The sixth 11-868 assignment is the first to set aside your homemade MiniTorch and use industry frameworks. Two problems, 50 points each. Problem 1: edit a DeepSpeed training script to turn on LoRA so Llama-2-7B can train on two 16GB V100s. Problem 2: fill in the TODOs of an SGLang inference script and tune parameters to make generation faster. The two problems want conflicting GPUs: SGLang doesn't support V100, so you need an L40S, A6000, or A100. The spring due date was April 13, and the assignment page publishes no grading tests.
11-868 spends two lectures on one question: how does an inference server handle many requests at once without wasting KV cache on the GPU? Lecture 22 (Lei Li) starts from SGLang's scheduling loop: ORCA's continuous batching, RadixAttention's radix tree for KV, sorting and routing by prefix hit rate, and hiding CPU scheduling behind GPU compute. Lecture 24 is given by vLLM author Woosuk Kwon: PagedAttention cuts KV cache into fixed-size blocks and virtualizes them with a block table, taking the batch on one A100 from 8 to 40. The second half covers how vLLM cuts CPU overhead, uses piecewise CUDA graphs, splits models across GPUs, and manages memory for hybrid architectures.
Open-source models now match closed-source on coding benchmarks, but self-hosting isn't just picking a model — vLLM handles high-concurrency production serving, SGLang is 29% faster on prefix-heavy workloads, Ollama is the local dev default, and llama.cpp runs on the least hardware. A100 cloud rentals run ~$1.4-2.2/hr; self-hosting breaks even at roughly 100M tokens/month.
The key question in self-hosted inference isn't how fast the engine is — it's your GPU utilization. A fully saturated A100 costs ~$0.70 per million output tokens; at 10% utilization that becomes $7, more than most cloud APIs. This overview maps seven tools across three layers to help you decide which layer you need.
Self-hosted inference servers fall into three layers: execution engine (llama.cpp), serving engine (vLLM, SGLang), and model management platform (Ollama, Xinference, Triton). Picking the right layer matters more than picking the right tool — ask where your bottleneck is before deciding where to add complexity.
Xinference wraps vLLM, SGLang, llama.cpp, Transformers, and MLX under a single management layer, using a Web UI and OpenAI-compatible API to manage LLMs, embedding, rerank, speech, and image models — suited for self-hosted deployments that need multiple model types to coexist. But the management layer's parsing logic also creates a larger attack surface than pure serving engines (CVE-2026-61539 is a case study).
SGLang is an inference engine for generative models. RadixAttention reuses KV cache across shared prefixes, while OpenAI-compatible APIs, structured output, and multi-GPU parallelism support production LLM serving; it is not a complete product backend.
The official provider directory now lists 60 entries. The most common failure when attaching a local model is writing Ollama's base URL with /v1 — that breaks tool calling, and the model starts emitting raw tool-call JSON as plain text.