How much video memory is needed for private deployment of large models?
Deeply understand the memory requirements of large AI model deployment to avoid huge waste of hardware costs.
Core content:
1. The two core components of large model memory requirements: static parameter memory and dynamic calculation cache
2. Calculation formulas for parameter memory and KV cache and actual case analysis
3. Hidden cost: the impact of intermediate activation values on memory and optimization methods
Recently, I have come into contact with many AI projects.
The customer asked right away: How much computing power is needed to deploy a large model?
"Boss, how much video memory do I need for a 32B model?" "Is 90GB enough?" "No, you may have underestimated by 30%!"
As billion-scale models become the industry standard, errors in video memory estimation can easily lead to hardware cost deviations of hundreds of thousands.
Today, I will try to explain computing power evaluation in a simple way by taking the deployment of the QwQ-32B model and the FB16 precision video memory requirement as an example. (A large autoregressive language model developed by Alibaba's Qwen team based on the Qwen2.5 architecture).
PS: On the day the article was published, Qwen3 was released, which greatly reduced the performance requirements. A related article will be published later to introduce it.
Video memory core components
The memory usage of large models is mainly composed of two modules: static parameter memory and dynamic calculation cache . The two together determine the baseline standard for hardware selection.
1. Parameter memory: static memory of the model
Calculation formula :
Parameter quantity x parameter precision byte number = parameter video memory (GB)
Take QwQ-32B as an example : Parameter size: 32B (32 × 10⁹ 32 billion) Precision: FP16 (2 bytes/parameter) Calculated according to the formula: 32×10⁹×2 = 64×10⁹bytes ≈ 64 GB
Calculation trap :
The above is a common reasoning scenario (AI chat). If it is a training scenario , additional gradient storage is required and the video memory is doubled to 128GB .
In addition, 1B=10⁹ (International Unit) ≠ Chinese "亿" (10⁸)
PS: In training scenarios, gradient calculation is usually enabled, which is mainly used to optimize model parameters to minimize prediction errors. It is a key step in training scenarios. In this scenario, the video memory must be at least doubled.
2. KV Cache: Memory Black Hole for Dynamic Reasoning
To speed up the inference process, it is necessary to useKV Cache, which is a standard feature of Transformer. The following calculations are based on typical Transformer model estimates. When the model processes long sequences, it needs to store a Key-Value vector for each token.
Calculation formula :
Number of layers × 2 × number of heads × head dimension × context length × number of element bytes = KV cache memory (GB)
Take QwQ-32B as an example :
• Number of layers (L): 40 • Number of attention heads (h): 64 • Dimensions of each head (d_head): 128 • Context length (S): 16,000 tokens (16k) • Number of bytes per element: 2 bytes for FP16
Calculated according to the formula: 40 layers × 2 × 64 heads × 128 dimensions × 16,000 tokens × 2 bytes ≈ 19.54GB
It is worth mentioning that DeepSeek's pioneering MLA technology reduces KV Cache by 93%, effectively reducing the requirements for high-performance GPUs in training and inference scenarios.
Hidden costs that cannot be ignored
1. Intermediate activations: transient memory of computational processes
Intermediate activation values are temporary data generated by calculations at each layer during the forward propagation of the neural network. They are like semi-finished products at each step in the cooking process and must be temporarily saved for subsequent operations.
• Training scenario : Activation memory can reach 5-7 times the number of parameters (32x5 ~ 32x7 requires about 160-224GB) • Inference scenario : Operator fusion (such as Flash Attention) can significantly reduce the activation value usage, but it may still occupy 5~10GB of video memory
2. Framework overhead: system "management fee"
Frameworks such as PyTorch require 10-20% additional video memory to handle memory management, just like aisle space needs to be reserved in a warehouse for people/goods to enter and exit.
3. Security redundancy: the last line of defense for system stability
To ensure stable operation, it is recommended to reserve at least 20% buffer space. Therefore, in normal inference scenarios, the total video memory calculation formula is:
Total video memory = (parameter video memory + KV cache) × 1.2
(64+19.54)×1.2 ≈ 100GB
It is safer to choose a 128GB graphics card (the smallest card memory is 24G/card).
Recommended hardware configuration
Scenario-based Selection Guide
| Training scenario | |||||
| Batch Inference | |||||
| Single card reasoning |
The KV cache requirement in the training scenario is large, and the intermediate value activation memory can reach 5-7 times the number of parameters. (The calculation process is above)
Hardware card video memory reference: A100 80GB: can meet the single card extreme reasoning (video memory optimization needs to be enabled) A100 40GB: requires model parallelism (more than 2 cards) H100 80GB: recommended for future deployment
Of course, you can also choose some domestic cards, such as the Muxixiyun C series, Hygon DCU, Ascend series, etc.
Summarize
When asked again: "How much video memory is needed for a privately deployed 32B model?"
You can confidently respond: "It takes about 90GB to meet basic reasoning needs, and 128GB is required for security and stability!"
