TBW / AI SYSTEMS / MODELS
Model Selection and Local Inference
Evaluate commercial and open-weight models against your workloads, constraints and infrastructure before choosing a serving approach.
Select for the Workload, Not the Leaderboard
A useful selection decision joins task evidence, data-handling constraints and an operable serving design. Avoid a universal provider ranking: the best route can differ across tasks and data classes.
Decision Criteria
Compare task fit, answer quality, latency, context handling, tool use and structured-output reliability. Review data-handling terms, license, deployment choices, provider stability and portability separately; a strong answer score does not override an unacceptable data path. Record constraints and weighted acceptance criteria before testing.
A Representative Benchmark Method
Assemble real tasks and a representative dataset with expected outcomes, difficult edge cases and security tests. Run side-by-side configurations under the same inputs; use human review for correctness and failure severity as well as automated checks where appropriate. Record model versions, prompts and settings so results are reproducible. Repeat after important model, prompt, tool or provider changes.
Commercial and Open-Weight Tradeoffs
Commercial services
May reduce serving operations and offer managed capabilities, subject to provider terms, availability, portability and data routing requirements.
Open-weight deployment
Can offer more control over serving location and configuration, but requires license review, capacity planning, updates and operations.
Neither category guarantees privacy, quality or lower cost. Assess the specific model and deployment against the same workload.
Model Size and Quantization
Model weights, precision and context allocation affect memory footprint; concurrency and throughput depend on the whole serving configuration. Quantization can reduce resource demand with potential quality trade-offs, so retest task-specific outputs. Gather input length, output length, concurrency and latency requirements before sizing infrastructure. See AI Systems for deployment context; hardware quantities require measurement for the chosen stack.
Serving Options Are Implementation Choices
Possible components include vLLM, TGI, Ollama, OpenAI-compatible endpoints and dedicated embedding or reranking servers. These are examples, not endorsements or partnerships. Evaluate protocol compatibility, isolation, observability, batching, scaling, authentication and update process; separate the embedding and reranking workloads if their operating needs differ.
Policy-Aware Model Routing
Route simple tasks to a suitably evaluated small model and difficult cases to a stronger candidate where evidence supports it. Define fallback behavior for outages or low-confidence results, including when to stop rather than silently change provider. Data class can require a private or local route; enforce the policy before sending content and record route decisions without unnecessary sensitive payloads.
Drift, Deprecation and Cutover
Track model updates and provider deprecation notices. Re-run the evaluation set to detect regressions, compare old and candidate versions side by side, then cut over gradually with a documented rollback. Pin versions where supported and identify an owner for upgrades, alerts and compatibility testing.
Model Supply Chain
Verify model provenance and license, record checksums or digests for weights and container images, and use an internal registry where appropriate. Pin approved versions and scan dependencies before promotion. A downloadable artifact is not automatically trusted or licensed for the intended use.
Real-Time Voice Changes the Latency Budget
For voice interaction, end-to-end turn latency includes recognition, model response and speech synthesis. Evaluate streaming, interruption handling and fallback as a combined system rather than measuring text inference alone. See Voice AI for the protected voice capability overview.
Implementation and Deliverables
Define workloads and constraints, build a reproducible test harness, compare candidates, prototype serving and routing, then stage cutover with operational ownership. Deliver a model evaluation report, test harness, serving architecture, routing recommendation, sizing inputs and upgrade plan.
Related: Local AI · Private AI · AI Systems · Data Integration and RAG
Frequently Asked Questions
Is an open-weight model always safer for sensitive data?
No. Serving location is only one control; access, logging, license, updates and operational security also matter.
How should models be compared fairly?
Test the same representative tasks and edge cases under documented settings, and include human review of consequential errors.
Does quantization always preserve answer quality?
Not necessarily. It can change outputs, so evaluate the quantized candidate on your actual workload and concurrency needs.
Can a fallback send restricted data to another provider?
Only if policy permits that route. Otherwise fail safely or use an approved private alternative.
What happens when a provider retires a model?
Evaluate a replacement side by side, verify integrations and security constraints, stage cutover and retain a rollback plan where possible.
Compare Models on Your Tasks
Share representative tasks, data classifications and latency constraints. We can establish evaluation criteria and a serving shortlist before committing to infrastructure.