Internally deployed AI models
Compare open-weight deployments, internally fine-tuned models, and self-developed models across sizes, inference options, input conditions, and output ranges.
Consulting
Internal AI Model & Service
Performance Testing
We test LLMs and AI models built and operated on the organization's own infrastructure, then isolate bottlenecks across internal inference servers, APIs, serving layers, gateways, queues, proxies, and infrastructure.Externally hosted models and commercial LLM APIs are out of scope.
Scope
Measure internally deployed models and the internal service path
We measure how open-weight deployments, models fine-tuned on internal data, and self-developed machine-learning or deep-learning models generate responses on internal inference infrastructure and deliver them to users.
Internal service path
Separate model time from delays in internal inference servers, API servers, inference gateways, serving layers, proxies, queues, connection pools, serialization, and the client.
Streaming experience
Verify that the first response starts promptly and new token chunks arrive without gaps, duplication, or avoidable interruption.
Metrics
Use metrics that explain user experience and system limits
We correlate start time, completion time, generation speed, concurrency, errors, throughput, and resource trends instead of relying on one average.
TTFT
Time to first token
Time from the request until the user receives the first response chunk.
E2E
Total latency and completion
Time from request start until the final token arrives and the response completes.
Token/s
Token generation speed
How steadily the internal model and serving layer generate and deliver tokens after the response begins.
I/O
Input and output token ratio
How input conditions and output volume affect latency, cost, and throughput.
Load
Concurrency and throughput
How response time and throughput change as concurrent users increase and where saturation begins.
Risk
Errors and resource correlation
Correlate failures and timeouts with CPU, memory, network, and connection usage trends.
Streaming
Confirm that each event sends only the newly generated token chunk
A healthy stream appends new chunks.If every event resends the entire sentence so far instead of only the new chunk, network traffic and browser rendering work can grow as the answer gets longer.
Check that each stream event contains only the newly generated token chunk
Detect long gaps, interruption, ordering errors, and duplicate events
Compare whether transfer volume and rendering time grow abnormally with response length
Separate server delivery behavior from browser rendering overhead
Scenarios
Test from a baseline through saturation and retesting
Increase concurrent load in stages and analyze response time, throughput, errors, and resources together to identify stable ranges and saturation points.
Internal model and option baseline
Compare internal model size, inference and generation options, input length, and output length to establish a reproducible baseline.
Stepped concurrency
Raise concurrent users gradually and observe response, throughput, error, and resource trends at each stage.
Saturation and endurance
Find the range the service can handle steadily and check whether latency or errors accumulate under sustained load.
Bottleneck isolation and retest
Separate the internal model, inference server, internal API, serving layer, queue, gateway, proxy, connection pool, serialization, and timeout candidates, then compare before and after under the same conditions.
Deliverables
Connect measurements to improvement and operational decisions
We do not promise a fixed performance level.Recommendations are based on the actual diagnosis, observed evidence, and the operating objective.
Measurement plan and scenarios
Document user flows, internal model and inference conditions, load stages, observed metrics, and decision methods in an executable test plan.
Performance analysis report
Explain response, throughput, error, resource trends, stable ranges, and saturation points in the context of the service architecture.
Bottleneck candidates and priorities
Separate model and service-layer candidates and recommend an improvement order based on the diagnostic evidence.
Before-and-after retest
Repeat the same conditions after changes to verify impact, remaining risk, and updated operating criteria.
Outcomes
Turn causes of delay and capacity limits into decision evidence
Use the evidence for internal model and inference-option selection, serving architecture improvements, capacity planning, and monitoring criteria.
Separate causes of perceived latency
Distinguish internal model generation time from internal service delivery time to identify which layer to improve first.
Support capacity decisions
Use stable ranges and saturation points to guide concurrency targets, scaling policies, and monitoring thresholds.
Verify improvements reproducibly
Compare before and after under the same conditions and continue with the next evidence-based priority.
FAQ
Common questions about internal AI performance testing
The measurement scope is set after reviewing the internally deployed model, inference and service architecture, infrastructure, and operating objective.
Which AI models are in scope for performance testing?
The scope covers open-weight LLMs deployed on internal infrastructure, models fine-tuned on internal data, self-developed machine-learning or deep-learning models, and internal inference servers. Externally hosted models and commercial LLM APIs are excluded.
Can the service be slow even when the internal model is healthy?
Yes. Internal inference servers, API servers, inference gateways, serving layers, proxies, queues, connection pools, serialization, and timeout settings can add end-to-end delay, so model time and service-layer time must be measured separately.
What is cumulative retransmission in streaming?
It is when each event repeats the entire sentence so far instead of sending only the new token chunk. As an answer grows, this can increase network transfer and browser processing work.
Can you guarantee a specific number of concurrent users?
We do not guarantee a fixed number in advance. We measure stable ranges and saturation points for the actual model, architecture, infrastructure, and usage scenario, then recommend operating criteria and improvements based on the diagnosis.
Measure the internally deployed model, inference, and service layers separately, then define improvement priorities and a retest plan from the diagnostic evidence.