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How do I route requests across open-weight LLMs? Put a policy and dispatch layer in front of your model-serving backends. The router inspects each request, removes models that cannot satisfy its capability, protocol, privacy, or latency requirements, then selects an eligible replica using signals such as queue depth, predicted latency, cache locality, priority, or a simple algorithm such as round robin. The router normally forwards the request; an inference server loads weights, schedules tokens, and executes the model.
A reliable design therefore has four parts: an explicit request policy, a stable API and model-alias layer, measurable selection and capacity rules, and documented fallback behavior. The steps below show how to build those parts without confusing routing with inference.
What is a model router, and what does it actually do?
A model router maps a client request to an eligible model backend according to a policy. It may terminate authentication, normalize an API request, discover healthy replicas, select one endpoint, and forward the request. It does not necessarily contain model weights or execute transformer layers. Those jobs belong to the inference server behind the router.
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Keeping the roles separate lets you change a model, GPU pool, or serving runtime without changing every application client. A public name such as chat-general can resolve to a policy-controlled pool that currently contains several open-weight models or replicas. The alias remains stable while the router changes the destination as health, load, or policy changes.
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The distinction matters operationally. Adding a second replica increases available serving capacity only if the backends have enough GPU memory and scheduling headroom. Adding routing logic alone cannot make a model fit in memory, extend its context window, or reduce the time needed to generate tokens.
Useful routing policies can be simple or model-aware:
- Round robin: cycle through healthy endpoints.
- Random: choose a healthy endpoint at random.
- Consistent hashing: map a key such as a session or tenant to a stable endpoint while limiting remapping when replicas change.
- Power of two choices: sample two endpoints and use the one with the better current score.
- Cache-aware selection: favor a replica likely to hold the request’s prefix in its KV cache.
- Load- or latency-aware selection: score queue state, active requests, token throughput, or predicted latency.
The vLLM Router project documents round-robin, random, consistent-hashing, power-of-two, and cache-aware approaches, together with service discovery and health monitoring; its documentation does not establish a common benchmark ranking among them. See the vLLM Router README.
How should I define the routing problem before choosing an algorithm?
Start with the workload, not with a router feature list. Write down what each request needs and what the system is allowed to do when no backend satisfies all requirements.
Describe the request classes
Separate traffic into classes that have different consequences. Interactive chat may need a fast first token and predictable streaming. Batch summarization may accept queueing in exchange for high aggregate throughput. Retrieval or tool-calling requests may require a larger context window or a specific structured-output feature. Per-tenant privacy rules may require that some prompts never leave a particular cluster or region.
Record capability and protocol requirements
- Required model family or task capability, such as instruction following, code generation, embedding, or vision input.
- Maximum input and output token counts, including the expected context length.
- Features such as JSON or grammar-constrained output, tool calls, multimodal content, log probabilities, or streaming.
- Accepted API dialect and authentication method.
- Data residency, network isolation, tenant, and encryption constraints.
- Priority, deadline, or service-level objective (SLO) class.
Define acceptable outcomes
Decide whether a request may be queued, downgraded to another model, retried on another replica, or rejected. Define whether changing models is acceptable for a conversation, and whether a retry is safe for a non-idempotent tool call. These decisions become router rules rather than ad-hoc incident responses.
Turn requirements into a policy object
A practical policy has an alias, an eligibility expression, a ranking strategy, timeout values, retry limits, and fallback behavior. For example, chat-fast could require streaming and a 16k-token context, exclude a restricted region, prefer replicas with an existing prefix cache, and fall back only before any response bytes are sent. Keep the policy versioned so that a change can be audited and rolled back.
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Expose one client-facing endpoint and translate its model name into an explicit backend pool. The router can accept an OpenAI-compatible request, an Anthropic-style request, or a private schema, but the supported combinations must be tested against the exact versions you deploy.
Use aliases instead of backend addresses
Clients should request a logical name such as chat-general, not a pod IP or GPU host. The router resolves that alias to a pool and records the selected model and replica in telemetry. This allows blue-green deployments, capacity changes, and model retirement without a client release.
