TL;DR
LLM inference optimization is the work of serving a trained model faster and cheaper while keeping output quality the same. Start by measuring time to first token, time per output token, goodput and cost per successful task. Those numbers tell you which bottleneck you have, whether it is compute, memory, queueing or wasted tokens. Slow first tokens point to prompt length and caching, while slow generation points to quantization and speculative decoding. Memory errors call for KV-cache management, and idle GPUs call for continuous batching or routing to smaller models. Keep any change only if quality and p95 latency hold on real traffic.
Key Takeaways Prefill reads the prompt in parallel and leans on compute, while decode writes one token at a time and waits on memory. Measure time to first token, time per output token, p95 latency, goodput and cost per successful task before changing anything. Quantization to FP8 or 4-bit formats cuts memory, but quality has to be checked task by task. Continuous batching and a paged KV cache raise throughput, and standard speculative decoding speeds generation without changing outputs. Some of the cheapest wins sit outside the GPU, in shorter prompts, prompt caching and routing easy requests to smaller models. Treat every optimization as a release, with real-traffic benchmarks, quality regression tests and a rollback plan. Watch on YouTube
Why Do Enterprise AI Pilots Fail After the Demo?
A short from Kanerika on why a generative AI pilot that looks great in the demo stalls once it meets real data and real compliance requirements.
One Model, Two Very Different Bills
Take an illustrative case, with hypothetical numbers. An internal assistant runs on one open-weight model during a pilot, and the GPU invoice is small enough that nobody reads it closely. Three months later the same model serves the whole company. Now the monthly bill is ten times the pilot’s, and users complain that answers take too long to start.
Yet nothing about the model changed. Instead, prompts grew longer, requests waited in a queue, and the GPUs spent much of their time moving data rather than computing.
LLM inference optimization closes the gap between that pilot bill and the production bill. The work runs in a fixed order, which is to measure, diagnose, fix and then prove that quality held.
What Is LLM Inference Optimization?
LLM inference optimization is the practice of making an already trained model answer faster and cheaper at the same quality. Inference is the serving side of a model’s life, the moment it reads a prompt and writes a response.
That distinction shapes budgets. A model is trained once, or retrained occasionally, but it runs inference on every request for as long as it stays in service. Kanerika’s guide to AI inference vs training covers that split in more depth.
Prefill vs Decode: Why the Two Phases Behave Differently
Every request moves through two phases before it completes. In prefill, the model reads the whole prompt at once and processes those input tokens in parallel. Because that work is compute-heavy, it decides how long the user waits before the first word appears.
Decode comes next, once the prompt is processed. The model generates output tokens one at a time, and each new token depends on the ones before it. According to NVIDIA’s inference optimization guide , decode is memory-bound, because moving weights and cached values from memory dominates latency more than the arithmetic does.
This is why a slow first token and slow streaming are different problems. The first one usually traces back to prefill, and the second one to decode.
Model, Serving Engine and Hardware Are Separate Layers
Three separate layers decide inference speed. The model sets the weights and the attention design, and the serving engine schedules requests and manages memory. Finally, the hardware sets the limits on compute and memory bandwidth.
Separating the layers also helps diagnosis. A team can keep the same model, change only the engine settings and get a very different bill. Kanerika’s comparison of an LLM and the vLLM engine explains why the model and the engine are separate choices.
Inference Optimization vs Training and Fine-Tuning
Fine-tuning, by contrast, changes what a model knows or how it behaves on a task. It does not, by itself, make serving cheaper, although a smaller fine-tuned model can replace a larger general one. Parameter-efficient fine-tuning sits on the training side of that line, while the techniques in this guide sit on the serving side.
The LLM Inference Metrics That Matter
Without a baseline, optimization is guesswork. Six numbers describe how a deployment behaves, and each one points to a different part of the stack.
Time to First Token, Time per Output Token and End-to-End Latency
Time to first token (TTFT) measures the wait between sending a request and seeing the first token. It reflects queueing plus prefill, so it grows with prompt length. Time per output token (TPOT) measures the gap between tokens once streaming starts, so it reflects decode speed.
