LLM INFERENCE & DEPLOYMENT
Build a measured LLM serving plan
Move from a model checkpoint to an observable serving workflow. Define quality, latency, capacity and cost targets, then test the configuration against your application.
Discuss your serving workloadDefine the workload before choosing GPUs
Bring the model and tokenizer revision, license, adapters, prompt lengths, output limits and expected concurrency. Interactive chat, asynchronous document processing and embedding requests have different serving requirements. Identify regions, data boundaries and peak traffic rather than sizing from a daily request count alone.
The initial deliverable is a workload specification and a benchmark plan: representative requests, a baseline, quality checks, latency targets and a spending limit. Model format and hardware compatibility need verification before deployment scope is agreed.
Compare serving engines with representative requests
Evaluate candidate runtimes against the model architecture, quantization format, tool calling, structured output and streaming requirements. A compatible HTTP interface does not establish identical behavior across engines. Pin versions and verify tokenization, chat templates, stop conditions and adapter loading.
Test the actual application path, including gateway authentication, retrieval and response parsing. Keep a reproducible reference configuration so engine changes can be compared with the same inputs and acceptance criteria.
Separate first-token latency from completion time
Time to first token describes when streaming begins; total completion time includes generating the whole answer. Measure both, along with inter-token latency, queue waiting, failures and throughput at the intended concurrency. Report the input/output length distribution and hardware alongside percentiles.
A short cached request cannot establish the latency of a long uncached prompt. Include cold starts, warm instances and sustained load. Define timeout and cancellation behavior so abandoned requests do not continue consuming capacity unnecessarily.
Optimize without hiding quality or queueing costs
Batching can improve utilization while increasing waiting time. Quantization can change outputs and requires task evaluation. Speculative decoding depends on the draft and target models and their acceptance behavior. Compare each change against the baseline instead of adding theoretical speed multipliers together.
Distinguish prefix/KV reuse from caching a final application answer. For answer caches, include tenant permissions, relevant data version, freshness and invalidation in the design. Requests from different accounts must not share protected responses. Test cache misses as well as hits.
Plan capacity, admission control and recovery
Measure the capacity of a warm instance before setting scaling rules. GPU provisioning, model download and initialization affect readiness. Agree on queue limits, rate limits, backpressure and overload responses rather than assuming scaling is instant. Test draining and failed instances with representative traffic.
Choose cloud, on-premise or hybrid placement from the data and operational requirements. Document where prompts, logs, backups and telemetry travel. Availability and response-time commitments need a defined service boundary, measurement window, dependencies and support plan in the proposal.
Compare total cost per useful response
Include idle capacity, GPU runtime, storage, network transfers, monitoring, engineering and incident support. Track accepted outputs, retries and review workload alongside tokens. A cheaper request that repeatedly fails the task can cost more to operate.
As an illustrative planning calculation, two assumed instances at $2 per hour running for 100 hours cost $400 for compute alone. These are hypothetical inputs, not a provider quote or TensorBlue benchmark. Replace them with measured utilization and an actual infrastructure estimate before deciding whether self-hosting beats an API.
Release with observable acceptance criteria
Package the model revision, serving configuration, evaluation report and infrastructure definition together. Use a limited rollout and keep a tested rollback version. Monitor request errors, queue depth, resource pressure, latency distributions and task-quality samples; separate service failures from unacceptable model answers.
The handover should identify deployment ownership, alert routing, log retention, recovery steps and the criteria for changing capacity or models. Bring a representative request set and existing measurements to the scoping discussion so the proposed work can be reviewed against concrete evidence.
Implementation references and related work
The vLLM online serving documentation describes its serving interfaces. Tool documentation informs compatibility checks; it is not evidence of TensorBlue performance. Explore model quantization, fine-tuning and our MLOps release guide.
Flexible Pricing
Published package options; model, capacity, billing scope and support terms are confirmed in your proposal.
Pay-Per-Request
- ✓No minimums
- ✓Auto-scaling
- ✓Reliability targets agreed in scope
Fixed Monthly
- ✓Dedicated GPUs
- ✓Priority support
- ✓Custom SLAs
Frequently Asked Questions
What inference frameworks do you use?
Candidate engines depend on model, hardware and application requirements. Verify architecture, quantization, adapters, streaming and API compatibility, then compare representative workloads before selecting a runtime.
How do you reduce LLM serving cost?
Evaluate batching, caching, quantization and routing against a measured baseline. Include quality, queueing, idle capacity, retries and operational costs. Savings depend on the workload and configuration; no fixed percentage follows from a technique alone.