How to Speed up AI Inference with vLLM Continuous Batching

Facing long waits or unpredictable spikes when serving chat or assistant models? Conversational AI Companies are leveraging vLLM Continuous Batching, which blends request queuing, dynamic batching, micro batching, and token streaming to raise throughput and tame tail latency on GPUs. This article shows practical ways to tune batching windows, batching policies, concurrency, and GPU utilization, so those exploring continuous batching for vLLM or optimizations for LLM inference speeds can achieve faster and more cost-effective results.

To reach those goals, Inference’s AI inference APIs let you try continuous batching with managed model serving, power applications like a text-to-speech tool, simplify request consolidation, and measure real improvements in latency, throughput, and cost without heavy engineering.

What is vLLM Continuous Batching, and What are Its Benefits?

Think of constant batching like a conveyor belt at a packing station. Instead of waiting until a whole cart is full before the belt moves, items keep sliding on and off the belt one at a time. 

vLLM runs the model at the same granularity: 

• It schedules generation one token step at a time
• Keeps a running batch of active requests
• Fills freed slots immediately with waiting requests

This contrasts with static batching, where you wait for a fixed group to form and then run the whole sequence.

Implementing Dynamic Batching for Latency and Throughput Optimization

What does that buy you? Requests start executing sooner, the GPU stays busy more of the time, and fewer cycles are wasted on padding. Latency for individual users drops because fast requests no longer wait behind slow ones. 

Throughput rises because the GPU processes more useful work per second. And the user experience improves because responses arrive more quickly and consistently.

What Is The KV Cache Bottleneck, And Why Does It Choke Serving

Autoregressive generation produces tokens one at a time. For each new token, the model computes a query vector and then attends to keys and values produced for all past tokens. Computing those keys and values every step would be prohibitively expensive, so systems store them in a KV cache.

The cost of that cache scales with: 

• Context length
• Number of layers
• Heads
• Head dimension

For long prompts or long outputs, this cache becomes huge, and it must remain in memory while a request is being generated. That growth pattern makes memory management the single most considerable constraint on concurrent inference.

KV Cache Characteristics That Break Naive Serving Designs

The KV cache is large per sequence, grows dynamically as generation proceeds, and lives only for the duration of a request. 

When you allocate one contiguous block per request, you hit three problems at once: 

• Wasted space when requests finish early
• Scattered free memory that cannot satisfy a new contiguous allocation
• Heavy allocator overhead from frequent big allocations and frees

Beam search and parallel sampling amplify the pain because many candidate sequences share a prefix, but naive systems copy entire caches for each branch.

Why Traditional Contiguous Allocation Causes Fragmentation And Stalls

Allocating a big contiguous buffer for each request creates internal waste if the request ends short of the reserved length. Over time, allocations and frees of variable sizes scatter free chunks across GPU memory, creating external fragmentation. Even when enough total memory exists, no contiguous block may be available for a new request. 

That causes: 

• Allocation failures
• Forced eviction
• Degraded throughput

Copying full caches for beams or samples produces long tail latency and heavy memory copying. These issues limit how many concurrent requests a server can accept.

How vLLM Continuous Batching Works Step By Step

vLLM schedules work at the step level. Incoming requests enter a queue. At each decoding step, the scheduler assembles a batch from active sequences and, if there is capacity, pulls new requests into that batch. 

The GPU does one forward pass for every active sequence at that step. When a sequence emits the end of the sequence, it frees its slot quickly, and the next step can accept a new request in that place. 

Fine-Grained Scheduling for Resource Optimization

The system never idly waits for an entire request group to finish before adding new work. This iteration-level scheduling reduces head-of-line blocking and minimizes padding overhead. It also forces memory allocation and deallocation to be fast and fine-grained, because active slots change every step.

How Pagedattention Rethinks The KV Cache As Virtual Memory

PagedAttention splits each sequence cache into many small fixed-size blocks, and maps sequence logical blocks to physical blocks much like a page table in an OS. Physical blocks live in a free pool on GPU memory. 

When a token produces new K and V vectors, vLLM writes them into the current logical block if space remains; otherwise, it grabs another physical block and updates the sequence block table.

Efficient Memory Management for Attention Mechanisms

Attention kernels use the block table to gather the K and V vectors across non-contiguous physical blocks during the attention compute. When a sequence finishes, vLLM returns its physical blocks to the pool without needing a costly large free call.

