$0.11 in API costs for a single query

I couldn’t believe it when I saw the API cost breakdown. I had uploaded less than 1GB of documents and attempted to extract summary information.

Boom $0.11 without even a single follow up question.

1KWh of power cost $0.07 where I live.

For the price of one query I can run a Local AI workstation for multiple hours with as many queries as I want.

Here is my breakdown of the current state of Local AI

Hardware Setup: M3 Ultra Mac Studio with 96GB of unified ram

  • Best Value for Local AI
  • Memory bandwidth is 800GB/s
  • Adding more RAM doesn’t justify the extra cost
  • Able to run qwen3 models at fp16
  • Able to run gpt-oss:120b but limited to 8k context

Best Model: qwen3:30b-a3b-instruct-2507-fp16

  • Runs on 64 GB of RAM
  • Good at web scraping
  • JSON output
  • Retrieval Augmented Generation
  • Written content generation ( email, etc. )

Best Chat Model: gpt-oss:20b

  • Feels just like chatGPT
  • Runs on 16GB of RAM
  • Good at coding
  • Great at Written content generation
  • Better results with follow up prompts
  • Least likely to get stuck in a repeat loop

Hope you all are enjoying the massive progress in AI!

Why “Local‑First” AI Matters

Data‑privacy and regulatory compliance

  • Keeping the model and the data on the same device means the raw code, proprietary documents, or patient health information never leave the organization’s firewalls. This eliminates the risk of accidental exposure through cloud‑provider logs, backups, or third‑party integrations.
  • GDPR requires that personal data be processed with “privacy by design” and that controllers be able to demonstrate where data is stored and who can access it. Running AI locally satisfies the storage‑location requirement and gives the controller full auditability of every inference step.
  • Under HIPAA, the Security Rule (45 CFR 164.304) obliges covered entities to implement technical safeguards that protect ePHI. The Notice of Privacy Practices can now explicitly state that AI is used on‑device, and the rule’s authentication provision (45 CFR 164.312(d)) can be satisfied by local credential checks rather than transmitting data to an external service [2].
  • Sending proprietary code to a cloud endpoint also creates intellectual‑property risk: the provider could retain snippets for model improvement or for debugging, potentially exposing trade secrets. A local‑first approach removes that vector entirely.

Latency benefits for real‑time coding assistance

  • First‑token latency (time to the initial response) is dominated by network round‑trip time in cloud setups. By eliminating the network hop, on‑device inference can deliver the first token in milliseconds rather than seconds, which is critical for interactive code‑completion or linting tools.
  • Hardware characteristics such as GPU/TPU memory bandwidth (e.g., 800 GB/s on high‑end machines) and low‑latency system buses further shrink the per‑token processing time. Larger models do increase compute demand, but the trade‑off is still far more favorable than the variable latency of a remote API [1].
  • Consistent, predictable latency enables developers to build “real‑time” features—instant refactoring suggestions, live documentation look‑ups, or on‑the‑fly test generation—without the jitter introduced by cloud load balancing or internet congestion.

Bottom line: Local‑first AI gives developers control over sensitive data to meet GDPR and HIPAA obligations while delivering the sub‑second response times needed for seamless, real‑time coding assistance.