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Also known as: Video RAM
Dedicated graphics memory on a GPU used to store textures, tensors, model parameters, and activations during rendering or training.
DESCRIPTION
VRAM is on-device GPU memory that enables high-throughput access for graphics and compute workloads. In deep learning, VRAM capacity dictates batch size, model size, and training throughput. Technologies include GDDR and HBM; bandwidth and capacity matter. Managing VRAM via memory-efficient models, gradient checkpointing, and mixed precision reduces footprint. VRAM is distinct from system RAM and typically not pageable without performance penalties. Monitoring VRAM usage prevents out-of-memory errors.
ALTERNATIVE DEFINITIONS
Wikipedia
Video RAM (VRAM) is a type of random-access memory used to store image data for a computer display.
Defines VRAM historically in graphics contexts as display image memory.
NVIDIA
VRAM on a graphics card stores textures, framebuffers, and other GPU data; modern GDDR6 provides high bandwidth to feed the GPU.
Highlights modern GDDR6 and role in feeding GPU.
AMD
Dedicated graphics memory (VRAM) is used by the GPU to store and quickly access resources required for rendering and compute workloads.
Emphasizes rendering and compute uses across AMD GPUs.
RELATED TERMS
Graphics Processing Unit Memory Bandwidth Model Parameters Activations Batch Size CUDA Tensor Cores Memory Footprint PCIe
COMPETING TERMS
System RAM HBM
KNOWLEDGE AREAS
Infrastructure AI MLOps Data Storage & Operations
TARGET AUDIENCE
AI Engineer MLOps Engineer Hardware Engineer Cloud Architect
EXTERNAL SOURCES
- Wikipedia - Video RAM
- PyTorch - CUDA memory management guidance
- NVIDIA - GPU memory optimization techniques
- Hugging Face - Strategies to fit big models
TOP QUESTIONS
How does VRAM affect deep learning training?
VRAM limits batch size and model size. Insufficient VRAM causes out-of-memory errors and throttles throughput.
Category: Example
What is a common mistake when managing VRAM?
Not freeing tensors or forgetting torch.no_grad() during evaluation leads to memory leaks.
Category: Mistakes
How can I reduce VRAM usage?
Use mixed precision, gradient checkpointing, smaller batches, or model pruning.
Category: HowTo
What is the risk of VRAM exhaustion?
Out-of-memory errors crash training; fallback to CPU or paging drastically slows performance.
Category: Risks
How does VRAM differ from system RAM?
VRAM is on-GPU memory with high bandwidth; system RAM is host memory accessed via slower interconnects.
Category: Misunderstanding
Is HBM a type of VRAM?
HBM is high-bandwidth memory used on GPUs; it serves as VRAM with much higher throughput than GDDR.
Category: Opinion
What’s a hot topic around VRAM?
Efficient LLM training with limited VRAM via tensor parallelism and offloading.
Category: Topic
How to monitor VRAM usage?
Use nvidia-smi, PyTorch memory stats, or vendor profilers to track allocation and fragmentation.
Category: HowTo
Does mixed precision help VRAM?
Yes, FP16/BF16 halves tensor sizes, increasing effective capacity and throughput.
Category: Example