How PagedAttention Kills Fragmentation And Enables Cheap Sharing

Fixed-sized blocks eliminate the need for large contiguous allocations, removing external fragmentation. Internal waste is limited to the tail block per sequence, which is small relative to the whole cache. Because multiple sequences can map their initial logical blocks to the same physical blocks, prompt sharing for parallel sampling and beam search becomes cheap. 

When branches diverge, you only allocate new blocks for new tokens. If a shared block ever needs to change, copy-on-write lets you allocate a new block, copy what you need, and update a single table entry rather than duplicating an entire prefix cache.

What The Attention Kernels And Block Table Routing Require

Attention kernels must support indirect addressing: they look up the block table and gather scattered K and V memory into the matrices used by the attention computation. That needs optimized CUDA kernels to keep memory access efficient despite a non-contiguous layout. 

The block table itself must be compact and fast to query, because the scheduler calls it every step. The memory manager must track free blocks at a very high speed to avoid becoming the scheduling bottleneck.

How Continuous Batching and PagedAttention Amplify Each Other

Continuous batching raises the demand for fast, flexible memory because active sequences join and leave constantly. PagedAttention supplies that flexibility by making allocation and deallocation tiny and local operations. 

With PagedAttention, the serving layer can pack many more sequences into the same GPU memory, which increases the effective batch sizes that continuous batching can form. Larger effective batch sizes raise GPU throughput while step-level scheduling keeps latency low for individual requests.

What To Trade Off When You Pick Block Size And Scheduler Policies

Block size matters. Smaller blocks cut internal waste and let you pack odd-sized caches tightly, but small blocks increase block table size and create more indirection in the attention kernel. 

Larger blocks reduce table overhead and improve locality, but waste more on the final block of each sequence. Think in terms of memory overhead and kernel throughput when you pick the size.

Balancing Latency and Throughput in Scheduler Design

The scheduler must balance latency and throughput. Aggressive admission keeps the GPU busy but may raise tail latency under heavy load. Conservative policies keep a predictable latency but leave GPU cycles unused. 

While tuning, watch: 

• Memory fragmentation metrics
• Step latency
• GPU occupancy

Operational Questions To Ask When You Deploy Vllm Continuous Batching

• How many tokens per second do you need to serve? 
• What is your acceptable p95 latency? 
• How often do users send short versus very long prompts? 
• Do you run beam search or heavy sampling? 

These answers determine: 

• GPU count
• Block size
• Admission thresholds
• Prewarming strategy

Practical settings worth trying: 

• Reserve a small pool of blocks for bursty traffic
• Profile different block sizes to find the best balance for your model and hardware
• Enable copy-on-write only when beam sizes exceed a low threshold to avoid needless overhead

Implementation Pieces You Must Build Or Tune

A fast memory manager that maintains a free block pool and handles allocation in microseconds. A scheduler that implements iteration-level scheduling with admission control based on free block count and compute capacity. Custom attention kernels that perform block table lookups and efficient gather operations. 

Telemetry that reports block usage, fragmentation metrics, GPU occupancy, and step timing so you can iterate on policy.

How Does This Affect Advanced Decoding Modes Like Beam Search And Many Samples?

Beam search benefits heavily since many beams share prefixes. With block-based sharing, the prefix is mapped once and beams reuse it. When beams diverge, you allocate only the new blocks. Parallel sampling from the same prompt becomes cheap because the prompt blocks can be shared across samples. 

That reduces per-sample memory and speeds up multi-sample use cases. What patterns do you use most in production, sampling, or beam search, and at what beam count? Tuning block table sharing policies hinges on those answers.

Want To Test Performance Quickly?

Run a controlled experiment: take a model like Llama 7B, set up two servers, one with contiguous per-sequence caching and static batching, the other with PagedAttention and continuous batching enabled. 

Use a mix of short and long prompts, enable beam search for: 

• Some runs
• Measure p50 and p95 latency
• Throughput
• GPU utilization
• Memory usage

The difference will expose where your bottlenecks live and which scheduler or block size settings need tuning.

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What is a vLLM Continuous Batching Configuration?

Continuous batching in vLLM means the scheduler issues new sequence slots whenever a running sequence completes an iteration. 

The scheduler no longer waits for a fixed global batch to finish; it performs iteration-level scheduling so GPU work fills gaps and latency variance between requests is absorbed. 

