Private GPU inference engineering
Production inference on GPUs you own or capacity we help source.
Gyde benchmarks the workload, selects the model, runtime, and hardware configuration, builds the serving platform, and provides the telemetry and runbooks your team needs to operate it.
Workload-shapedQuantization-awareOperations included
MODELopen-model-70b
PRECISIONFP8 candidate
SHAPE4 × GPU replica
LOAD
service objective
✓
quality gate preserved✓load profile completed customer environmentmeasured results onlybenchmark recorded
Where it can run
Four infrastructure paths for private inference.
Deployment starts with the business boundary: existing capital, approved cloud accounts, capacity availability, data location, support ownership, and the representative demand profile.
GPUs you already own
Assess the existing estate, topology, memory, network, storage, utilization, and operational constraints before deciding what it can serve well.
Your cloud account
Provision private capacity inside an approved AWS, Azure, or GCP environment with the network, identity, region, and data controls you require.
Capacity we help source
Compare suitable cloud and specialist GPU capacity, then provision and operate the agreed environment under a clear commercial and support model.
Hybrid inference
Keep predictable or sensitive workloads on dedicated GPUs while using approved managed endpoints for burst, fallback, or specialist capabilities.
Workload profile
Select the deployment configuration from the workload profile.
We profile representative requests before committing the runtime, precision, accelerator, replica count, or scaling policy.
Model×Precision×Runtime×Hardware×Traffic→ measured production shape
Performance engineering
Evaluate quality, latency, throughput, utilization, and cost.
Runtime selection
Compare suitable serving engines and vendor runtimes against the same model, traffic profile, hardware, and acceptance criteria.
Quantization
Compare each precision option across model quality, memory use, throughput, latency, and cost on the selected hardware.
Batching and scheduling
Tune request admission, continuous batching, queue limits, and priority against both throughput and interactive latency thresholds.
Caching
Design prompt or prefix caching around repetition, tenant isolation, invalidation, rollout behavior, and measurable hit rates.
Parallelism and topology
Test tensor or pipeline placement, multi-GPU communication, memory pressure, and replica shape against the accelerator topology.
Speculative execution
Use draft-model, n-gram, or predicted-output techniques only when measured acceptance rates improve the target workload.
QQuality stays in the benchmark. Runtime, precision, cache, and execution changes use the same task-level evaluation and performance measures.
Serving platform
A complete serving system around the model.
The endpoint is only one layer. Production inference also needs traffic control, release discipline, observability, security boundaries, and accountable operations.
Stable inference contractStreaming, errors, metadata, compatibility
01↓Gateway and admissionAuth, quotas, priorities, routing, backpressure
02↓Runtime and modelPrecision, batching, cache, parallelism
03↓GPU clusterDrivers, topology, storage, network, replicas
04↓SLO and lifecycleMetrics, releases, incidents, capacity, cost
05The engagement
Profile the workload, benchmark configurations, and deploy the selected platform.
Each phase produces measured results or an implementation asset that the operating team can own.
1
Profile
Characterize the workload
Capture representative prompts, output lengths, concurrency, bursts, quality gates, failure tolerance, residency, and growth.
2
Benchmark
Compare configurations
Test model, precision, runtime, hardware, cache, and scaling choices with representative payloads and controlled quality checks.
3
Deploy
Build the production path
Provision the cluster, API, identity, telemetry, release pipeline, isolation, capacity policy, and application integration.
4
Operate
Test and hand over
Exercise load and failure, tune alerts, document ownership, train operators, and record the thresholds for expansion.
Results and handoff
Application performance and platform operations.
We instrument the serving path from caller to queue, prefill, generation, GPU, quality result, and cost allocation.
Time until the first generated token reaches the caller
Output tokens per second under representative concurrency
Requests and tokens completed within the service objective
Errors, timeouts, saturation, recovery, and degraded behavior
GPU, memory, cache, queue, and replica utilization
Cost per token, request, tenant, feature, or successful task
Engagement deliverables
- ✓Workload and traffic profile
- ✓Model, runtime, and hardware benchmark matrix
- ✓Quantization and quality comparison
- ✓GPU capacity and total-cost model
- ✓Production inference service and deployment pipeline
- ✓Performance, GPU, and cost dashboards
- ✓Load, failure, and release test report
- ✓Security architecture, operating runbook, and team handover
Decision controls
Infrastructure decisions use recorded benchmark results.
The benchmark captures results from representative traffic and documents configuration trade-offs for the deployment decision.
Quality is held constant
Every performance configuration is evaluated against the task and regression criteria that make its output useful.
Provider-confirmed capacity
Cloud GPU availability, pricing, regions, and reservations are validated before an architecture or delivery commitment is made.
Isolation follows the workload
Network paths, identity, artifacts, cache keys, logs, and tenant boundaries are designed around the actual data and risk model.
Operations have an owner
Scaling, upgrades, model replacement, incident response, and cost management receive named ownership before production handover.
Questions
Before the first benchmark run.
We already own GPUs. Can Gyde assess them?
Yes. We begin with the accelerator type and memory, server and network topology, storage path, drivers, orchestration, current utilization, and workload requirements. The assessment identifies viable workloads, operating constraints, and alternative configurations where relevant.
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 service ownership and procedures. Availability, commercial terms, billing ownership, regions, and support responsibilities are confirmed for each engagement.
Do you support quantization?
Yes. We evaluate precision choices as part of the benchmark, measuring the effect on model quality, memory footprint, time to first token, generation rate, throughput, and economics on the selected hardware.
Which inference runtime do you use?
There is no universal winner. We select and benchmark appropriate open or vendor runtimes based on the model architecture, hardware, traffic profile, feature needs, deployment environment, and operating constraints.
Can this run entirely inside our environment?
Yes, when the required hardware, network, storage, and operational prerequisites are available. We can design for a customer data centre or private cloud account and document any external dependencies explicitly.
How do managed model APIs fit into the architecture?
A hybrid design can retain approved managed endpoints for burst traffic, fallback, migration, or models that have weak private-hosting economics. Routing and data policy determine where each request may go.
How do you report performance improvements?
We agree the workload and acceptance criteria, benchmark comparable configurations, and report measured results and trade-offs before recommending the production configuration.
Who operates the platform after launch?
The engagement defines that early. Gyde can enable the customer platform team, provide a transition period, or agree an ongoing operating scope. Monitoring, incident, upgrade, and capacity responsibilities are documented before go-live.
Private GPU inference
Bring us the model, the workload, and the GPU constraint.
We will design a benchmark with representative traffic to identify the configuration that meets the required quality, performance, reliability, and economics.
Plan an inference benchmark↗