End-to-end latency is the full request time, roughly TTFT plus TPOT multiplied by the number of output tokens. Report it at p50, p95 and p99, because averages hide the slow tail. Streaming changes perception too, since a fast first token makes a long answer feel quicker.
Latency matters because people wait on it. A chat assistant with a slow first token feels broken, whereas a batch summarization job cares more about total throughput than about any single response.
Throughput vs Goodput
Throughput counts tokens or requests per second, regardless of whether those requests met their deadlines. Goodput, however, is stricter. The DistServe paper defines it as the maximum request rate a system can serve while meeting both its TTFT and TPOT targets.
The difference shows up when traffic peaks. For example, a server can post high throughput while a share of its requests miss the latency target, and goodput exposes that gap.
Memory, Quality and Cost Metrics
GPU memory use and KV-cache occupancy show how close a server is to its concurrency limit. Quality metrics, such as task accuracy or a rubric score, catch the regressions that faster settings can introduce. Cost per successful task then ties all of it to money, and a later section shows how to calculate it.
Metric What it measures Likely cause when it is bad Measurement caveat Time to first token (TTFT) Wait until the first token appears Long prompts, queueing or slow prefill Rises with prompt length, so test real prompt sizes Time per output token (TPOT) Gap between streamed tokens Memory-bound decode or batch contention Varies with batch size and output length p95 and p99 latency The slow tail of requests Bursts, queueing or long outlier prompts Needs enough requests to be stable Throughput Tokens or requests per second Idle GPUs or poor batching Ignores whether deadlines were met Goodput Requests per second that meet TTFT and TPOT targets Capacity that misses the latency target Depends on the targets you set Cost per successful task Spend divided by tasks that passed the quality bar Retries, idle capacity or oversized models Needs a written definition of success
Where LLM Serving Bottlenecks Come From
LLM serving bottlenecks fall into a few categories, and each also leaves a different fingerprint in the metrics above. Diagnosis comes before any fix, since the wrong fix can make things worse.
Model Weights and GPU Memory
Before anything else, weights claim memory. NVIDIA’s worked example puts a 7-billion-parameter model such as Llama 2 7B at roughly 14 GB in 16-bit precision, for the weights alone. After that, whatever memory remains is the room left for serving requests.
The KV Cache Problem
During decode, the model stores the keys and values of earlier tokens in a KV cache. That way it does not recompute them for every new token. That cache, however, costs memory. In the same NVIDIA example, one 4,096-token sequence at batch size 1 needs about 2 GB of KV cache for Llama 2 7B in FP16.
The cache also grows linearly with batch size and sequence length. Ten concurrent requests of that length would need about 20 GB on top of the 14 GB of weights. That is how a server that looks oversized for its weights still hits a memory wall under load.
Memory Bandwidth, Compute and Queueing
Decode speed is limited by how fast data moves out of GPU memory, not by raw compute. Prefill, by contrast, leans on compute. Queueing sits on top of both, because a request that waits for a free slot adds that wait to its TTFT.
Finally, a fourth bottleneck hides outside the GPU entirely. Oversized prompts, repeated context and requests sent to a larger model than the task needs all burn tokens before any serving trick can help. Teams choosing among open-source LLMs face this early, because model size sets the floor for every later optimization.
Model-Level Techniques: Make the Model Cheaper to Run
Model-level techniques change the artifact you serve, so they come first. They shrink the weights, shrink the cache or swap in a smaller model, and each one carries some risk to quality that needs checking.
Quantization: FP8, INT8, INT4 and FP4
Quantization stores weights, and sometimes activations, in fewer bits than the 16-bit default. Fewer bits mean less memory to hold and less data to move during decode, which targets the memory-bound phase directly.
The research results are strong, although they are tied to their setups. The GPTQ paper quantized 175-billion-parameter models to 3 or 4 bits per weight in about four GPU hours. In the authors’ tests, the accuracy loss was negligible. They also measured end-to-end speedups of around 3.25x on NVIDIA A100 and 4.5x on A6000 GPUs against FP16.