Configuring Scheduler Parameters for Performance Tuning

Key pieces you can configure are the scheduler capacity, per-request token reservation behavior, and the memory page or block size for KV cache. These interact: a larger scheduler capacity increases parallel decode throughput but raises per-request jitter and GPU memory pressure, while a tighter capacity reduces jitter but sacrifices throughput. 

Scheduler Knobs You Typically Tune And How They Affect Throughput And Latency

• Max_num_seqs or max concurrent sequences: This limits how many sequence groups the scheduler allows to be active. Raise it to pack more concurrent decodes into the GPU for higher throughput. Lower it to tighten tail latency and avoid context switching inside the scheduler.  
• Per request max_tokens reservation: vLLM uses each request’s declared max_tokens to reserve KV slots unless paged attention is enabled. Large reservation values pre-allocate memory and can fragment GPU memory. Reduce declared max_tokens whenever possible to avoid wasted memory.  
• Iteration time limit or batch wait time: Some deployments expose a small batching window or a max_wait_ms that holds new arrivals briefly to increase batch coalescing. Increase it to gain batching efficiency at the cost of head latency. Set it near your latency SLO in ms.  
• Priority and scheduling policy: Use priorities to prefer low-latency clients. When a high-priority request arrives, the scheduler can preempt lower-priority sequences rather than letting them fill slots. Configure request priority so interactive traffic is not starved. 

Which of these do you change first? If you want raw throughput, increase max_num_seqs and batch wait time. If you wish to reduce latency, consider: 

• Reducing max_num_seqs
• Batch wait time
• Surface priorities

KV Cache And Pagedattention Knobs You Can Change And Why They Matter

Block Size For KV Cache

Smaller page blocks let vLLM allocate KV memory on demand and reduce internal fragmentation. That increases the number of concurrent sequences you can host in GPU memory. 

Smaller blocks add bookkeeping and potential kernel overhead. If you are swapping a lot because GPU memory is tight, reduce the block size. If you have ample GPU memory and want fewer page misses, increase the block size.  

Max Blocks Per Sequence Or Model

That controls how many blocks a sequence may hold before eviction becomes necessary. Increasing it lets long-running generations stay resident; decreasing it forces earlier eviction.  

Preemption Mode

Swap to CPU or recompute. Swapping moves blocks to the CPU then back, and is slower on PCIe but saves compute. Recomputation discards GPU cache and reruns the forward path to rebuild KV. Use swap on systems with fast NVLink or ample PCIe bandwidth. Use recompute if compute is cheap relative to PCIe latency and if you have a few extended sequences.  

Global Page Budget

This is the total number of blocks the system will allocate across sequences. Tighten the budget to bound GPU memory use and force eviction sooner. Loosen it to let more sequences stay resident.  

Tune block size and page budget first if you see out-of-memory or frequent preemption. Adjust preemption mode next based on your PCIe and GPU compute profile.

Selective Batching And Attention Fusion Options To Improve Kernel Efficiency

vLLM and systems inspired by Orca selectively batch non-attention operators while treating attention differently when sequence lengths differ. 

You can control the following:  

• Fused attention backend selection: Choose FlashAttention, Triton fused kernels, or paged attention kernel. Fused kernels reduce round-trip to CUDA and eliminate torch.bmm overhead. If your model supports FlashAttention, you will usually see a considerable speed-up on the attention-heavy parts.  
• Enable or disable selective batching: Some implementations expose flags to group non-attention ops across sequences even when attention cannot be coalesced; keep this on for heterogeneous lengths as it increases GPU utilization.  
• Attention kernel block sizes and launch parameters: When available, tune kernel tile sizes to match your GPU compute capability and the model head dimension. Larger tiles can be more efficient but may need more shared memory. 

If you have lots of short queries with different lengths, enable selective batching and a fused attention kernel to avoid many small torch bmm calls.

Preemption, Swapping, And Eviction Policy Settings Explained With Tradeoffs

vLLM uses iteration-level scheduling and a block manager to decide when to evict. The key controls are:  

• Eviction granularity: vLLM chooses all or nothing eviction per sequence because sequences access all blocks together. If your implementation offers alternative eviction policies, prefer ones that minimize page thrashing.  
• Eviction order and fairness: Configure LRU or priority-based eviction. Choose LRU when all sequences are equal. Choose priority based on when interactive work must be protected.  
• Swap thresholds and hysteresis: Set thresholds to avoid frequent swapping in and out for the identical sequences. Add hysteresis or cooldown windows to reduce thrash.  
• Background swap bandwidth limits: Cap swap throughput to prevent starving the GPU with transfer interrupts. 

