What is Tensor Parallelism?
Tensor Parallelism splits individual layers' operations across multiple devices, enabling models with layers too large for single GPU memory. Tensor parallelism provides fine-grained parallelism for extremely large models.
This LLM training and alignment term is currently being developed. Detailed content covering technical concepts, implementation approaches, best practices, and practical considerations will be added soon. For immediate guidance on LLM training strategies, contact Pertama Partners for advisory services.
Tensor parallelism enables inference serving of models too large for single GPU memory while maintaining the low latency that interactive applications require. This technique is essential for deploying 70B+ parameter models with response times under two seconds for real-time user-facing products. Organizations mastering tensor parallelism serve premium model capabilities at competitive latency benchmarks without requiring exotic specialized hardware.
- Splits matrix operations within layers across devices.
- Enables layers exceeding single GPU memory capacity.
- High communication requirements between devices.
- Most effective within fast-interconnected GPU nodes.
- Requires framework support (Megatron-LM, DeepSpeed).
- Typically combined with pipeline and data parallelism (3D parallelism).
- Deploy tensor parallelism within single-node multi-GPU configurations where NVLink interconnects provide the 600+ GB/s bandwidth that tensor splitting demands.
- Partition attention heads and feed-forward layers across GPUs using column and row parallelism patterns that minimize synchronization communication volume.
- Benchmark tensor parallel efficiency against pipeline parallel alternatives at your specific model size since the optimal strategy shifts across parameter count ranges.
Common Questions
When should we fine-tune vs. use pretrained models?
Fine-tune when domain-specific performance is critical and you have quality training data. Use pretrained models with prompting for general tasks or when training data is limited. Consider parameter-efficient methods like LoRA for cost-effective fine-tuning.
What are the costs of training LLMs?
Training costs vary dramatically by model size, data volume, and compute infrastructure. Small models may cost thousands, while frontier models cost millions. Most organizations fine-tune rather than pretrain, reducing costs by 100-1000x.
More Questions
Implement RLHF or DPO alignment, extensive red-teaming, safety evaluations, and guardrails. Monitor for unintended behaviors in production. Safety is ongoing process, not one-time activity.
References
- NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology (NIST) (2023). View source
- Stanford HAI AI Index Report 2025. Stanford Institute for Human-Centered AI (2025). View source
Flash Attention is an optimized attention algorithm that reduces memory usage and increases speed by recomputing attention on-the-fly rather than materializing full attention matrices. Flash Attention enables longer contexts and faster training for transformer models.
Ring Attention distributes attention computation across devices in a ring topology, enabling extremely long context windows by parallelizing sequence dimension. Ring Attention allows processing of contexts exceeding single-device memory.
Sparse Attention computes attention for only a subset of token pairs using predefined patterns, reducing computational complexity from quadratic to near-linear. Sparse attention enables longer context windows by limiting attention computation.
Sliding Window Attention restricts each token to attend only to nearby tokens within a fixed window, reducing complexity to linear while maintaining local context. Sliding window enables efficient processing of long sequences.
Grouped Query Attention (GQA) shares key-value pairs across groups of query heads, reducing memory and computation for multi-head attention while maintaining quality. GQA provides middle ground between multi-head and multi-query attention.
Need help implementing Tensor Parallelism?
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