How Do You Scale LLM Inference to Handle Thousands of Concurrent Users in 2027?

By Kory White · Updated Oct 2026 · 9 min read

Direct answer

You scale LLM inference by stacking a few well-understood layers. Inside each GPU server, run a serving engine that does continuous batching and a paged KV cache so many requests share the GPU. Across servers, run horizontal replicas behind a load balancer with a request queue. Scale the replica count on queue depth or tokens in flight, not CPU. Stream responses so users see text early, cache shared prompt prefixes, put rate limits and priority tiers in front, and keep a managed API as overflow for spikes.

The number that ties it together is tokens per second per GPU at your latency target, measured on your own model and traffic. No source publishes a universal replica count per thousand users, so this page gives you the method and labels any example figures as illustrative.

This page is about concurrency and throughput. For cutting the bill see reducing GPU costs when serving LLMs, for the orchestration layer see Kubernetes in AI infrastructure, and for hardware see choosing an inference accelerator.

First, define what a concurrent user costs

Thousands of users is not thousands of simultaneous GPU jobs. Most users are reading, typing or idle. What loads the system is the number of requests in flight and the tokens they generate. A standard queueing result, Little's law, gives the first estimate: average requests in flight equals the arrival rate times the average time each request spends in the system.

Illustrative arithmetic, not a measurement: 50 requests per second with each request lasting 12 seconds means about 600 requests in flight at once.

That is why you plan in requests per second and tokens per second, and why the latency of one request feeds back into how many requests you hold at once.

Pick a serving engine that batches continuously

A naive server processes a fixed batch until every sequence finishes, leaving the GPU idle behind the longest one. Anyscale describes the difference: with static batching "the batch size remains constant until the inference is complete," while with continuous batching a new sequence is inserted as soon as one completes. NVIDIA's TensorRT-LLM docs call the same idea in-flight batching, also known as continuous batching or iteration-level batching.

vLLMPagedAttention paper reports vLLM improves the throughput of popular LLMs by 2-4x at the same latency as FasterTransformer and Orca (arXiv 2309.06180). The vLLM blog reports up to 24x over Hugging Face Transformers and up to 3.5x over TGI, on LLaMA-7B on an NVIDIA A10G and LLaMA-13B on an NVIDIA A100 40GB, with request lengths from ShareGPT (vLLM blog).
TensorRT-LLMIn-flight batching and a paged KV cache that decomposes the cache into blocks from a pool; chunked context splits long inputs into chunks (TensorRT-LLM docs). No throughput figure is quoted on that page.
SGLangIts paper reports up to 6.4x higher throughput than state-of-the-art inference systems and introduces RadixAttention for KV cache reuse (arXiv 2312.07104). The abstract does not name the baselines in the passage I read.
Hugging Face TGIOffers continuous batching, token streaming over Server-Sent Events, tensor parallelism and Prometheus metrics, but its docs now say it is in maintenance mode and recommend vLLM and SGLang going forward (TGI docs).

These are each project's own numbers on the models, GPUs and workloads named, and they are maximums rather than promises for your traffic. The Anyscale post reports up to 23x over naive static batching with vLLM on Meta's OPT-13B on one A100 40GB, using 1,000 requests of 512 input tokens in a burst, and about 8x from continuous batching alone (Anyscale). Treat the choice between engines as something to settle with your own load test; see how to load test an LLM inference service.

The KV cache sets how many users fit on one GPU

Every active sequence keeps its attention keys and values in GPU memory. That memory, not raw compute, often caps how many requests one GPU can hold. The vLLM team says existing systems waste 60% to 80% of memory through fragmentation and over-reservation, that a single LLaMA-13B sequence can take up to 1.7GB of KV cache, and that PagedAttention cuts the waste to under 4% (vLLM blog).

Paged caches work by splitting the KV cache into blocks looked up through a table, so the cache need not be contiguous and blocks are allocated as needed (TGI's PagedAttention page; TensorRT-LLM). More free memory means a bigger batch, which means more tokens per second per GPU.

No per-token KV cache size is published in the sources used here, and it depends on the model's layers, heads and precision. Read it from your model's configuration, or measure how much memory your engine reports free after loading the weights, rather than copying a figure from another model.

