How much VRAM does Qwen2.5 14B need?
Qwen2.5 14B needs about 31.5 GB of GPU memory at FP16 and about 10.1 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 31.5 GB of GPU memory to run Qwen2.5 14B at FP16 / BF16.
- Model weights 27.6 GB
- Conversation memory (KV cache) 0.8 GB
- Runtime & working space 3.2 GB
NVIDIA L40S is the most affordable AI Computer that fits — ৳343/hour, billed by the minute.
| AI Computer | Fits? | Speed (1 user) | Price |
|---|---|---|---|
| NVIDIA L424 GB | Too small |
≈ 6 tokens/s | ৳147/hr |
| NVIDIA A1024 GB | Too small |
≈ 12 tokens/s | ৳196/hr |
| NVIDIA L40S48 GB | Fits |
≈ 18 tokens/s | ৳343/hr |
| NVIDIA A10080 GB | Fits |
≈ 41 tokens/s | ৳638/hr |
| NVIDIA H10080 GB | Fits |
≈ 68 tokens/s | ৳1,047/hr |
| NVIDIA H200141 GB | Fits |
≈ 97 tokens/s | ৳1,390/hr |
| NVIDIA B200180 GB | Fits |
≈ 162 tokens/s | ৳1,652/hr |
| NVIDIA B300288 GB | Fits |
≈ 162 tokens/s | ৳1,880/hr |
Qwen2.5 14B at each precision
| Precision | GPU memory needed | Cheapest ComputeBD AI Computer |
|---|---|---|
| FP32full precision — rarely needed | 61.3 GB | NVIDIA A100 · ৳638/hr |
| FP16 / BF16standard — original quality | 31.5 GB | NVIDIA L40S · ৳343/hr |
| 8-bitalmost no quality loss | 17.6 GB | NVIDIA L4 · ৳147/hr |
| 4-bit (GGUF / AWQ / GPTQ)small quality loss, ~4× less memory | 10.1 GB | NVIDIA L4 · ৳147/hr |
- Made by
- Alibaba Qwen
- Parameters
- 14.8B
- Max context
- 131,072 tokens
- LoRA / QLoRA fine-tune
- ≈ 33.5 GB / 13.7 GB
- Hugging Face
- Qwen/Qwen2.5-14B-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 14B need?
About 31.5 GB at FP16 (original quality) and about 10.1 GB at 4-bit, for one user with a 4k context.
Which GPU can run Qwen2.5 14B?
At full quality it fits on an NVIDIA L40S (48 GB), ৳343/hour on ComputeBD.
How much VRAM to fine-tune Qwen2.5 14B?
About 33.5 GB with LoRA and 13.7 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.