Alibaba · Qwen2.5 · based on Qwen/Qwen2.5-32B
Qwen2.5-32B-Instruct
A 32B dense open-weight model under Apache-2.0 with 128K context and broad multilingual coverage; fits on two 48 GB GPUs at INT4.
2,234,705 downloads · 362 likes · 27,641 GitHub stars · Model card · GitHub · synced 2026-09-19
Specifications
- Parameters
- 32.8B
- Architecture
- dense · qwen2
- Layers
- 64
- Hidden size
- 5,120
- KV heads · head dim
- 8 · 128
- Vocabulary
- 152,064
- Native dtype
- bfloat16
- Context window
- 128,000 tokens
- Released
- 2024-09-17
- Last modified on Hub
- 2024-09-25
Features & licensing
- License
- Apache-2.0
- Commercial use
- Yes
- Modalities
- text
- Tool / function calling
- Yes
- Reasoning mode
- No
- Languages
- EN, ZH, UR, AR, FR, DE, ES, JA
- Quantised variants on Hub
- AWQ, EXL2, FP8, GGUF, GPTQ, bitsandbytes
- Pipeline
- text-generation
Task fit
Editorial scores (0–100) used by the recommendation engine.
- Chat85
- RAG / Q&A86
- Code80
- Summarisation84
- Extraction85
- Agents78
Hardware to run Qwen2.5-32B-Instruct
Weights need about 72 GB at FP16, 38 GB at INT8 and 22 GB at INT4. The KV cache adds roughly 262 MB per 1,000 tokens per request. For a reference workload of 5 requests per second with 1,500 input and 300 output tokens, total GPU memory is around 35.6 GB, which fits on 1 × NVIDIA A100 (80 GB).
| Configuration | VRAM | Utilisation | Est. first token | Cloud / month |
|---|---|---|---|---|
| 2 × NVIDIA L4 (24 GB) | 48 GB | 74 % | ~0.5 s | $1,168 |
| 1 × NVIDIA L40S (48 GB) | 48 GB | 74 % | ~0.2 s | $1,387 |
| 1 × NVIDIA A100 (80 GB)recommended | 80 GB | 45 % | ~0.1 s | $2,336 |
| 1 × NVIDIA H100 (80 GB) | 80 GB | 45 % | ~0.0 s | $3,285 |
Estimates only. Run an assessment for your own traffic.
Qwen2.5-32B-Instruct balances quality and cost for retrieval and chat workloads. Its permissive license makes it a common choice for regulated deployments.