How much VRAM does Llama 3.1 70B need?
Llama 3.1 70B needs about 144.3 GB of GPU memory at FP16 and about 42.2 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 144.3 GB of GPU memory to run Llama 3.1 70B at FP16 / BF16.
- Model weights 131.5 GB
- Conversation memory (KV cache) 1.3 GB
- Runtime & working space 11.5 GB
NVIDIA B200 is the most affordable AI Computer that fits — ৳1,652/hour, billed by the minute.
| AI Computer | Fits? | Speed (1 user) | Price |
|---|---|---|---|
| NVIDIA L424 GB | Too small |
≈ 1 tokens/s | ৳147/hr |
| NVIDIA A1024 GB | Too small |
≈ 3 tokens/s | ৳196/hr |
| NVIDIA L40S48 GB | Too small |
≈ 4 tokens/s | ৳343/hr |
| NVIDIA A10080 GB | Too small |
≈ 9 tokens/s | ৳638/hr |
| NVIDIA H10080 GB | Too small |
≈ 14 tokens/s | ৳1,047/hr |
| NVIDIA H200141 GB | Too small |
≈ 20 tokens/s | ৳1,390/hr |
| NVIDIA B200180 GB | Fits |
≈ 34 tokens/s | ৳1,652/hr |
| NVIDIA B300288 GB | Fits |
≈ 34 tokens/s | ৳1,880/hr |
Llama 3.1 70B at each precision
| Precision | GPU memory needed | Cheapest ComputeBD AI Computer |
|---|---|---|
| FP32full precision — rarely needed | 286.3 GB | Does not fit on one AI Computer |
| FP16 / BF16standard — original quality | 144.3 GB | NVIDIA B200 · ৳1,652/hr |
| 8-bitalmost no quality loss | 77.7 GB | NVIDIA H200 · ৳1,390/hr |
| 4-bit (GGUF / AWQ / GPTQ)small quality loss, ~4× less memory | 42.2 GB | NVIDIA L40S · ৳343/hr |
- Made by
- Meta
- Parameters
- 70.6B
- Max context
- 131,072 tokens
- LoRA / QLoRA fine-tune
- ≈ 154.1 GB / 59.6 GB
- Hugging Face
- meta-llama/Llama-3.1-70B-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 Llama 3.1 70B need?
About 144.3 GB at FP16 (original quality) and about 42.2 GB at 4-bit, for one user with a 4k context.
Which GPU can run Llama 3.1 70B?
At full quality it fits on an NVIDIA B200 (180 GB), ৳1,652/hour on ComputeBD.
How much VRAM to fine-tune Llama 3.1 70B?
About 154.1 GB with LoRA and 59.6 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.