How much VRAM does Qwen2.5 32B need?
Qwen2.5 32B needs about 68 GB of GPU memory at FP16 and about 20.6 GB at 4-bit. Pick a precision below to see which ComputeBD AI Computer fits and what it costs per hour in taka.
You need about 68 GB of GPU memory to run Qwen2.5 32B at FP16 / BF16.
- Model weights 61.1 GB
- Conversation memory (KV cache) 1 GB
- Runtime & working space 5.9 GB
NVIDIA A100 is the most affordable AI Computer that fits — ৳638/hour, billed by the minute.
| AI Computer | Fits? | Speed (1 user) | Price |
|---|---|---|---|
| NVIDIA L424 GB | Too small |
≈ 3 tokens/s | ৳147/hr |
| NVIDIA A1024 GB | Too small |
≈ 5 tokens/s | ৳196/hr |
| NVIDIA L40S48 GB | Too small |
≈ 8 tokens/s | ৳343/hr |
| NVIDIA A10080 GB | Fits |
≈ 19 tokens/s | ৳638/hr |
| NVIDIA H10080 GB | Fits |
≈ 31 tokens/s | ৳1,047/hr |
| NVIDIA H200141 GB | Fits |
≈ 44 tokens/s | ৳1,390/hr |
| NVIDIA B200180 GB | Fits |
≈ 73 tokens/s | ৳1,652/hr |
| NVIDIA B300288 GB | Fits |
≈ 73 tokens/s | ৳1,880/hr |
Qwen2.5 32B at each precision
| Precision | GPU memory needed | Cheapest ComputeBD AI Computer |
|---|---|---|
| FP32full precision — rarely needed | 134 GB | NVIDIA B200 · ৳1,652/hr |
| FP16 / BF16standard — original quality | 68 GB | NVIDIA A100 · ৳638/hr |
| 8-bitalmost no quality loss | 37.1 GB | NVIDIA L40S · ৳343/hr |
| 4-bit (GGUF / AWQ / GPTQ)small quality loss, ~4× less memory | 20.6 GB | NVIDIA L4 · ৳147/hr |
- Made by
- Alibaba Qwen
- Parameters
- 32.8B
- Max context
- 131,072 tokens
- LoRA / QLoRA fine-tune
- ≈ 72.4 GB / 28.5 GB
- Hugging Face
- Qwen/Qwen2.5-32B-Instruct
How the estimate works
- Weights = parameters × bytes per parameter (FP16 = 2 bytes, 8-bit ≈ 1, 4-bit ≈ 0.56).
- KV cache (conversation memory) grows with context length and the number of people at once: 2 × layers × KV-heads × head size × tokens × 2 bytes.
- Runtime: about 1 GB for CUDA plus ~8 % of the weights for working space.
- Fine-tuning: LoRA keeps an FP16 copy of the model and trains ~1 % extra parameters; QLoRA keeps a 4-bit copy; full fine-tuning needs ~16 bytes per parameter. Gradient checkpointing is assumed.
- Speed: generating text for one user is limited by memory bandwidth, so tokens/second ≈ bandwidth × 60 % ÷ model size.
These are good planning estimates, not guarantees: the real figure depends on the software (vLLM, llama.cpp, Transformers…), settings and your data. Leave ~10 % spare.
VRAM needed, model by model
Frequently asked questions
How much VRAM does Qwen2.5 32B need?
About 68 GB at FP16 (original quality) and about 20.6 GB at 4-bit, for one user with a 4k context.
Which GPU can run Qwen2.5 32B?
At full quality it fits on an NVIDIA A100 (80 GB), ৳638/hour on ComputeBD.
How much VRAM to fine-tune Qwen2.5 32B?
About 72.4 GB with LoRA and 28.5 GB with QLoRA (sequence length 2,048, batch 1, gradient checkpointing). Estimate the time and cost with the fine-tuning cost estimator.
Try it on a real AI Computer
JupyterLab opens in about a minute. Pay in taka with bKash, billed by the minute — stop whenever you like.