Process multiple requests simultaneously for better performance
Learn how to handle multiple conversations concurrently
Neural network weights stored in memory
Key Insight: The model weights are loaded once and shared across all sequences. Each sequence maintains its own independent conversation history, allowing multiple conversations to run in parallel without interfering with each other.
Model weights (often 4-8GB+) are loaded once and shared. Only conversation histories are duplicated, which is much smaller (~few MB per sequence).
Each sequence maintains its own conversation context. User A's conversation doesn't affect User B's, even though they share the same model.
Multiple sequences can process requests simultaneously. With GPU support, this provides true parallelism and significant performance improvements.
All sequences use the same underlying model, ensuring consistent behavior and quality across all parallel conversations.
Promise.all() starts all promises simultaneously and waits for all to complete. With multiple context sequences, each sequence processes independently while sharing the same model weights. This enables true parallel execution on GPUs, dramatically reducing total processing time.
Sweet spot: Usually 512-1024 for consumer GPUs
Different ways to structure parallel processing for LLM applications
Multiple users share a pool of model instances. A load balancer distributes requests across available sequences.
Different specialized agents work on the same task simultaneously, each using their own model instance.
More sequences = more memory needed
Sequences share total context space. More sequences = less history per sequence
With GPU: True parallel. CPU only: Interleaved processing (still helps!)
Only helps with independent requests, not dependent sequential steps
Parallelism is essential for production AI agent systems
Sequences share model but maintain independent state
Promise.all enables concurrent JavaScript execution
Memory is the limit - more sequences need more VRAM