If you see a sequence repeatedly swapped out and back in, increase the page budget or raise its priority so it stays resident.

Prompt Handling And Mixing Prefill With Decode: What To Configure

vLLM treats prompt and decode iterations separately for correctness since batched tensors must be homogeneous. 

You can tune:  

• Grouping policy for prompts: Batch prompt runs aggressively because they are token dense. Group them with padding or run them separately, depending on your latency tolerance. If your prompt throughput is bursty, allow multiple prompt iterations back-to-back to reduce context switching.  
• Padding token policies: Limit padding to minimize attention compute overhead while preserving the ability to batch multiple prompts together.  
• Decode priority and concurrency: Give decode sequences higher priority if you need steady online decoding, or let prompts occupy the GPU in batching windows if throughput is the primary goal. 

Which should you pick? If prompt latency matters less than throughput, let prompts run in larger batches. If decode latency matters most, isolate decode from prefill.

Guidelines For Choosing Settings By Workload Pattern

Interactive low-latency small responses

• Max_num_seqs: Low value so each request gets predictable slots.  
• Batch wait time: Tiny, near zero.  
• Block size: Small to reduce resident memory needs.  
• Preemption mode: Avoid swapping to minimize long tail stalls. 

Do you serve chat agents or UI typed responses where millisecond latency matters?

Short Queries With Bursty Arrivals And Mixed Lengths

• Max_num_seqs: Medium to capture bursts.  
• Batch wait time: Small but non-zero to let a few arrivals coalesce.  
• Enable selective batching and fused attention.  
• Set block size to moderate to balance fragmentation against bookkeeping. 

This gives good GPU utilization with modest tail latency.

Long Generations And Throughput-Oriented Workloads

• Max_num_seqs: high to maximize concurrent pipeline.  
• Batch wait time: larger to accumulate more tokens per iteration.  
• Block size: larger, so fewer block allocations and fewer page misses.  
• Preemption mode: prefer swap if PCIe or NVLink is fast; otherwise, recompute.  
• Allow more page budget to reduce eviction. 

Do you stream long documents or run offline batch generation where throughput is king?

Practical Tuning Checklist You Can Apply Right Now

• Measure baseline: record per iteration GPU utilization, batch sizes, and page miss rate.  
• Lower per-request declared max_tokens to avoid over-reservation.  
• Enable fused attention if your hardware supports it.  
• Increase max_num_seqs until GPU utilization plateaus or latency tail worsens.  
• If memory pressure appears, shrink the block size or enable swapping with reasonable thresholds.  
• Set priorities so interactive requests do not get preempted by large offline runs. 

Apply one change at a time and watch the metrics for iteration time, page miss frequency, and end-to-end latency.

What To Watch In Telemetry And Logs While Tuning

Track these signals continuously: 

• GPU utilization
• Kernel launch counts and time
• Attention kernel efficiency
• Page allocation and eviction events per second
• Swap bandwidth and PCIe transfer latency
• Batch size distribution per iteration
• Per-request tail latency percentiles

A high eviction rate or frequent swap activity signals memory budgeting issues. Many small kernel launches indicate that selective batching or fusion is not working. If attention kernels dominate latency despite high utilization, experiment with alternative fused kernels.

Tuning Strategies Based on Request Profiles and Latency SLOs

Question to help you tune: 

What is your typical request length distribution, and how tight is your latency SLO?

Answering that clarifies whether you should favor higher concurrency and swapping, or lower concurrency and strict latency isolation.

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Continuous vs Dynamic Batching for AI Inference

Why Batch Inference Matters For GPU Throughput

GPUs are built for parallel work. Running one request at a time leaves most arithmetic units and memory bandwidth idle. Batch inference groups requests so the model weight loads and kernel launches serve many activations at once, raising throughput by an order of magnitude in many cases. 

The goal on the GPU is to convert wasted cycles into sustained inference throughput while keeping latency within service-level objectives.

No Batching: The Naive Baseline

No batching means each request runs as soon as it arrives. That is simple to implement with frameworks like FastAPI plus PyTorch, but it wastes the GPU. Each forward pass pays the full cost of moving large model weights through caches for just one activation set. 

