How much VRAM does DeepSeek-R1-Distill-Qwen 32B need?
DeepSeek-R1-Distill-Qwen 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 DeepSeek-R1-Distill-Qwen 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 |
DeepSeek-R1-Distill-Qwen 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
- DeepSeek
- Parameters
- 32.8B
- Max context
- 131,072 tokens
- LoRA / QLoRA fine-tune
- ≈ 72.4 GB / 28.5 GB
- Hugging Face
- deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
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 DeepSeek-R1-Distill-Qwen 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 DeepSeek-R1-Distill-Qwen 32B?
At full quality it fits on an NVIDIA A100 (80 GB), ৳638/hour on ComputeBD.
How much VRAM to fine-tune DeepSeek-R1-Distill-Qwen 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
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