Normalize and validate at the edge
Validate required fields, token limits, streaming options, tool definitions, and authentication before selection. Normalize equivalent fields into an internal request object. Reject unsupported features with a clear error rather than silently dropping them.
The vLLM Semantic Router describes an Envoy-based data plane and a policy layer that coordinates model candidates. Its API documentation lists OpenAI Chat Completions, OpenAI Responses when enabled, and Anthropic Messages. Confirm which APIs and backend combinations are enabled in your deployed version using the system overview and Router API documentation.
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Keep inference concerns behind the boundary
The serving backend should own weight loading, batching, token scheduling, KV-cache allocation, and generation. The router should own admission, endpoint selection, retries, and policy. Sharing those responsibilities without a clear boundary makes capacity calculations and failure diagnosis much harder.
Why should filtering happen before ranking?
Ranking an ineligible backend is a category error. First remove every endpoint that cannot meet hard requirements; only then compare the remaining candidates by softer preferences such as latency or cache locality.
Apply hard eligibility checks
- Capability: the model supports the requested task, modality, tools, and output format.
- Context: configured maximum sequence length can accommodate the input plus expected output.
- Protocol: the server implements the requested endpoint, streaming mode, and parameter set.
- Health: readiness checks pass and the endpoint is not in a draining or failed state.
- Deployment: region, tenant, network, data-retention, and hardware policies allow the request.
- Capacity: admission control predicts that the request can fit without violating configured memory or token limits.
Then rank eligible candidates
After filtering, calculate a score or use a deterministic algorithm. A score can combine normalized queue time, active requests, estimated time to first token, output-token rate, cache-hit probability, and priority. Keep hard exclusions separate from scores so an attractive latency number cannot override a privacy or capability rule.
Handle no-match cases explicitly
If the eligible set is empty, return a typed capacity or capability error, queue within a bounded deadline, or invoke a documented fallback class. Do not silently send a request to a model with a shorter context or different data-handling policy.
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Which routing algorithm fits my workload?
No algorithm is universally best. Choose according to request locality, replica homogeneity, burstiness, and the cost of a wrong assignment.
| Strategy | Best fit | Strength | Risk or limitation |
|---|---|---|---|
| Round robin | Similar replicas and steady traffic | Simple, predictable distribution | Ignores request size, queue depth, and cache state |
| Random | Small, homogeneous pools | Minimal coordination | Can create short-lived imbalance during bursts |
| Consistent hashing | Session or tenant affinity | Stable mapping when membership changes | Can concentrate a hot key or preserve a bad assignment |
| Power of two choices | Large pools with changing load | Improves choices using only a small sample | Needs fresh, comparable endpoint state |
| Cache-aware | Long shared prefixes or conversational sessions | May reuse KV-cache work and lower prefill cost | Affinity can reduce fairness if a replica becomes hot |
| Latency/load score | Mixed prompt sizes and strict SLOs | Responds to queue and measured performance | Stale or poorly normalized metrics can cause oscillation |
For a first deployment, round robin with health checks is an understandable baseline. Add cache or load signals only when measurements show that locality or imbalance materially affects the workload. Keep a stable fallback algorithm for periods when telemetry is missing.
How can I route LLM traffic based on latency, load, or cache state?
Use signals that correspond to the bottleneck you are trying to control, and define how often they are refreshed. A router that sees only request count cannot distinguish a 50-token prompt from a 50,000-token prompt.
Cache affinity
When many requests share a system prompt, document prefix, or conversation history, prefer the replica likely to retain that prefix in its KV cache. Affinity can reduce repeated prefill computation, but it must have an escape path when the preferred replica is overloaded or its cache has expired.
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Track queued requests, active sequences, in-flight tokens, and the configured batch or token budget. Penalize endpoints that are near admission limits. A queue-length-only policy can still misroute long generations, so combine it with estimated input and output token demand when available.
