KV Cache Quantization: Fitting Larger Context Windows on Single GPUs
The bottleneck for long-context agents is memory, not compute. Learn how to implement FP8 or INT8 KV caching to double your context length and survive inference at scale.
Tag: vLLM
RadixAttention vs PagedAttention: The New Frontier in Context Management
A deep dive into the mechanics of SGLang's RadixAttention and why it represents a breakthrough for multi-turn agentic workflows compared to vLLM's PagedAttention.
Hierarchical KV Caching: Scaling Context Beyond VRAM Limits
As context windows scale to a million tokens, the KV cache becomes too large for GPU memory. The solution is a multi-tiered cache that offloads data to CPU and NVMe without killing latency.
· Rajat Pandit · AI InfrastructureContinuous Batching in vLLM: Killing the Hardware Idle
If your GPUs are idling at 40% utilization during inference, you are burning capital on memory bottlenecks, not computation.
Single-Batch Inference: Speculative Decoding on an A100
See how speculative decoding performs for single-batch requests on an NVIDIA A100. We analyze acceptance rates, latency, and the mechanics of the draft model gamble.
The Efficiency Moat - Navigating the New Economics of AI Inference
As the AI industry moves from model training to large-scale deployment, the strategic bottleneck has shifted from parameter count to inference orchestration. This post explores how advanced techniques like RadixAttention, Chunked Prefills, and Deep Expert Parallelism are redefining the ROI of GPU clusters and creating a new standard for high-performance AI infrastructure.