This approach suits debugging and small-scale test deployments where latency per request is the only metric that matters.

Static Batching: Wait Until A Batch Is Full

Static batching collects requests until the batch reaches a fixed size and then runs them together. That maximizes utilization when traffic is predictable and latency is not sensitive, such as nightly document processing. Static batching simplifies orchestration because the server always runs full batches, but it forces each request to wait for enough peers. 

Use static batching when you can tolerate queued latency and when a separate queueing layer manages flow.

Dynamic Batching: Timer Plus Max Batch For Production

Dynamic batching starts a timer when the first request arrives and collects more requests until the batch is full or the timer expires. You configure a maximum batch size and a wait window. This reduces average latency under sporadic traffic while still giving you throughput gains during bursts. 

Dynamic batching excels for modalities where each request takes roughly the same time, like image generation with a fixed decode cost per sample. It is also easier to add to existing model servers because you only need a batching layer that enforces the size and time rules.

Continuous Batching: Token-Level Scheduling And VLLM-Style Servers

Continuous batching operates at the token level. For LLMs, the expensive part is predicting the next token many times. Continuous batching walks model layers across a rotating set of in-flight sequences so you reuse weight loads across heterogeneous sequence lengths. 

The server performs a prefill pass for the initial context, then runs next token prediction across many requests in an interleaved way. 

Benchmarking and Comparing Continuous Batching Implementations

Implementations include vLLM continuous batching, vLLM server features, TGI, and TensorRT LLM using in-flight batching to achieve similar gains. This approach keeps the GPU busy when responses vary widely in length because slots free up and new requests slot in without waiting for the longest response.

How These Methods Differ In Practice

• Granularity: No batching acts on requests. Static and dynamic batch at request granularity. Continuous batching slices work at the token level so the same model weights can serve tokens from many requests in one layer sweep.  
• Latency behavior: Static gives the highest queued latency. Dynamic bounds that latency by a window. Continuous reduces tail idle time by absorbing variable lengths, lowering per token latency variance.  
• Complexity: No batching is trivial. Static batching requires queueing but is straightforward. Dynamic batching needs a timer, a batch queue, and tuning of batch size and window. Continuous batching requires token orchestration, state management for variable sequence shapes, and support from the inference server or runtime.  
• Throughput shapes: Static can give maximal throughput when queues are full. Dynamic balances throughput and latency across traffic patterns. Continuous maximizes throughput for autoregressive models by eliminating wait idle time between batches.

Trade Offs And Key Configuration Knobs

• Batch size and wait window: For dynamic batching tune the maximum batch size and the time window to hit your latency SLO while preserving throughput. Larger batch sizes raise throughput but increase waiting.  
• Sequence shapes and anticipated lengths: Continuous batching needs estimates of input and output token shapes so the server can plan memory and slot usage. Over estimating wastes memory. Under estimating forces reallocation.  
• Prefill cost versus next token cost: The first token generation includes a prefill that is compute heavy. Continuous batching focuses optimization on next token prediction where the per token cost dominates across the request lifetime.  
• Complexity and server support: Continuous batching relies on an inference server that manages state and scheduling. vLLM continuous batching and TGI provide that orchestration and token streaming primitives. If your runtime lacks token level scheduling, dynamic batching is simpler to adopt.  
• Predictability of per-request work: If each request needs roughly equal compute, dynamic batching delivers strong results with easier operation. If response lengths vary a lot, continuous batching recovers idle cycles and reduces tail latency by keeping GPU slots occupied.

Practical Guidance And Decision Points

Which models are you serving and what traffic pattern do you expect? If you run autoregressive LLMs with variable generation lengths and you target low latency and high throughput, prioritize continuous batching and a vLLM capable server. 

Pick a sensible maximum batch size and plan for sequence shape variance. If your workload is images or other models with consistent per request cost, use dynamic batching with a tuned window and batch size. For offline bulk jobs where latency is irrelevant, static batching remains the simplest high throughput choice.

Questions To Help Next Steps

• What model family and approximate context sizes are you deploying?  
• What latency SLOs and peak throughput targets must you hit?  
• Do you have an inference server with vLLM continuous batching or TGI available, or will you add dynamic batching to a custom stack? 

Answering these will let you pick batch size, window, and the operational approach that best uses GPU bandwidth, minimizes tail latency, and fits your engineering constraints.

