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
Radix Attention in SGLang vs. PagedAttention
Radix attention (RadixAttention) is a context management breakthrough. Discover how SGLang's radix tree cache mechanism optimizes multi-turn workflows and compares 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 InfrastructurevLLM Continuous Batching & PagedAttention: Maximizing Throughput
vLLM continuous batching combined with PagedAttention dramatically increases inference throughput. Learn how this architecture eliminates KV cache fragmentation and boosts GPU utilization by 3x.
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.