Deploying LLMs on Yandex Cloud with GPU and vLLM

LLM Deployment: Where Infrastructure Breaks Under Load

On a test bench everything looks smooth: individual requests go to the model and return in fractions of a second, response latency is predictable, the queue is empty.

As soon as a real flow of requests arrives — dozens of concurrent dialogues, different prompt lengths, long chat histories — the picture changes: some requests sit in the queue, some drop by timeout.

The bottleneck is almost always the same — accelerator memory. Model weights occupy a fixed portion of it, and the remainder is divided between the context cache of active sessions and buffers for computation.

When concurrent sessions exceed what fits, the server evicts other contexts and recomputes them — response time grows in steps, not linearly.

Failure under peak load looks like “the model suddenly got dumber”: responses arrive, but slowly and not to everyone. Average metrics look calm — the failure is visible only in the upper latency percentiles and the share of requests closed by timeout.

Scaling the number of replicas does not always help: if the ceiling is set by the inference configuration, new replicas wait for the same resources and lengthen the queue.

That is why we start not with abstract tests, but with the load profile for model inference in your scenario: prompt length, number of concurrent sessions, share of long responses. Then we work out how this is covered.

What a Dedicated Model Inference Environment in Production Provides

A dedicated inference environment is an isolated layer where the model, inference server, and request queue belong to one service, and nothing runs alongside that would take accelerator resources.

Neighboring tasks stop affecting response time, and latency becomes a predictable value rather than a random one. Below are the signs by which such an environment is checked before being brought into production.

• Predictable latency. Response time stays within a narrow corridor and does not grow severalfold at peak: requests do not compete for the accelerator, and queue length is bounded from above.
• Failed response rate. Timeouts and connection drops are counted as a separate metric, and a long context does not lead to out-of-memory failure.
• Service downtime. Model updates go node by node, so the service does not stop entirely at any step.
• Fault tolerance. A node is removed from rotation — traffic is redistributed, and weights come up from local disk without reloading from storage.
• Recovery time. Measured separately: a replaced node returns to service predictably, without manual environment assembly.
• Failure class. A whole group goes away — degradation under load, different model versions on nodes, “floating” response length.
• Observability. Accelerator utilization and queue depth are visible; every response has a request ID and model version.

We fix the load profile in advance: share of long requests, context length, peak values per day. On dedicated accelerators Yandex Cloud, the environment is assembled as a separate node group, so test run results transfer to production without reassembly. Each item in the list is a metric by which the result is visible, not a promise.

LLM Deployment in a Closed Environment: Placement Options

The choice of placement hinges not on model size, but on the load profile. A conversational assistant expects a response in fractions of a second, nightly batch processing of a document array runs for hours, and fine-tuning for a domain is not about serving requests at all.

A single environment handles these scenarios poorly: you have to choose between responsiveness and accelerator utilization.

A local environment removes the perimeter question: requests, logs, and weights do not leave your infrastructure; accelerators are dedicated only to your workload; inference parameters and quantization are selected for the task.

The limitation is strict — the peak falls on your fleet of GPUs: the queue grows, latency degrades, and there is nowhere to add capacity at peak time. The cloud absorbs peaks but adds approvals for data transfer.

In practice, the modes do not compete but combine: interactive keeps the model with continuous batching of requests — we deploy vLLM, which mixes new requests into the current inference step and keeps the accelerator from idling — archives go to a nightly batch, and training runs in a separate environment.

The hybrid scheme provides predictable latency for dialogue and full GPU utilization for batch, while data determines which part of the environment remains within the closed perimeter.

Workflow: From Load Audit to Environment Handover

The work proceeds sequentially: first we measure the actual load, then design the environment, launch inference, and after checks hand the system over to your team. The sequence removes the main risk — an environment built for assumed rather than actual requests.

1. Load audit. We collect the request profile: context length, average and peak request rate, share of long generations, acceptable time to first token. The source is your service logs or a benchmarking run, not a rough estimate.
2. Environment design. We determine the number and type of accelerators, placement of the inference service on vLLM, balancing, queues, weight storage, and network boundaries. The environment remains closed; only application access points are exposed.
3. Inference tuning. We select the model for the task, batch size, parallelism, quantization, and context limit, and enable warm-up and shared prefix reuse. The goal is to increase throughput without growing latency.
4. Load testing. We run the environment against the profile from step one: stepwise growth, long sessions, failure of one node. We record latency, throughput, and behavior under overload.
5. Handover to operations. We provide the environment configuration as code, dashboards, alerting rules, model update regulations, and the procedure for rolling back to the previous version.
6. Team training. We go over diagnostics of typical failures and manual version switching with your engineers, so that later you can change the model and parameters yourselves.

Then — by agreement: we maintain the environment or leave it to your team with full documentation.

Documentation, Diagrams, and Regulations as Output

A project is considered delivered not when the model responds to requests, but when your team can bring it up, update it, and roll it back without us.

That is why we close LLM deployment with a package of artifacts: configurations, diagrams, and regulations remain in your repository together with the system, not in a chat.

