API design is the design of explicit contracts through which software components exchange requests, events, streams, data, errors, and capabilities. Good APIs stabilize model inference, hardware services, data platforms, microservices, and external integrations while permitting secure independent evolution.

Architecture and principles. A contract defines resources or methods, schemas, semantics, identity, authorization, idempotency, ordering, pagination, filtering, timeouts, errors, rate limits, and compatibility. Names and defaults should be consistent; validation errors should be actionable; retries require idempotency keys or safe methods. OpenAPI, Protocol Buffers, JSON Schema, and examples make contracts machine-readable. Documentation must explain behavior, not merely fields.

Execution and system behavior. REST maps resource operations onto HTTP and interoperable JSON. GraphQL lets clients select connected fields through one typed schema, reducing overfetch but complicating cost and caching. gRPC uses Protocol Buffers and HTTP/2 for efficient typed unary and streaming RPC. Async event APIs decouple producers. SSE streams server-to-client tokens simply; WebSockets support bidirectional sessions. Batch APIs trade responsiveness for throughput and durable job semantics.

Applications and semiconductor impact. AI inference APIs must specify model or policy version, input limits, tokenization assumptions, sampling parameters, structured output, streaming events, cancellation, usage, safety behavior, retention, and deterministic options. Large uploads need object references or multipart flows. Accelerators benefit from batching, but gateways must balance queueing against tail latency. Asynchronous jobs need status, expiry, callbacks, and exactly-once-looking idempotent client behavior.

Trade-offs and current engineering. Version additively when possible and deprecate with telemetry, migration guides, dates, and dual-run support. Authentication identifies callers; authorization scopes operations and data; quotas prevent abuse; TLS protects transport; audit trails support investigations. Test schemas, contracts, compatibility, load, faults, retries, partial streams, and malformed input. Observe request IDs, latency, status, saturation, and downstream dependencies without leaking secrets.

Verification and lifecycle. A production implementation begins with explicit terminal conditions, operating ranges, loading, accuracy, noise, latency, efficiency, area, cost, lifetime, and fault behavior. Schematic or architectural models establish feasibility; extracted, package, board, thermal, and control-loop models then reveal interactions hidden by ideal sources and loads. Verification spans process, voltage, temperature, mismatch, aging, startup, shutdown, overload, brownout, and recovery. Teams should define measurement bandwidth, observation point, stimulus, pass limit, guard band, and statistical confidence before simulation. Layout review covers current return, thermal gradients, matching, parasitic coupling, electromigration, voltage stress, latch-up, ESD paths, and test access. Correlation retains netlists, models, scripts, tool versions, raw results, lab conditions, calibration status, and explanations for outliers. This evidence turns a nominal design into a reproducible component that can be signed off across device, circuit, package, firmware, and system teams. Corner selection should follow sensitivity rather than blindly combining labels. Deterministic sweeps expose monotonic trends, targeted Monte Carlo analysis estimates distribution tails, and importance sampling can explore rare failures. Reviewers should distinguish model uncertainty from manufacturing variation and avoid claiming yield from too few samples. The interface contract must state what happens outside normal operation. Open and short terminals, reverse polarity, hot plug, disabled bias, floating control pins, clock loss, thermal shutdown, current limiting, and repeated fault cycling often determine field reliability even though they are absent from the nominal transfer function. Dynamic behavior deserves the same attention as steady state. Settling, overshoot, ringing, slew, recovery from saturation, mode transitions, and interaction with external poles can violate a system limit long before a DC endpoint does. Time-domain tests should include realistic edge rates and source impedance. Noise should be referred to the signal or supply point that matters to the application and integrated only over a stated bandwidth. Thermal, flicker, quantization, switching, reference, substrate, and electromagnetic contributions may combine differently across modes, so a single spot-noise number rarely completes the specification. Power and thermal claims should include quiescent, active, transient, and fault states. Average efficiency can hide localized current density or hot spots; electrothermal simulation and temperature-aware device models connect electrical stress to lifetime, drift, and protection thresholds. Physical design must preserve the assumptions behind the schematic. Symmetry, common-centroid placement, dummies, shielding, guard rings, Kelvin sensing, wide current paths, via arrays, controlled coupling, and quiet reference routing are selected according to the dominant error rather than applied as decoration. Production test strategy is part of design. Trim range, observability, loopback modes, built-in self-test, boundary conditions, test time, and instrument uncertainty determine which specifications can be guaranteed economically. Characterization across wafers and lots should feed model and guard-band updates. System telemetry can extend laboratory correlation into deployed products. Error counters, calibration codes, temperatures, supply monitors, fault flags, margin measurements, and performance events help distinguish random failures from systematic drift without exposing sensitive implementation details. A useful comparison normalizes alternatives at equal output requirement and environment. Peak headline values can be misleading when bandwidth, drive, voltage, area, cooling, external components, calibration, or reliability differs; the decision record should name the workload and weighting used. Cross-functional review should trace each requirement from physical mechanism through circuit behavior to application impact. That trace prevents duplicated margin, exposes assumptions that span ownership boundaries, and makes later process or package substitutions safer. Corner selection should follow sensitivity rather than blindly combining labels. Deterministic sweeps expose monotonic trends, targeted Monte Carlo analysis estimates distribution tails, and importance sampling can explore rare failures. Reviewers should distinguish model uncertainty from manufacturing variation and avoid claiming yield from too few samples. The interface contract must state what happens outside normal operation. Open and short terminals, reverse polarity, hot plug, disabled bias, floating control pins, clock loss, thermal shutdown, current limiting, and repeated fault cycling often determine field reliability even though they are absent from the nominal transfer function.