AWQ , by comparison, takes a weight-only approach. Its authors found that protecting only 1% of salient weights, chosen by looking at activations, greatly reduces quantization error.
Hardware decides which formats pay off. The vLLM FP8 documentation says FP8 weights and activations run on NVIDIA Ada Lovelace, Hopper and Blackwell GPUs. Older Turing and Ampere GPUs, however, get weight-only FP8. The same page cites a 2x cut in model memory and up to 1.6x higher throughput, with minimal accuracy impact.
NVIDIA’s NVFP4 format , introduced on June 24, 2025, goes down to 4-bit floats on Blackwell GPUs. NVIDIA reports about 3.5x less memory than FP16 and 1% or less accuracy loss against FP8 on DeepSeek-R1-0528, which is a single-model vendor result.
Smaller weights do not always mean lower latency, though. A format without fast kernels on your GPU can run slower than the 16-bit original, so benchmark before you commit.
KV-Cache Quantization
The cache can also be quantized. Storing keys and values in an 8-bit format roughly halves the cache footprint against 16-bit, which leaves room for more concurrent requests. Even so, long-context accuracy deserves its own test after this change, because long answers depend on many cached values.
Distillation and Pruning
Distillation trains a smaller student model to reproduce a larger teacher’s behavior, an idea set out by Hinton, Vinyals and Dean in 2015 . The student is cheaper on every request, but it needs training data and an evaluation set that reflects real tasks. Kanerika’s guide to small language models covers when a compact model can carry the load, and its SLMs vs LLMs comparison weighs the trade-offs.
Pruning, similarly, removes weights or whole structures that contribute little to the output. Its speed benefit depends on whether the serving engine and GPU can exploit the resulting sparsity. For that reason, pruning needs the same benchmark discipline as quantization.
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Explore LLM Development Attention Architecture: MQA and GQA
Multi-query attention (MQA) and grouped-query attention (GQA) shrink the KV cache by sharing key and value heads across query heads. The GQA paper found that uptrained GQA reaches quality close to standard multi-head attention at a speed comparable to MQA.
In other words, this is a model-design property, not a serving switch. You pick it when you pick the model, and it is one reason two models of similar size can have very different serving costs.
Serving-Level Techniques: Get More From the Same GPUs
Serving-level techniques leave the model untouched and instead change how the engine runs it. None of them needs retraining, and they are where open-source engines compete hardest.
Continuous Batching
Static batching waits for a group of requests, runs them together and holds the whole batch until the longest one finishes. Continuous batching, introduced as iteration-level scheduling in the Orca paper at OSDI 2022, swaps requests in and out at every generation step instead.
In Anyscale’s June 2023 benchmark on a single NVIDIA A100 with OPT-13B, continuous batching reached up to 23x higher throughput than naive static batching. That figure included vLLM’s memory optimizations, and continuous batching alone gave 8x.
Paged KV Cache and Prefix Caching
Before paging, engines reserved large contiguous blocks of memory per request, and much of that space sat unused. PagedAttention borrows the idea of virtual memory pages and allocates the cache in small blocks as it grows. The vLLM paper from SOSP 2023 reports near-zero cache waste and 2-4x higher throughput at the same latency than FasterTransformer and Orca in its tests.
For the engine-level detail, Kanerika’s explainer on how vLLM works walks through the paging model step by step.
Prefix caching extends the same idea across requests. The vLLM prefix caching docs describe the pattern: many requests share a long system prompt and then diverge. The engine then reuses the cached keys and values for the shared part. SGLang’s RadixAttention generalizes that reuse. Its December 2023 paper reports up to 6.4x higher throughput for the whole SGLang system, of which RadixAttention is one part.
Speculative Decoding
Speculative decoding instead attacks the one-token-at-a-time limit of decode. A small, fast draft model proposes several tokens, and the large model checks them all in a single forward pass. After that check, accepted tokens are kept, and the first rejected one is replaced by the large model’s own choice.
Because the large model verifies every token, the output distribution does not change. Leviathan and colleagues showed 2x to 3x faster sampling on T5-XXL with identical outputs in their ICML 2023 paper.