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Start Building with $10 in Free API Credits Today!

Inference gives you OpenAI-compatible serverless inference APIs for leading open source LLM models. You call familiar endpoints and get predictable request semantics while the platform handles autoscaling, cold start reduction, and warm start behavior. 

The API supports streaming tokens, batched requests, and async job submission, so you can pick low-latency paths or high-throughput pipelines as your product requires. Want to test it quickly? Start building with $10 in free API credits and map your existing OpenAI calls to these endpoints.

vLLM Continuous Batching Explained and Why It Changes Throughput

vLLM continuous batching uses a continuous batching scheduler to coalesce incoming prompts into efficient GPU-ready batches. Instead of waiting for a fixed timer or a fixed count, the scheduler forms a rolling batch that grows as new requests arrive, then streams tokens back as they generate. 

This reduces idle GPU time and smooths out bursty traffic by enabling micro-batching, request coalescing, and adaptive batch sizing. How you set the batching window and the priority rules will determine your latency SLA and tail latency behavior.

Dynamic Batching Tactics and Batch Scheduling Mechanics

Dynamic batching adapts batch size at runtime based on queue depth, token length distribution, and latency SLOs. The scheduler may use heuristics like size-aware packing, token budget per batch, or priority lanes for small latency-sensitive requests. 

Implementing batch scheduling with preemption and priority queues keeps short requests from getting stuck behind long ones. What metrics should you monitor to tune those knobs effectively?

GPU Utilization, Memory Management, and Model Parallelism

Maximizing throughput means squeezing more useful work out of GPU cycles. Use mixed precision and int8 quantization to cut memory pressure and increase batch capacity. Combine tensor parallelism and pipeline parallelism to shard large models across multiple devices and reduce peak memory. 

Offload embeddings or non-critical tensors to host memory when you need extra headroom. Employ CUDA graph captures and kernel fusion to reduce launch overhead and improve steady state throughput.

Token Streaming, Micro Batching, and Latency Tradeoffs

Token streaming reduces perceived latency by delivering partial outputs as they arrive. Micro-batching groups small requests into tiny batches to preserve low latency while still gaining some packing efficiency. But larger micro batches increase throughput at the expense of tail latency. 

Use hybrid strategies: give high priority to streaming small requests while routing bulk jobs to an async batch pipeline that accepts higher latency.

Specialized Batch Processing for Large-Scale Async AI Workloads

For large async workloads, design a separate batch processing tier. Queue jobs, shard documents into chunks, and group similar tasks into large, efficient batches during off-peak windows. 

Include retry logic, checkpointing for long-running jobs, and parallel chunk embedding generation so indexing and ingestion keep pace with demand. How you partition and schedule those jobs drives cost per query and end-user latency.

Document Extraction and RAG Workflows That Scale

Document extraction for retrieval augmented generation requires chunking, OCR, or parsing, and embedding generation. Batch document preprocessing to produce consistent chunk sizes and use vector indexing for fast nearest neighbor retrieval. 

When you couple document extraction with vLLM continuous batching, you can stream chunk embeddings, reduce duplicate encoding via caching, and keep RAG latency predictable by batching retrievals separately from generation.

Cost Efficiency, Performance Balancing, and the $10 Trial

You will trade throughput for latency and cost. Use quantized models for bulk inference, reserve full precision or larger models for high-value queries, and route requests by priority to the appropriate model class. 

Voice AI gives you a low barrier to test these trade-offs with $10 in free API credits. Will a smaller quantized model plus aggressive continuous batching meet your SLOs while cutting cost?

Integration Patterns and OpenAI Compatible Migration Steps

Mapping an existing OpenAI integration typically means swapping endpoints, passing the same request shape, and enabling streaming if you used streaming before. Use SDKs that preserve familiar semantics like stop sequences and role content. 

Test with a replay of production traffic to measure: 

• Cold start behavior
• Batching effectiveness
• Latency percentiles

Operational Monitoring and Best Practices for Inference Optimization

Track throughput, mean latency, p90 and p99 tail latency, GPU utilization, batch size distributions, and queue depth. Set alerts on sudden increases in token length or shifts in request patterns. 

Warm up models during expected peaks, pin memory where possible, and use prioritized queues so short interactive requests do not wait behind large generation jobs. Which of these metrics will you make part of your daily dashboard?