Latency signals
Measure time to first token separately from inter-token timing and complete-request duration. A backend may start quickly but generate slowly, or have a fast steady-state rate but a long prefill queue. Use rolling windows and guardrails so one outlier does not redirect the entire fleet.
Priority and fairness
Assign interactive, batch, and background traffic explicit priorities. Fair-share scheduling can prevent a large tenant or batch job from consuming every slot. The llm-d architecture separates proxying, endpoint picking, request handling, and flow control; its endpoint picker scores pods using metrics, KV-cache affinity, and policy, while flow control includes priority queues, fair-share scheduling, and overload protection. See llm-d’s architecture documentation.
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Prevent routing oscillation
Use hysteresis, minimum dwell times, or score smoothing before moving traffic away from an endpoint. Otherwise, every replica can appear attractive immediately after it receives fewer requests, causing synchronized swings and worse cache locality.
How do tensor and data parallelism affect the serving pools?
Routing cannot compensate for an undersized model replica. Capacity depends on GPU memory, parallelism, batching configuration, precision, context length, and the shape of requests.
Tensor parallelism
Tensor parallelism splits a single model across multiple GPUs so a model that does not fit on one device can execute as one logical replica. It introduces communication between GPUs and may reduce the number of independent replicas you can run.
Data parallelism
Data parallelism runs independent copies of the model. More copies generally increase concurrent request capacity, but each copy has less aggregate memory available if you are operating within a fixed GPU budget. That trade-off can reduce KV-cache space per copy and limit long contexts or high concurrency.
Use memory figures as planning examples, not guarantees
Hugging Face’s vLLM Inference Endpoints documentation gives illustrative estimates of about 16 GB for Llama 3 8B in FP16 and about 140 GB for Llama 3 70B in FP16. Those figures are vendor documentation examples, not independent measurements or universal requirements: runtime overhead, weights format, quantization, KV-cache datatype, sequence limits, and batching add materially to the real budget.
Configure sequence and batched-token limits, tensor-parallel size, KV-cache datatype, and the parallelism strategy together. A GPU for local LLM inference is suitable only when its memory and interconnect match the selected model, precision, context, concurrency, and runtime. Do not choose hardware from parameter count alone.
Expose capacity to the router
Publish each backend’s readiness, maximum context, supported features, parallelism layout, and current token or request budget. During warm-up, model load, or rolling replacement, mark the endpoint unavailable for new traffic while allowing existing streams to drain.
What is a practical implementation sequence?
Build the smallest policy that is observable and reversible, then add intelligence as evidence supports it.
- Inventory backends. Record model identifier, revision, context limit, supported APIs, streaming and tool features, region, GPU layout, and health endpoint.
- Create logical aliases. Map client-facing names to pools rather than individual addresses. Version the alias policy in source control.
- Define request classes. Specify interactive, batch, tenant, privacy, and capability requirements, including maximum input and output tokens.
- Implement eligibility filtering. Reject or exclude endpoints that fail capability, protocol, context, health, deployment, or capacity checks.
- Start with a baseline selector. Use round robin or random selection among homogeneous eligible replicas. Add consistent hashing only when affinity is a requirement.
- Add bounded retries. Retry connection and pre-response failures only when the request is safe to replay. Cap attempts and use backoff to avoid amplifying an outage.
- Instrument every decision. Emit policy version, alias, selected model, replica, eligibility exclusions, queue time, first-token time, output timing, completion status, and retry reason.
- Load-test representative traffic. Vary prompt length, output length, concurrency, streaming, cache reuse, and tenant mix. Include cold starts and partial backend failures.
- Canary the policy. Send a small percentage of traffic to the new rule, compare SLOs and error classes, then expand gradually.
- Drain and roll back safely. Stop admitting new work to a retiring backend, let active streams finish when possible, and keep the prior alias policy ready for immediate rollback.
Which metrics should a model router expose?
Measure the complete path from admission to final token, not just the router’s own response time. The llm-d request-handling documentation lists the following dimensions in its observability model; adapt them to your workload rather than adopting universal thresholds.
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- Active requests, queued requests, and in-flight requests and tokens.