Load balancing, queueing and prefix-aware routing

One engine on one node does not reach thousands of users. You run replicas and route to them. The vLLM production-stack is one open-source example: it aims to let you scale from a single vLLM instance to a distributed deployment without changing application code, with a router that supports round-robin and session-ID routing, service discovery through the Kubernetes API, and per-instance metrics including QPS, time to first token and pending, running and finished requests.

Plain round-robin ignores something LLM servers care about: cache locality. The production-stack's prefix-aware routing tutorial describes sending requests that share a prompt prefix to the same instance so that instance can reuse its cached attention data (production-stack prefix-aware routing tutorial). The README marks prefix-aware routing as work in progress, and the tutorial publishes no performance figures, so verify the gain on your traffic.

Queue at the edge. Hold excess requests in a bounded queue in front of the replicas so a burst becomes waiting time, not errors. Cap queue length and time so users get a clear failure instead of a hang.
Route on load, not just rotation. Use the pending and running request counts the router exposes to avoid piling onto a busy replica.
Stream the response. Server-Sent Events token streaming, as TGI documents, lets users start reading after the first token instead of waiting for the full answer.

Autoscale on queue depth or tokens in flight

CPU use is a poor signal for a GPU server. Scale on what the engine reports. vLLM exposes metrics on a /metrics endpoint, including time to first token and inter-token latency histograms (vLLM metrics docs). Feed a queue-depth or in-flight signal to the autoscaler.

In Kubernetes, the Horizontal Pod Autoscaler computes desired replicas as the current replica count times the ratio of current to desired metric value, rounded up, and supports custom and external metrics through the custom.metrics.k8s.io and external.metrics.k8s.io APIs (Kubernetes docs). The same page says the sync period defaults to 15 seconds, scale-up has no stabilization window, and scale-down uses a 300-second window by default to avoid flapping. KEDA works alongside the HPA, can scale a deployment to zero, and has a Prometheus scaler.

GPU replicas are slow to start because weights must load before they can serve. Autoscaling reacts after the spike has begun, so keep headroom and use the queue to absorb the gap. The production-stack README lists autoscaling on vLLM-specific metrics as an upcoming feature, so you may need to wire your own metric. For the cluster side see the Kubernetes explainer.

Latency targets: TTFT and TPOT

Set service-level objectives on two numbers. NVIDIA's NIM benchmarking docs define time to first token (TTFT) as the wait from sending a query until the first non-empty token arrives, and inter-token latency, also called time per output token (TPOT), as the end-to-end latency minus TTFT, divided by the output tokens minus one. End-to-end latency includes queueing, batching and network time (NVIDIA).

Batching raises throughput but lengthens each request, so there is a trade-off. Total tokens per second across all simultaneous requests is the system throughput; per-user tokens per second is what one reader experiences. Choose the SLOs from the product (chat needs low TTFT; batch summaries do not), then find the highest throughput that still meets them. For causes of slowness see what causes high LLM inference latency.

Prefix and prompt caching

Many requests start with the same system prompt, tool definitions or document. Engines can reuse the computed KV cache for a shared prefix (SGLang's RadixAttention is one example), and hosted APIs offer prompt caching. This saves prefill work, which frees capacity for more users. It does nothing when requests share no prefix. For a full-response cache see semantic caching for LLM inference.

Rate limits, priority tiers and managed APIs as overflow

Protect your own fleet with limits per user, per team and per tier, and put interactive traffic ahead of batch work. Providers use the same design, and their published limits show how to size overflow. Figures below were read on October 20, 2026 and change, so check the current pages.

Anthropic: how limits are measuredRequests per minute, input tokens per minute and output tokens per minute per model class, enforced with a token bucket that replenishes continuously. For most models only uncached input tokens count toward the input limit (Anthropic rate limits).
Anthropic: Claude Sonnet 5.5 standard limitsStart tier 1,000 RPM, 2,000,000 input and 400,000 output tokens per minute. Build tier 5,000 RPM, 5,000,000 and 1,000,000. Scale tier 10,000 RPM, 10,000,000 and 2,000,000. Higher limits are arranged with sales.
Anthropic: caching effectThe page's own example: with a 2,000,000 input-token limit and an 80% cache hit rate you could process 10,000,000 total input tokens per minute.
OpenAI: how limits are measuredRequests, tokens and images per minute, plus daily limits for some; you hit whichever threshold you reach first. Limits apply at organization and project level and vary by model (OpenAI rate limits).
OpenAI: ramping guidancePast 1 million input tokens per minute, do not raise traffic by more than 50% every 15 minutes; use exponential backoff with random jitter and cap retries.