• Component interaction diagram — which services communicate with each other, where trust boundaries run, which accelerator nodes serve model inference, and what happens if each of them fails.
• Deployment configurations — manifests, environment variables, and parallelism parameters of the inference server with comments: which values are critical for latency, which can be changed without rebuilding.
• Access roles and policies — service accounts, key scopes, separation of rights for reading logs, rolling out a new version, and access to the monitoring panel.
• Team instructions — starting and stopping services, reading logs, diagnosing typical failures: insufficient accelerator memory, timeouts on long requests, unavailability of external storage.
• Model update regulations — the procedure for rolling out a new weight build: environment preparation, shadow run on live traffic, quality comparison against a fixed set of questions, rollback conditions and time.
• Backup procedure — what exactly is copied, how often, where it is stored, and how long recovery takes.
• Monitoring map — metrics and thresholds that trigger an alert, tied to the action expected from the on-call engineer.

Case: Model Inference for a Support Service — What Changed

Implementation case: a support service with a flow of tens of thousands of requests per day was serving model responses through an external service.

The median response time stayed around 4 seconds, but p95 exceeded 12, and during peak hours requests queued up — the operator waited longer for a response than it took to formulate the question.

The share of erroneous responses stayed around 10%, and some of them were attributed to model quality, although the logs showed timeouts and connection drops.

Inference was moved to a dedicated cluster with accelerators in Yandex Cloud, with serving via vLLM. Inference was placed in a separate environment with request batching and a cache of frequent prompts, and updating model weights no longer required rebuilding the service.

Autoscaling was tied to queue length rather than average accelerator utilization: the queue is the first to show that the system is spinning out of control.

Metrics Before and After

• Median response time: from 4 to 1.2 seconds.
• p95: from 12 to 3.2 seconds.
• Share of erroneous responses: from 10% to 3.5%.
• Peak traffic up to 2.5 times higher than usual: passes without queueing or failures.

Behavior at peaks changed the most. Previously, a traffic spike meant manually increasing capacity, and some requests still failed; now nodes connect within tens of seconds, and the queue clears before the operator notices it.

We added a fallback route: when a node degrades, the request goes to a neighboring one, the response is served normally, and the incident is visible in monitoring.

How Does Cloud Model Inference Differ from a Local Environment?

The difference between cloud model inference and a local environment is not in hardware speed, but in who is responsible for data isolation, downtime, and operations.

Both approaches solve the same task: accept a request, run it through the weights, and return a response in acceptable time.

In the cloud, the model lives on a rented GPU pool, for example in Yandex Cloud, while scaling, drivers, and replacement of failed nodes are on the provider’s side.

Launch takes hours: we deploy a container with an inference server, connect weight storage, and restrict network access. Data leaves the perimeter — isolation rests on a private network, encryption, and storage access policy, but physically it is on someone else’s hardware.

A local environment is the reverse deal. Data does not leave your network at all, including requests, logs, and cache, which removes concerns for the security service and regulators.

In exchange, you maintain the accelerator fleet, update drivers and runtime, stay on call when a node fails, and provide throughput for peak load yourself.

In practice, a hybrid more often wins: sensitive data is processed locally, and the remaining scenarios go to the cloud. The choice is made based on one question — where the data physically resides and who is ready to be responsible for it.

Technical Doubts Before Work Starts

Engineering objections before starting rarely sound like “will it work at all” — more like “what will happen after launch”. How to migrate an already running model, where data goes, what happens if a node fails, and who is responsible for the system six months later. Below are the five questions that most often come up at technical acceptance.

Will data leave the environment?

The model and embedding storage live in a private network; only the API gateway is exposed outward. Logs are written with masking, keys are through a secret store with rotation. If policy requires it, the environment is assembled entirely without external traffic.

What happens if a GPU node fails?

There are at least two replicas; the failed one is removed from rotation; session state is stored outside the node — requests move without losing context. The input queue absorbs the burst while a replacement comes up.

How is a model update rolled out?

The version is run against the same reference set of checks as the current one and rolled out in a separate environment. Responses are compared on real requests; traffic is switched only after quality is confirmed.

What about support after handover?

We provide configurations, diagrams, access credentials, and regulations for typical failures, and train your team. After that — support with an agreed response time or consultations on request.

What to Discuss at the First Meeting About Your Environment

To come back with a timeline estimate and work plan, rather than general words, we need at least some initial information about your environment — what to prepare for the first meeting and what can be clarified during the process.

A ready-made technical specification is not required: answers to a few questions that you already keep in mind are enough.

• Which scenarios we cover: chat over an internal knowledge base, document parsing, a support assistant, generation of responses based on regulations.
• Load profile: requests per hour at peak, average and maximum request and response length, whether streaming output is needed.
• Environment requirements: public service or internal network, where sensitive data resides, and whether it can be moved out.
• Access: cloud account, role with rights to network and compute, who on the customer side confirms changes.
• Data and materials: document corpus for quality checks, examples of typical requests, current prototypes and model tests.
• Acceptance criteria: target answer accuracy, acceptable response time, failure rate, readiness to roll back to the previous version.

This is enough to select a GPU node configuration for peak load with headroom, determine a model update scheme without service downtime, and break down LLM deployment timelines by stage.

Based on the meeting, you will receive a work plan with checkpoints, a timeline estimate, and team composition; undefined inputs will be recorded as separate tasks of the first stage. Describe the task — we will come back with an estimate and plan.