Since that paper, production engines have shipped several variants. The vLLM speculative decoding docs list EAGLE, multi-token prediction, draft models, n-gram matching and suffix decoding, and call the approach lossless up to hardware numerics. One method, FLy, relaxes verification on purpose and does not preserve the output distribution.
The gain depends on how often the large model accepts the drafts. Acceptance rates vary by workload, so measure them on your own traffic before counting on a speedup.
Optimized Attention Kernels and CUDA Graphs
FlashAttention computes exact attention while cutting reads and writes between GPU high-bandwidth memory and on-chip SRAM. Its widely quoted 3x speedup came from training GPT-2, not from inference, a detail that matters when reading speedup claims.
FlashAttention-3 targets Hopper GPUs and reports 1.5x to 2.0x speedups on H100 in its July 2024 paper. CUDA graphs, on the other hand, are a separate trick. They record a sequence of GPU kernel launches once and replay it, which cuts launch overhead during decode.
Chunked Prefill for Mixed Traffic
A long prompt can occupy the GPU while it is prefilled, stalling the token streams of every request already decoding. Chunked prefill splits that prompt into smaller pieces and interleaves them with decode steps. As a result, active users see steadier TPOT, while the long request’s own first token may arrive a little later.
Technique Bottleneck it targets Evidence (source, setup, date) Quality risk Effort Continuous batching Idle GPU time between requests Anyscale, one A100, OPT-13B, 2023: up to 23x throughput None Low (engine setting) Paged KV cache KV-cache memory waste vLLM paper, SOSP 2023: 2-4x throughput vs FasterTransformer and Orca None Low Prefix caching Repeated long prompts SGLang paper, Dec 2023: up to 6.4x throughput (whole system) None if scoped per tenant Low Speculative decoding Slow, memory-bound decode Leviathan et al., ICML 2023, T5-XXL: 2-3x, identical outputs None for lossless methods Medium FP8 quantization Weight memory and bandwidth vLLM docs: 2x less memory, up to 1.6x throughput Low, test per task Low to medium 4-bit quantization (GPTQ, AWQ, NVFP4) Weight memory GPTQ paper, 2022: about 3.25x on A100; NVIDIA, 2025: NVFP4 about 3.5x less memory than FP16 Medium, test per task Medium FlashAttention-3 Attention compute and memory traffic FlashAttention-3 paper, 2024: 1.5-2.0x on H100 None (exact attention) Low if the engine supports it Chunked prefill Long prompts stalling decode Mechanism only, no general figure None Low
Scaling Out: Parallelism and Disaggregated Serving
Once one GPU is not enough, there are two directions to go. You can add replicas of the same server, or you can split one model across several GPUs.
Tensor, Pipeline and Expert Parallelism vs More Replicas
At first, replicas are the simplest way to raise throughput, since each copy serves its own share of traffic. However, they only work when the model fits on a single GPU or node.
Tensor parallelism splits each layer’s matrices across GPUs, and pipeline parallelism places different layers on different GPUs. Both let a model run when it does not fit on one device. Both also add communication between GPUs on every step, so interconnect speed becomes part of the latency budget.
Mixture-of-experts models add expert parallelism, which places different experts on different GPUs. Kanerika’s guide to MoE architecture explains how that expert routing works.
Disaggregated Prefill and Decode: When It Pays Off
Since prefill and decode stress different resources, some systems run them on separate GPU pools. The DistServe paper at OSDI 2024 did this to maximize goodput. It reported 7.4x more requests or a 12.6x tighter latency target than prior systems, with over 90% of requests within their limits.
Even so, production support is less settled. The vLLM disaggregated prefill docs mark the feature as experimental and state plainly that it does not improve throughput. Instead, its purpose there is to tune TTFT and inter-token latency separately and to control the tail.
NVIDIA Dynamo builds the pattern into an open-source serving layer over vLLM, SGLang or TensorRT-LLM backends, with KV-aware routing as well. Treat disaggregation as an option to benchmark at scale, not a default.
Application-Level Optimization: The Cheapest Wins
Some of the cheapest savings, though, need no GPU work at all. They come from sending fewer tokens, reusing work already done and matching each request to the smallest model that can handle it.