Two lessons carry over to your own platform. First, ramp traffic gradually, since both providers warn that sharp increases can trigger 429 errors even within the stated limits. Second, route work that can wait to a batch path: both providers document a batch API with its own limits, and Anthropic's Scale tier lists 4,000 requests per minute and 500,000 batch requests in the processing queue for its Message Batches API.

A managed API makes a sensible overflow target: when your queue or time to first token passes a threshold, send lower-priority or non-sensitive requests there. Check data-handling terms, model quality differences and the provider's limits first, and watch spend caps, since Anthropic documents that hitting a monthly spend cap returns a 429 with no retry-after header.

A capacity-planning method you can run

No published figure can replace a measurement on your model, GPU and prompts. The method has few steps, in order:

Define the workload. Peak requests per second, typical input and output lengths, and how many streams run at once (use Little's law above).
Set the SLOs. A TTFT target and a TPOT target at a high percentile such as the slowest few percent of requests, or whatever your product needs.
Load test one replica. Raise concurrency until a latency target breaks. Record tokens per second per GPU just below that point.
Divide. Required total tokens per second divided by tokens per second per replica gives replicas. Add headroom for failures, deploys and slow scale-up.
Re-measure. Whenever the model, quantization, engine version, GPU or prompt mix changes, repeat the load test and the division.

Illustrative example, invented to show the arithmetic and not measured on any system: 2,000 simultaneous streams at 20 tokens per second each need 40,000 tokens per second. If your load test shows 2,500 tokens per second per replica at your latency target, that is 16 replicas, and 25% headroom makes it 20.

Hardware choice changes the load-test result more than anything else; see choosing an inference accelerator and the cost levers in reducing GPU costs.

Questions people ask

How many GPUs do I need for thousands of concurrent users?

There is no published universal number. It depends on the model, GPU, prompt and output lengths and your latency targets. Measure tokens per second per GPU in a load test, compute total tokens per second needed, and divide, then add headroom.

What is continuous batching?

A scheduling method where finished sequences leave the batch and new ones join at every step, instead of waiting for the whole batch to finish. TensorRT-LLM calls it in-flight batching. Anyscale reports about 8x over naive static batching from it alone in its OPT-13B A100 test.

Should I autoscale on CPU?

Usually not. Scale on what the engine reports, such as queue depth, requests in flight or latency histograms, through the Kubernetes HPA with custom or external metrics, or KEDA.

Is Hugging Face TGI still a good choice?

Its own documentation says it is in maintenance mode and recommends vLLM and SGLang going forward, so new deployments usually start with those or with TensorRT-LLM.

Can a managed API handle my overflow?

Often, within the provider's published limits for your tier. Anthropic and OpenAI both document requests-per-minute and tokens-per-minute limits, 429 responses and retry guidance. Ramp gradually and check data terms first.

Sources

vLLM: Easy, fast, and cheap LLM serving with PagedAttention · KV cache waste and throughput figures
Efficient Memory Management for LLM Serving with PagedAttention · vLLM paper
Anyscale: Continuous batching for LLM inference · Batching benchmark and conditions
TensorRT-LLM docs: in-flight batching and paged KV cache · Engine features
SGLang paper · RadixAttention and throughput claim
Hugging Face Text Generation Inference docs · Features and maintenance mode
vLLM production-stack · Router, routing algorithms, metrics
vLLM metrics docs · Prometheus metrics endpoint
Kubernetes Horizontal Pod Autoscaling · Scaling formula, defaults
KEDA scaling deployments · Event-driven autoscaling
NVIDIA NIM LLM benchmarking metrics · TTFT, ITL/TPOT, throughput definitions
Anthropic API rate limits · Tiers, limits, caching effect
OpenAI API rate limits · Measures, tiers, retry guidance

Figures were read from the linked pages on October 20, 2026. Benchmarks are the publishers' own, on the models, GPUs and workloads named; provider limits and prices change. Example numbers in the capacity method are illustrative. Test on your own traffic before committing.

Top photo: Carl Lender / CC BY 2.0 via Wikimedia Commons