As an AI Infrastructure Engineer, you must build these projects. 1.) High-Throughput Serving Engine Tuning Build: Deploy vLLM or SGLang, tuning chunked prefill, continuous batching and max-num-seqs for a specific GPU architecture. Why: Default configs waste 60% of VRAM. Squeezing the hardware is the baseline job. 2.) KV-Cache Prefix Routing Proxy Build: A load balancer that hashes incoming system prompts and routes requests to the specific replica holding that KV-cache block in memory. Why: Reused context is free speed. This alone can slash Time-To-First-Token (TTFT) by 80%. 3.) Custom Fused Triton Kernel Build: Write a fused RMSNorm or RoPE (Rotary Position Embedding) kernel in Triton and benchmark it against the native PyTorch implementation. Why: Python overhead and memory reads kill inference. Fused kernels save milliseconds that compound at scale. 4.) Speculative Decoding Pipeline Build: A serving setup where a local 2B "draft" model generates tokens in parallel and a 70B "target" model verifies them in a single forward pass. Why: 2x decode speed at zero quality loss is the closest thing to a free lunch in inference. 5.) Disaggregated Prefill/Decode Cluster Build: Separate GPU pools where compute-heavy prefills and memory-bound decodes are routed to different hardware optimized for each phase. Why: This is the exact architecture frontier labs use to maximize MFU (Model Flops Utilization) in 2026. 6.) FP8 Quantization & Calibration Harness Build: A pipeline that calibrates a model for FP8 inference, serves it via TensorRT-LLM and measures the exact perplexity drop against throughput gains. Why: Quantization is the only way to fit frontier models on edge GPUs and protect your cloud margins. 7.) Queue-Based GPU Autoscaler (KEDA) Build: An autoscaler that spins up spot GPU nodes based on pending token queue depth and gracefully drains them when the queue empties. Why: Idle H100s burn $3+/hr. FinOps and scheduling are now core infra responsibilities. 8.) Complexity-Routing AI Gateway Build: A proxy that analyzes prompt entropy and token length to instantly route simple queries to a quantized local 8B model and complex reasoning to a frontier API. Why: Unit economics decide if your AI product survives the next API price hike. 9.) Multi-Node Tensor Parallelism Setup Build: Shard a 70B+ parameter model across 4 nodes using NCCL over RDMA/RoCE v2 and profile the exact network bottleneck between GPUs. Why: Single-node inference is dead for frontier models. You must master the interconnect. 10.) Semantic Cache Layer Build: A Redis + Vector DB proxy that intercepts requests, checks for >98% embedding similarity, and returns cached outputs without hitting the GPU. Why: The cheapest and fastest token is the one you never generate. 11.) DCGM Observability & Throttling Dashboard Build: A Grafana stack tracking GPU temp, SM occupancy, VRAM OOMs and PCIe TX/RX to catch silent hardware throttling. Why: You cannot fix an SLO violation if your dashboard only shows HTTP 200s while the GPU is melting. 12.) MoE (Mixture of Experts) Load Profiler Build: An expert-parallel deployment tracking token-to-expert routing to identify and fix expert load imbalance and communication overhead. Why: Sparse MoE models are the 2026 standard, and naive routing destroys their latency advantage. 13.) Inference Chaos & Fault Tolerance Suite Build: A harness that injects network partitions, simulated GPU ECC errors, and OOMs to test the cluster's ability to migrate KV-caches and recover. Why: At scale, hardware fails daily. Your architecture must heal itself. 14.) Checkpointing & Weight Streaming Engine Build: A system that streams model weights directly from an S3 bucket to GPU VRAM on cold-start, bypassing local disk bottlenecks. Why: Cold-start latency kills autoscaling. Streaming weights directly to memory is the ultimate optimization. 15.) Public Inference Benchmark Teardown Build: 3 published deep-dives with Pareto curves (Cost vs TTFT vs Throughput) comparing vLLM vs SGLang vs TensorRT-LLM on your specific hardware. Why: Public proof of hardware mastery and methodology gets you hired instantly by top AI labs. Systems that prove you can serve, optimize & scale when silicon meets intelligence. Bookmark & Repost.

They built a 37K cult using a fake avatar to hijack the algorithm. Code never lies and who is actually behind it is revealed inside. Model credit: @ViC305 & @Blackfrost_ai (not participants). Read the receipts behind Mia’s AI Lab 👇

Introducing Prime Sandboxes: MicroVM sandboxes purpose-built for RL training. Model training requires running tens of thousands of concurrent sandboxes, leading to complex and costly configuration. We built Prime Sandboxes for our own team. Today we're releasing them publicly.

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which of your favorite token brokers are contributing back to open source ai? shall we count the ways? a slice of data: number of PRs upstreamed and merged into the most popular oss inference engines, by PR contributor org