Trim Prompts and Cap Output Length
Every input token adds to prefill, while every output token adds a decode step. Long system prompts, duplicated instructions and unbounded retrieval context all inflate TTFT and cost. Kanerika’s piece on context engineering vs prompt engineering shows how to keep context tight, and a well-built RAG pipeline retrieves fewer, better passages.
In addition, output caps matter just as much. A max-token limit and a request for concise answers shorten the decode phase directly.
Prompt Caching on Hosted APIs
Hosted APIs also apply prefix caching on their side and pass part of the saving on. As of November 2026, OpenAI’s prompt caching guide says caching is on by default for supported models. Cached input tokens are discounted by up to 95%. The minimum cacheable prompt is 1,024 tokens for GPT-5.6 and later.
Anthropic’s prompt caching is priced differently. Cache writes cost 1.25x the base input price for a five-minute lifetime or 2x for one hour. Cache reads cost 0.1x on most models, with lower read rates on some newer ones.
In both cases, the pricing rewards the same habit, which is putting stable content at the start of the prompt and variable content at the end. Semantic response caching, in contrast, is a different technique altogether. It returns a stored answer when a new question looks similar to an old one. As a result, it saves the whole call, but it can also serve a stale or wrong answer.
Route Easy Requests to Smaller Models
Model cascading sends each request to a small, cheap model first. Then it escalates to a larger one only when confidence is low or a check fails. The thresholds are a cost and latency trade-off to tune on real traffic. A loose threshold lets weak answers through, while a strict one escalates nearly everything.
An LLM gateway is a natural place to run this logic, along with rate limits and spend tracking. Sometimes the cheapest path is to run no inference stack at all, because a hosted assistant such as a custom GPT already covers the need.
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Can LLM Gateways Make Enterprise AI Safer?
Kanerika’s experts explain the LLM gateway as middleware, covering shadow AI, unmonitored API spending and why tokens, not requests, are the unit to govern.
Agent Workloads Multiply Every Inefficiency
Agents call a model many times per task, and each step can resend a growing context. A wasteful prompt costs once in a chatbot, but it costs again on every step of an agent loop.
For this reason, prefix caching, tight context and per-step routing pay off most here. Capping the number of steps also caps the bill.
LLM Serving Frameworks in 2026: Where These Techniques Live
Serving engines package the techniques above, so the engine you run decides which ones you can switch on. The table reflects the options as of November 2026, and Kanerika’s roundup of vLLM alternatives goes deeper on engine selection.
Framework What it is Built-in techniques Best fit vLLM Open-source serving engine PagedAttention, continuous batching, prefix caching, speculative decoding, FP8 and 4-bit quantization General-purpose self-hosting on GPUs SGLang Open-source engine with a frontend language RadixAttention cache reuse, fast structured output decoding Heavy prompt reuse and structured outputs TensorRT-LLM NVIDIA’s inference library Optimized kernels and low-precision formats for NVIDIA GPUs Teams standardized on NVIDIA hardware NVIDIA Dynamo Distributed serving layer over vLLM, SGLang or TensorRT-LLM Disaggregated prefill and decode, KV-aware routing, SLA-driven autoscaling Multi-node, datacenter-scale serving llama.cpp and Ollama Local inference runtimes Quantized models on CPUs and consumer GPUs Local, edge and developer machines Hugging Face TGI Hugging Face serving engine, now in maintenance mode Continuous batching Existing deployments only
Hugging Face’s Text Generation Inference repository states that TGI is now in maintenance mode. For new work, Hugging Face recommends vLLM, SGLang, llama.cpp or MLX going forward. For local and developer setups, Kanerika’s vLLM vs Ollama comparison covers the trade-offs.
Which Optimization Should You Try First? A Decision Framework
The right first step depends on the symptom, not on which technique is in fashion. Match what the metrics show to a likely bottleneck, then run one experiment against a defined validation test.
Match the Symptom to the First Experiment
The table maps six common symptoms to a first move. Change one variable at a time, so that any result can be traced to its cause.