- End-to-end duration, queue duration, and backend processing duration.
- Time to first token for streaming responses.
- Inter-token timing or normalized time per output token.
- Input and output token counts, including rejected or truncated requests.
- Retry count, fallback count, and the phase in which a failure occurred.
- SLO violations by request class and backend.
- Cache-affinity decisions and, where available, cache-hit or reuse indicators.
Define SLO targets from observed user needs: for example, an interactive class may target a first-token percentile while a batch class targets completion throughput. Avoid publishing a single latency number that hides prompt-size and concurrency differences. The metric definitions are described in llm-d’s request-handling documentation.
How should fallback, retries, and streaming be designed?
Document the exact failure phase at which a request may move to another backend. A connection refusal before generation is different from a stream that has already delivered tokens to the client.
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Before a response starts
It is usually possible to retry a safe request after a failed connection, readiness transition, or immediate backend error. Preserve the original policy constraints when selecting the replacement; a retry must not bypass privacy or capability rules.
After streaming starts
Switching models mid-response can duplicate or contradict output and may break tool-call state. Unless your protocol defines resumable generation, return a clear stream error and let the client decide whether to restart. OpenLLM documents this behavior explicitly: “Fallback happens only before a response starts.” Its documentation says that once streaming output begins, the selected model remains in use and a later failure is returned to the tool. Treat that as OpenLLM-specific behavior, not a universal router rule; see How OpenLLM routes requests.
Non-idempotent requests
Be especially cautious with tool calls, writes, or jobs that trigger external effects. Attach an idempotency key where the application supports one, and avoid automatic replay when the server may have executed the action but the acknowledgment was lost.
Fallback classes
Define whether fallback means another replica of the same model, another revision, a smaller model, or a queued response. Each choice changes quality, latency, and privacy, so expose the outcome in telemetry and, when appropriate, in response metadata.
How do I validate API compatibility and model aliases?
Compatibility is more than accepting the same URL. Compare request fields, token accounting, streaming event format, tool-call serialization, error codes, authentication, and response metadata for every alias and backend combination.
- Run contract tests for chat completions, responses, and messages endpoints you advertise.
- Test streaming cancellation, client disconnects, and backend termination.
- Verify that unsupported parameters fail loudly and consistently.
- Confirm that the advertised model alias appears in logs and response metadata as intended.
- Check tokenizer and usage-count differences before enforcing token budgets across model families.
Keep compatibility tests in the deployment pipeline. A router upgrade, model-server upgrade, or new backend adapter can change behavior even when the public URL remains unchanged.
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Compare capabilities and operational fit, not undocumented speed or cost claims. The available project documents describe different designs and features rather than a common performance benchmark.
| Comparison axis | Questions to answer |
|---|---|
| Signals and algorithms | Does it support round robin, hashing, cache affinity, queue metrics, predicted latency, or policy scores? How stale may the signals be? |
| API and aliases | Which request formats and streaming modes are supported? Can a stable alias map to multiple model revisions? |
| Deployment footprint | Does it require an Envoy data plane, Kubernetes controllers, service discovery, or additional control-plane components? |
| Queueing and fairness | Are priorities, tenant limits, fair-share scheduling, and overload protection available? |
| Observability | Can it emit queue time, first-token time, token timing, retries, SLO violations, and selection reasons? |
| Cache and sessions | Can it preserve prefix or session affinity without trapping traffic on an overloaded replica? |
| Failover and streaming | When may a request move, and what happens after the first byte or token? |
| Operational complexity | Who owns policy rollout, health checks, upgrades, and recovery when the control plane is unavailable? |
For example, the vLLM Semantic Router emphasizes an Envoy-based data plane and policy-driven candidate selection, while llm-d separates proxying, endpoint picking, request handling, and flow control. These are architectural distinctions to evaluate against your environment, not evidence that one is universally faster or cheaper.
Should I self-host, use managed endpoints, or run Kubernetes serving?
Choose the deployment model that gives you adequate control over data, capacity, and operations for the request classes you defined.
