CNBC Daily Open: SK Hynix and Micron join the $1 trillion club as AI memory rally accelerates

Baseten documents a training and inference platform at docs.baseten.co where teams deploy models via the open-source Truss framework or call hosted Model APIs without standing up infrastructure. Config-only Truss deployments point at Hugging Face checkpoints, select GPU resources, and engines such as TensorRT-LLM; `truss push` builds optimized containers and exposes OpenAI-compatible sync endpoints like `https://model-{model_id}.api.baseten.co/environments/production/sync/v1`. Custom architectures use a Truss `Model` class with `load` and `predict` in `model.py`. Model APIs provide immediate OpenAI-SDK-style access to catalog models (DeepSeek, Qwen, GLM, and others listed in docs) using `BASETEN_API_KEY`.

Modal documents a serverless cloud at modal.com where engineers run compute-intensive Python with zero infrastructure configuration: deploy OpenAI-compatible LLM services, batch workflows, job queues, GPU training and fine-tuning, and thousands of isolated Sandboxes for agent-generated code. Official guides show defining apps with `@app.function`, container images via `modal.Image`, and GPU types in code rather than YAML. Modal states pricing is per-second serverless usage with pooled capacity across major clouds, and supports calling functions from JavaScript/Go clients in addition to Python.

GroqCloud exposes hosted language, speech, and compound workloads through Groq’s HTTP APIs. Documentation highlights compatibility with OpenAI client libraries when you point `base_url` at Groq’s OpenAI-compatible endpoint and supply a Groq API key, alongside first-party Groq SDKs for Python and JavaScript. Pricing pages publish per-model token rates (USD) for on-demand inference.

The zilliztech/mcp-server-milvus project (documented at milvus.io/docs/milvus_and_mcp.md) exposes Milvus vector-database operations to MCP clients such as Claude Desktop and Cursor. The recommended launch path is `uv run src/mcp_server_milvus/server.py --milvus-uri http://localhost:19530` without a separate install step, with optional `MILVUS_URI`, `MILVUS_TOKEN`, and `MILVUS_DB` environment variables. Tools listed in Milvus docs include `milvus-text-search`, `milvus-hybrid-search`, `milvus-multi-vector-search`, `milvus-query`, and `milvus-count` for collection management, semantic retrieval, filtered hybrid search, and entity counts.

Structures verification of public claims about AI-driven memory shortages, high-bandwidth memory (HBM) demand, and trillion-dollar memory-chip valuations into an evidence checklist for finance, procurement, and platform teams. The workflow separates analyst price-target moves, year-to-date equity rallies, and vendor statements about agentic-AI workloads from independently observable supply signals (long-term agreements, stated capacity constraints, peer pricing power). It cites CNBC reporting that Micron crossed a $1 trillion market cap on May 26, 2026 after UBS raised its price target from $535 to $1,625, and that SK Hynix joined the trillion-dollar club on May 27, 2026 with shares up roughly 250% year to date amid AI chip demand lifting South Korea's Kospi—without endorsing any single stock call.

Turns public semiconductor announcements into a verification checklist when vendors claim novel scaling laws, stacked logic architectures, or nanometer-class equivalence without independent benchmarks. Teams separate marketing nomenclature from manufacturing readiness by demanding yield, thermal, packaging, and third-party validation evidence—patterns highlighted when CNBC reported Huawei's LogicFolding and τ Scaling Law claims alongside analyst skepticism about true 1.4nm-class process breakthroughs without EUV access. The skill also maps export-control context (ASML EUV restrictions) and competitive implications for GPU vendors operating in constrained geographies.