Unknown capacity
Accelerator count alone leaves model, precision, context, concurrency, and service limits unresolved.
Consulting / Inference
AI inference engineered for your workload.
We profile the workload, benchmark model and hardware configurations, deploy the serving platform, and measure its performance under representative traffic.
Workload-shaped inference
Representative scope
Model × Runtime × Hardware × Traffic
The production shape is selected against explicit quality, performance, reliability, security, and economic requirements.
Time to first token
Generation rate
Peak concurrency
GPU utilization
Task economics
01Owned, cloud, or sourced GPUs
02Quantization and runtime optimization
03Build plus operating handover
Why this layer matters
Production inference combines capacity, runtime engineering, and operations.
Model memory, prompt lengths, concurrency, batching, cache behavior, topology, traffic bursts, and release practices determine whether the available capacity becomes a dependable product or an expensive bottleneck.
Unproven optimization
Quantization, batching, caching, and parallelism change quality and performance differently across models, hardware, and traffic shapes.
Missing operational controls
A reliable service requires admission control, telemetry, release gates, failure tests, assigned ownership, and runbooks.
What we deliver
One engagement, three connected workstreams.
01
Workload & capacity engineering
Characterize model, precision, context, output, concurrency, burst, availability, region, and isolation requirements before choosing the production shape.
- Traffic and workload profile
- GPU estate or provider assessment
- Capacity and TCO model
02
Runtime & platform engineering
Benchmark and implement the runtime, quantization, batching, caching, parallelism, API, orchestration, and release path on the selected infrastructure.
- Configuration benchmark matrix
- Production inference endpoint
- Deployment and rollback automation
03
Reliability & economics
Connect request, queue, token, cache, GPU, quality, and cost signals to service objectives and accountable operating decisions.
- Load and failure testing
- Dashboards, alerts, and cost allocation
- Incident, upgrade, and capacity runbooks
The engagement
Each phase answers a production question.
The initial scope is narrow. Each phase produces working software and a reviewable deliverable for the next decision.
1
Profile
Characterize the workload
Capture representative prompts, outputs, traffic, quality gates, security boundaries, and growth assumptions.
2
Benchmark
Compare configurations
Measure model, precision, runtime, accelerator, cache, and replica choices against the same representative workload.
3
Deploy
Build the serving platform
Provision the API, runtime, cluster, identity, telemetry, scaling policy, release path, and application integration.
4
Operate
Prove and hand over
Exercise load and failure, tune the production shape, document ownership, and enable the team that will run it.
What you leave with
Deployed software, test results, and an operations runbook.
The engagement includes implementation documentation and a defined handover.
A measured deployment configuration
Benchmark results identify the model, precision, runtime, hardware, and scaling configuration that meets the agreed workload.
An operable inference service
A secured endpoint with deployment automation, telemetry, load controls, release gates, and failure behavior.
Capacity and economic control
Utilization, cost allocation, growth thresholds, sourcing decisions, and runbooks tied to named operating owners.
Typical building blocks
Open modelsInference runtimesQuantizationKubernetesGPU telemetryLoad testingOpenAI-compatible APIsHybrid endpoints
Packaged engineering engagement
Private GPU Inference
A dedicated path for organizations that already own GPUs, want infrastructure inside their cloud account, or need help sourcing and operating approved capacity.
Questions
Before we begin.
We already own GPUs. Can Gyde assess them?
Yes. We assess accelerator memory, server and network topology, storage, drivers, orchestration, utilization, and workload requirements before recommending what the estate should serve.
Can Gyde help us source cloud GPU capacity?
Yes. We can compare suitable capacity across approved cloud and specialist providers, help provision it, and define the commercial and operating responsibilities. Availability, pricing, regions, and reservations are confirmed for each engagement.
Do you support quantization?
Yes. We benchmark precision choices against model quality, memory footprint, time to first token, generation rate, throughput, and economics on the selected hardware.
Do you only work with self-hosted models?
We focus on dedicated and private infrastructure. Approved managed endpoints can support burst traffic, fallback, migration, or specialist capabilities in a hybrid design.
Which measures guide inference optimization?
We evaluate quality, latency, throughput, reliability, security, operability, utilization, and cost together. Production recommendations must satisfy the agreed thresholds across these measures.
Can you improve an existing production stack?
Yes. We can begin with an estate, architecture, workload, and telemetry assessment, reproduce the bottleneck, and benchmark the most relevant changes.
Do you guarantee a performance improvement?
No generic percentage is credible. We agree the workload and acceptance criteria, compare configurations under controlled conditions, and report the measured trade-offs before recommending a production change.
Bring a defined business constraint
Bring us the model, the workload, and the GPU constraint.
We will define a focused benchmark using representative traffic to select the deployment configuration, service thresholds, owners, and runbooks.
Plan an inference benchmark