Self-hosted GPU pools
Self-hosting offers control over model revisions, network boundaries, scheduling, and data retention. You must purchase or allocate GPUs, operate drivers and runtimes, size for failures, and handle upgrades. A local-inference GPU purchase should follow measured memory, context, concurrency, and interconnect requirements rather than a model’s parameter count.
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Managed endpoints can reduce cluster operations and provide an easier path to elastic capacity. Verify model availability, region, precision options, context limits, streaming behavior, data handling, current pricing, and service terms before committing; these details vary by provider and change over time.
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Kubernetes-hosted serving
Kubernetes can provide service discovery, health management, rolling deployment, and policy integration for multiple model pools. It also adds scheduling, GPU-device-plugin, networking, and observability complexity. Keep the router’s policy configuration separate from cluster manifests so a routing change does not require rebuilding every serving image.
What common routing failures should I troubleshoot first?
Use the first failing phase in telemetry to narrow the cause.
Requests all land on one replica
Check whether consistent hashing, a fixed session key, or stale endpoint discovery is forcing affinity. Inspect the distribution by alias and replica, then verify that health and capacity filters are not excluding every other endpoint.
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Latency rises although request count is balanced
Compare prompt and output token distributions, queue time, first-token time, and in-flight tokens. Round robin can look balanced by count while one replica receives substantially longer generations or larger contexts.
Cache-aware routing increases queueing
Lower the affinity preference or add a load threshold that overrides it. Cache reuse is a performance hint, not a hard requirement; a hot replica must be allowed to shed traffic.
Fallback repeats a side effect
Inspect whether a retry happened after the backend accepted the request but before the client received a response. Disable automatic replay for non-idempotent operations and require idempotency keys for workflows that can safely retry.
Streaming clients see inconsistent errors
Record whether the failure occurred before headers, after headers, or after the first token. Align router behavior with the client protocol and document that an in-progress stream may terminate rather than switch models.
A new model is rejected despite healthy GPUs
Check eligibility metadata: context limit, API feature flags, tokenizer limits, model revision, and deployment-region policy. GPU readiness alone does not prove protocol or capability compatibility.
How do I roll out a routing policy without disrupting active users?
Separate policy rollout from model rollout and make both reversible. Publish the new policy under a versioned identifier, validate it with shadow or canary traffic where safe, and compare request-class SLOs rather than fleet averages.
- Warm the new model replicas and verify readiness, tokenizer behavior, and API contract tests.
- Mark them eligible for a small canary percentage while retaining the existing pool.
- Monitor errors, queue time, first-token time, token timing, cache behavior, and fallback rate by policy version.
- Increase traffic in stages only after each stage meets the workload’s SLOs.
- When retiring a pool, stop new assignments, allow active streams to drain, and retain logs needed to diagnose late failures.
- Keep the previous alias mapping and backend revision available until active sessions and delayed jobs have completed.
For distributed inference release patterns and component boundaries, consult the Google Cloud announcement about llm-d 1.0, while treating its reported results as specific to the described use case rather than a general router benchmark.
Frequently Asked Questions
Can one router send different requests to different model families?
Yes, if the router’s eligibility metadata distinguishes capabilities, context limits, protocols, and deployment constraints. Use a logical alias or request classifier to select among model families, then apply the same health, capacity, and fallback rules to each pool.
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Use a conversation or tenant key with consistent hashing, or record an explicit session-to-replica assignment with an expiry. Add an overload escape rule so the session can move when the original replica is unhealthy or saturated.
Should routing decisions use model quality scores?
They can, but quality scores must come from an evaluation set that matches your tasks and must be treated as policy inputs rather than universal thresholds. Keep quality eligibility separate from live load scoring so temporary congestion does not silently change the quality tier.
What happens if the router itself becomes unavailable?
Deploy redundant router instances behind a health-checked service address and keep policy state in a versioned, recoverable store. Decide whether clients fail closed, use a static endpoint, or queue during control-plane loss; a static emergency route should still enforce authentication and basic capability limits.
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