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  <text x="380" y="28" fill="#e6edf3" font-size="21" font-weight="700" text-anchor="middle">Inference API Design — REST, gRPC, and Streaming</text>
  <text x="380" y="48" fill="#8b98a5" font-size="12" text-anchor="middle">expose model capabilities through well-designed endpoints: auth, validation, streaming, rate limiting, versioning</text>

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  <text x="532" y="116" fill="#6b7684" font-size="6.5" text-anchor="middle">GPU execution</text>

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  <text x="380" y="153" fill="#6b7684" font-size="8" text-anchor="middle">POST /v1/chat/completions → {model, messages[], temperature, max_tokens, stream: true}</text>

  <!-- === MIDDLE LEFT: API patterns === -->
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  <text x="45" y="215" fill="#60a5fa" font-size="8.5" font-weight="600">OpenAI-compatible</text>
  <text x="180" y="215" fill="#8b98a5" font-size="8.5">/v1/chat/completions (de facto standard)</text>
  <text x="45" y="233" fill="#34d399" font-size="8.5" font-weight="600">Streaming (SSE)</text>
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  <text x="115" y="251" fill="#8b98a5" font-size="8.5">async upload → poll → download (50% off)</text>
  <text x="45" y="269" fill="#c4b5fd" font-size="8.5" font-weight="600">Function calling</text>
  <text x="155" y="269" fill="#8b98a5" font-size="8.5">structured tool_calls in response</text>
  <text x="45" y="287" fill="#f87171" font-size="8.5" font-weight="600">gRPC / Triton</text>
  <text x="140" y="287" fill="#8b98a5" font-size="8.5">binary protocol, lower latency, typed schema</text>
  <text x="45" y="301" fill="#8b98a5" font-size="8">GraphQL: rare for LLMs (streaming mismatch)</text>

  <!-- === MIDDLE RIGHT: Key design decisions === -->
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  <text x="562" y="193" fill="#e6edf3" font-size="10" text-anchor="middle" font-weight="600">Design Decisions</text>

  <text x="410" y="215" fill="#8b98a5" font-size="8.5">• Versioning: /v1/, /v2/ (never break clients)</text>
  <text x="410" y="233" fill="#8b98a5" font-size="8.5">• Rate limits: per-key token bucket + concurrency</text>
  <text x="410" y="251" fill="#8b98a5" font-size="8.5">• Idempotency: client-supplied request ID for retries</text>
  <text x="410" y="269" fill="#8b98a5" font-size="8.5">• Pagination: cursor-based for list endpoints</text>
  <text x="410" y="287" fill="#8b98a5" font-size="8.5">• Error format: type, message, code, param (OpenAI-style)</text>
  <text x="410" y="301" fill="#fbbf24" font-size="8.5">• Usage headers: tokens consumed in response headers</text>

  <!-- === BOTTOM: Streaming detail === -->
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  <text x="380" y="333" fill="#e6edf3" font-size="10" text-anchor="middle" font-weight="600">Streaming Response Format (Server-Sent Events)</text>

  <text x="45" y="355" fill="#60a5fa" font-size="8.5">data: {"choices":[{"delta":{"content":"Hello"}}]}</text>
  <text x="45" y="371" fill="#60a5fa" font-size="8.5">data: {"choices":[{"delta":{"content":" world"}}]}</text>
  <text x="45" y="387" fill="#f87171" font-size="8.5">data: [DONE]</text>

  <text x="450" y="355" fill="#8b98a5" font-size="8.5">• First byte = TTFT (time to first token)</text>
  <text x="450" y="371" fill="#8b98a5" font-size="8.5">• Each chunk = one decoded token</text>
  <text x="450" y="387" fill="#8b98a5" font-size="8.5">• usage object in final chunk (token counts)</text>
  <text x="450" y="399" fill="#6b7684" font-size="8">Client: EventSource / fetch + ReadableStream</text>

  <!-- Key insight -->
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  <text x="380" y="426" fill="#fbbf24" font-size="9" text-anchor="middle">The OpenAI API shape won: /v1/chat/completions is the HTTP interface every serving framework now implements.</text>

  <text x="380" y="460" fill="#6b7684" font-size="11" text-anchor="middle">Good API design hides GPU complexity: the client sends messages and gets tokens back — streaming makes it feel instant.</text>
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