VRAM
Dedicated memory on a graphics card, used to store the model weights and intermediate data required for GPU computation; its capacity directly determines the scale of models that can be loaded.
Timeline (last 90 days)
On Windows devices, VRAM usually refers to the dedicated VRAM on a discrete graphics card, with fixed capacity and high bandwidth.
In MoE models, the attention layers, embedding layers and shared layers are used for every token, are small in size, and should be placed in VRAM.
When a model runs, its weights need to be loaded into RAM (system memory) or VRAM (video memory), and having large storage capacity does not mean the model can actually run.
A model must fit entirely within memory that the GPU can directly access (GPU VRAM or unified memory) to be truly usable; it cannot reside on an SSD or regular system memory.
VRAM is the most critical parameter for determining whether a large model can run smoothly locally; once a model exceeds the VRAM capacity, speed drops dramatically or the model may fail to run entirely.
VRAM production capacity has been locked up by fulfillment contracts spanning the next decade.
Running medium-scale code models locally for inference typically requires a discrete GPU with more than 16 GB of VRAM to run smoothly.
Whether a model fits entirely in VRAM is more important than the absolute size of the model file; once CPU offloading occurs, the data transfer overhead between GPU and CPU negates the space advantage of a smaller model.
Agent inference latency is closely related to GPU VRAM, and KV Cache management and batching strategies affect agent response time.
机器学习笔记本内存建议至少16GB，系统内存与显存是完全独立且不能互相替代的两个资源
All Facts (11)
显存带宽与系统内存带宽之间相差大约10倍
85%UnverifiedOn Windows devices, VRAM usually refers to the dedicated VRAM on a discrete graphics card, with fixed capacity and high bandwidth.
50%UnverifiedIn MoE models, the attention layers, embedding layers and shared layers are used for every token, are small in size, and should be placed in VRAM.
50%UnverifiedWhen a model runs, its weights need to be loaded into RAM (system memory) or VRAM (video memory), and having large storage capacity does not mean the model can actually run.
50%UnverifiedA model must fit entirely within memory that the GPU can directly access (GPU VRAM or unified memory) to be truly usable; it cannot reside on an SSD or regular system memory.
50%UnverifiedVRAM is the most critical parameter for determining whether a large model can run smoothly locally; once a model exceeds the VRAM capacity, speed drops dramatically or the model may fail to run entirely.
50%UnverifiedVRAM production capacity has been locked up by fulfillment contracts spanning the next decade.
50%UnverifiedRunning medium-scale code models locally for inference typically requires a discrete GPU with more than 16 GB of VRAM to run smoothly.
50%UnverifiedWhether a model fits entirely in VRAM is more important than the absolute size of the model file; once CPU offloading occurs, the data transfer overhead between GPU and CPU negates the space advantage of a smaller model.
50%UnverifiedAgent inference latency is closely related to GPU VRAM, and KV Cache management and batching strategies affect agent response time.
50%Unverified机器学习笔记本内存建议至少16GB，系统内存与显存是完全独立且不能互相替代的两个资源
50%