwQ inference cost is only 1/10 of the competitor

Achieves consumer‑grade hardware breakthrough

Open‑Source Ecosystem

Apache 2.0 open‑source license

Free commercial authorization

Over 100 k derived models

MIT License open‑source

Forms the world’s largest open‑source model community

Lowers the barrier to AI technology adoption

Deploy QwQ‑32B and DeepSeek‑R1 locally to evaluate real‑world performance.

1.2 Deployment Methods

Pure Local Data‑Center Deployment

Advantages

High data security

Model can be fine‑tuned per specific needs

One‑time investment with long‑term usage

Disadvantages

High threshold for local fine‑tuning; requires AI specialists

Hardware scaling is relatively difficult

Hybrid Local + Cloud Deployment

Advantages

Sensitive data stays on‑premise while less‑sensitive workloads run in the cloud

Vertical‑domain fine‑tuned models can be deployed locally

Leverages cloud compute for horizontal scaling

One‑time local server investment with long‑term usage

Disadvantages

High threshold for local fine‑tuning; requires AI specialists

Overall cost is relatively higher than pure‑local

Pure Cloud Deployment

Advantages

Data security cannot be guaranteed

Vertical‑domain fine‑tuned models can be deployed in the cloud, with potential attack risk

Utilizes cloud compute for horizontal scaling

Disadvantages

Model fine‑tuning requires AI specialists

Annual cloud server rental costs are high

1.3 Plan Summary

Deployment Scheme

Investment Cost

Notes

Pure Local Data‑Center

Human resources: 3‑5 part‑time staff, ¥500k‑800k

Facility cost (local): ¥100k+

Prefer this scheme initially; switch to hybrid when annual business growth >30%.

Local + Cloud Hybrid

Human resources: 3‑5 part‑time staff, ¥500k‑800k

Facility cost: local ¥100k + cloud ¥100k+/year

Adopt based on business development needs.

Pure Cloud

Human resources: 3‑5 part‑time staff, ¥500k‑800k

Facility cost: ¥150k+/year

Requires long‑term cloud server rental; relatively expensive.

2. Knowledge Base Document Preparation

2.1 Document Format Requirements

Supported file types: txt, markdown, pdf, html, xlsx, docx, csv

Maximum size per document: 15 MB

Encoding: UTF‑8

File naming convention: Category_Title_Version_Date.md (e.g., Dify_Deployment_Guide_v1.0_20231001.md)

Content guidelines:

Markdown: clear headings (# H1, ## H2) and separators (---, ***)

Word: use Heading 1, Heading 2, Heading 3

Remove irrelevant parts such as headers and footers

2.2 Segmentation Modes

The knowledge base supports two segmentation modes:

General Mode : The system splits content into independent segments according to user‑defined rules. When a query is submitted, keywords are extracted and relevance scores are computed against each segment; the most relevant segments are sent to the LLM for answering.

Parent‑Child Mode : Builds a two‑level hierarchy (parent and child segments) to balance precise retrieval with contextual information, achieving both accurate matching and comprehensive context.

2.3 Indexing Methods

High‑Quality : Uses embedding models to convert segmented text blocks into vectors, enabling efficient compression and storage of large corpora and providing highly accurate query‑text matching.

Economic : Retrieves each block using only 10 keywords, reducing accuracy but eliminating embedding costs; results are selected via inverted‑index ranking. Users can upgrade to the high‑quality method later if needed.