LLMs

SWE 1.7 vs Claude Opus 4.8: Terminal coding benchmark showdown, SWE-bench Verified resolve rates, and real token cost economics in production swarms.

Evaluate Mixture-of-Experts routing entropy against expert saturation on DeepSeek V4 architectures to balance token distribution and eliminate tail latency.

Benchmark Lookahead Decoding against Medusa multi-head speculation on dual NVIDIA H100 GPUs across 100,000 inference requests for TTFT and memory bandwidth.

Benchmark KV cache pruning against chunked prefill in vLLM to optimize long-context serving throughput, reduce TTFT, and eliminate GPU memory exhaustion.

Analyze tiered KV cache offloading to NVMe SSDs with INT4 quantization and GPUDirect Storage to scale 512K context LLM serving at 70% lower GPU VRAM cost.

Benchmark speculative decoding using small draft models against multi-head Medusa architectures, analyzing acceptance rates, VRAM overhead, and generation speed.

Benchmark FP8 against AWQ 4-bit quantization on NVIDIA Blackwell GPUs, analyzing token throughput, memory bandwidth pressure, and perplexity degradation.

Discover how the vLLM V1 engine rewrite unifies chunked prefill scheduling with Radix prefix caching to achieve 2.8x higher throughput on multi-turn LLMs.

Discover how OpenAI Decisions API replaces continuous softmax temperature sampling with calibrated probability routing to eliminate deadlocks in AI swarms.

Explore disaggregated prefill and decode architecture in modern LLM serving to decouple compute-bound prompt ingestion from memory-bound token generation.

Analyze test-time compute scaling mechanics by comparing Monte Carlo Tree Search rollouts against Process Reward Model (PRM) pruning for complex reasoning.

Analyze the real economics of autonomous coding agents by comparing price per million tokens against true dollar cost per verified resolved issue on SWE-bench.