Continuous batching is the single largest throughput unlock for transformer inference on any hardware. NVIDIA stacks have spent three years optimizing around it: vLLM, TensorRT-LLM, and SGLang all converge on the same pattern of paged KV cache, request scheduling, and prefill/decode interleaving. MLX is younger, the runtime is different, and most NVIDIA intuitions do not survive contact with Apple's unified memory architecture.
RAG over a Shopify Plus product catalog is not the same problem as RAG over documents, knowledge bases, or codebases. The data is structured, it mutates constantly through orders and inventory updates, it has hard relevance signals from sales velocity and margin, and it lives behind a platform with strong opinions about how you read and write it. The generic LangChain tutorial that embeds your documents into Pinecone and calls it done falls apart at enterprise catalog scale within the first week of production traffic.
Shopify's native content management was never the strength of the platform. Custom metafields and Online Store 2.0 sections solve the simple cases. Once you have marketing teams who want to ship landing pages weekly, brand campaigns that span multiple regions, or editorial content that lives alongside the catalog, the native tooling runs out. Hydrogen makes the gap obvious because content rendering moves into your application code and your CMS choice becomes a first class architectural decision.
Tensor parallelism on NVIDIA is a transport problem. Splitting a 70B model across four A100s means moving attention shards across NVLink at every layer boundary, and the bandwidth ceiling is what bounds throughput. Apple Silicon does not have that problem because the GPU cores share one memory pool. That changes the calculus, and most NVIDIA intuitions transfer badly.
If you run LLMs locally on a Mac, you have probably been asked to choose between a GGUF file from Hugging Face and an MLX version of the same model. The default advice is to pick whichever your runtime supports and move on. That advice is wrong often enough to matter. The two formats quantize weights differently, store metadata differently, and behave differently under load. The right choice depends on your model size, your hardware, and what you are optimizing for.
Shopify's built in Storefront API search works fine for catalogs under roughly 5,000 SKUs and shoppers who arrive with a clear query in mind. Once you cross 10,000 SKUs, add faceted filtering on more than three attributes, or need ranking customization (boost in stock items, demote slow movers, surface new arrivals on certain queries), the native search path stops being sufficient. The enterprise Hydrogen storefronts we work on at Contra Collective almost always reach for a dedicated search index by the time the catalog gets serious.
The model weights are not what kills your context length on Apple Silicon. The KV cache is. We have measured this across dozens of configurations on M4 Pro, M4 Max, and M5 Max hardware, and the same pattern shows up every time: engineers size unified memory for the model file, then watch their inference server OOM at 16K or 32K tokens because nobody did the cache math.
The headless Shopify decision in 2026 has narrowed to two serious frameworks: Shopify Hydrogen and Next.js Commerce. Both ship a React storefront detached from Liquid. Both target Shopify Plus merchants who want full design control and a modern frontend stack. They make meaningfully different trade-offs on data fetching, rendering, hosting, and how deep the integration with Shopify's primitives runs.
The headless CMS market in 2026 has consolidated around three names that show up on every enterprise commerce evaluation: Sanity, Contentful, and Strapi. Each one wins on a different axis. Contentful is the safe managed bet with the deepest enterprise feature set. Sanity is the developer-experience pick with the best structured content tooling in the market. Strapi is the open-source self-hosted option for teams that want full control of their content infrastructure.
vLLM is the production-grade inference engine that won the throughput conversation on CUDA hardware. Continuous batching, PagedAttention, prefix caching, speculative decoding. None of that, historically, ran on Apple Silicon. Search data for terms like omlx vs llama.cpp, vmlx, and vllm vs mlx reveals real demand for a bridge between the two stacks, much of it expressed as typos because the integration story is genuinely confusing.
LM Studio has a feature that feels too convenient to be true: it serves a local LLM via OpenAI-compatible REST API. Load a model, click "start server," and your code that calls client.chat.completions.create(model='gpt-4', ...) works unchanged, hitting local inference instead of OpenAI.
The M5 Max arrived in May 2026 with 40GB unified memory, up from M4 Max's 36GB. On paper, that is a 11% increase. In practice, for local inference, it unlocks a new class of models and quantization strategies.
The search infrastructure decision for headless commerce has become more interesting, not less, since Algolia stopped being the only serious answer. In 2026, three open-or-managed options dominate the category: Algolia (managed, mature, expensive), Typesense (open-source, simple, fast), and Meilisearch (open-source, developer-experience-led, increasingly capable). Pick the wrong one and you are either paying too much, operating too much, or fighting the engine's defaults for the next two years.
If you have decided to host an LLM on Apple Silicon and you have already picked your runtime (MLX or llama.cpp), the next question is the server. Both projects ship an HTTP server that speaks the OpenAI API: mlx_lm.server from the MLX-LM project, and llama-server from llama.cpp. Either one will turn a loaded model into a /v1/chat/completions endpoint your backend can call. The interesting question is which one belongs in your production stack.
When teams deploy local inference on M-series hardware, they face an architectural fork. MLX is native: it targets Apple Silicon directly, uses Metal acceleration natively, and integrates tightly with the platform. vLLM is portable: it brought GPU serving patterns to Apple Silicon through Metal support, brings production-grade batching, and treats your Mac like a server. Both will run models. Only one fits your workload.
The e-commerce search infrastructure conversation shifted in 2026. Three years ago, it was simple: Elasticsearch or Algolia. In 2024, Elastic ended the free tier and AWS forked OpenSearch. The economics changed overnight, and now the calculus is about operational cost, not just features.
If you've benchmarked vLLM against Ollama or llama.cpp on the same hardware and wondered why vLLM consistently achieves 2x to 5x higher throughput, the answer is PagedAttention. It's not magic, but it's a genuinely clever memory optimization that changes how you reason about local inference at scale.
The llama.cpp vs vLLM conversation usually ends with "both are fast." That's misleading. They're fast at different things, and choosing between them requires understanding the architectural gap.