LLMs

Compare Process Reward Models (PRM) and Outcome Supervision (ORM) in reasoning LLMs. Learn how inference-time step pruning cuts reasoning token costs by 48%.

Compare FlashAttention-3 and OpenAI Triton kernels on NVIDIA H100 GPUs. Discover how warp-specialization slashes long-context forward pass latency.

Compare FP8 and INT4 quantization on NVIDIA Blackwell B200 GPUs. Analyze Tensor Core throughput, memory bandwidth saturation, and perplexity retention.

Compare Tensor Parallelism and Pipeline Parallelism across Megatron-LM and vLLM. Analyze all-reduce GPU interconnect bottlenecks and pipeline bubbles.

Benchmark AWQ, GPTQ, and BitsAndBytes 4-bit quantization in vLLM. Analyze memory bandwidth saturation, perplexity degradation, and serving speed.

Compare continuous iteration-level batching against dynamic request-level batching. Analyze GPU Tensor Core saturation, queue bubbles, and 4x throughput.

Explore PagedAttention internals in vLLM. Understand virtual memory block allocation, zero-waste KV cache paging, and 4x throughput gains in LLM serving.

Benchmark speculative tree attention in vLLM. Compare Eagle recurrent features, Medusa prediction heads, and Lookahead cache algorithms for fast inference.

Benchmark KV cache offloading across DeepSpeed and vLLM. Analyze PCIe 5.0 vs NVMe throughput, GPU HBM bandwidth squeeze, and long-context decode latency.

Benchmark FlashDecoding++ vs FlashAttention-3 for ultra-long context LLM serving. Analyze memory bandwidth, asynchronous warp scheduling, and decode speed.

Master prompt compression with LLMLingua-2 to achieve 4x context reduction, cutting agent token costs by 72% while preserving extraction accuracy.

Compare chunked prefill vs disaggregated serving architectures to eliminate TTFT latency spikes and achieve deterministic SLA response times in LLM clusters.