Symptom Likely bottleneck Try first Validate with Slow first token (high TTFT) Long prompts, queueing or prefill compute Prompt trimming, prompt and prefix caching, chunked prefill p95 TTFT on real prompt lengths Slow token generation Memory-bound decode Quantization (FP8 first) or speculative decoding TPOT plus task quality scores Out-of-memory errors or low concurrency KV-cache memory Paged KV cache, KV-cache quantization, a shorter maximum context Concurrent requests served at the latency target High spend with idle GPUs Poor batching or an oversized model Continuous batching and routing to smaller models Goodput and cost per successful task Long p99 tail Bursts or long prompts blocking decode Chunked prefill, admission control, more replicas p99 latency under burst load Repeated long system prompts Prefill wasted on identical context Prefix caching (self-hosted) or prompt caching (hosted API) Cache hit rate and TTFT
After each experiment, two rules keep this honest. Keep a change only if quality and p95 latency hold, and roll it back the moment either one slips.
Five Stages of Optimization Maturity
Optimization work can be staged in five steps, each one building on the last.
Baseline. Metrics, traces and a test set exist for every production model.Controlled experiments. One change at a time, measured against the baseline.Production optimization. Proven changes ship with canaries and a rollback path.Governed adaptation. Model, runtime and prompt versions are tracked and reviewed together.Continuous economics. Cost per successful task is reported next to quality and guides routing decisions.After all, an experiment without a baseline proves nothing, and a production change without versioning cannot be audited later.
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Start Your AI Assessment → How to Benchmark LLM Inference Before and After
A benchmark is only useful when it resembles production. Synthetic prompts of fixed length flatter every technique, so build the test set from real traffic.
A Repeatable Benchmarking Process
The same eight steps apply whichever engine or model you test.
Sample real prompts and keep the true spread of input and output lengths. Fix the environment and record the model version, engine version, GPU type and settings. Warm the server, then measure with caches both warm and cold. Ramp concurrency in steps until the latency targets break. Record TTFT, TPOT, p95 and p99 latency, goodput and error rate at each step. Score output quality on a fixed evaluation set. Apply one change and repeat the identical run. Compare cost per successful task, and keep the change only if quality holds. Quality scoring at scale can use one model to grade another. Kanerika’s guide to LLM as a judge covers how to make those scores trustworthy, and its LLM evaluation framework sets out the wider test design.
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Get the Checklist → Benchmark Mistakes That Produce Misleading Results
The common traps are easy to spot once you know them. Short fixed prompts hide prefill costs, and warm-cache-only runs overstate caching gains. Meanwhile, averages hide the tail, and runs that drop failed or truncated requests make a broken setting look fast.
Undisclosed versions are the last trap, and the easiest to avoid. Both vLLM and SGLang released about every two weeks from July to November 2026, so log the engine version and GPU type with every result. Also watch for factual drift, since a quantized model can stay fluent while getting facts wrong, a risk covered in Kanerika’s piece on LLM hallucination .
The Cost Side: From Cost per Token to Cost per Successful Task
Cost per token is easy to quote but easy to misread. A cheap token that produces a wrong answer, triggers a retry or needs human cleanup is not cheap at all.
Calculate Cost per Successful Task
In short, cost per successful task divides total serving spend by the number of tasks that met the success bar. Spend includes GPU hours, idle ones too, plus API charges, retries and fallbacks to larger models. It also counts every call in a multi-step agent run.
Here, success means the output passed the quality check you defined, rather than merely that a response came back. Here is a hypothetical example. Suppose a deployment spends $10,000 a month on 50,000 tasks, and 40,000 of them pass the check. In that case, the real cost is $0.25 per successful task rather than $0.20 per task.
Hosted API vs Self-Hosted Inference
There is no universal break-even point between a hosted API and self-hosting. Self-hosting makes sense when GPUs stay busy, because an idle GPU still costs money while a hosted API charges only per token.
Hosted APIs therefore suit spiky or low-volume traffic. By contrast, steady high-volume workloads, or those with strict data residency needs, lean toward self-hosting. Kanerika’s AI workload guide covers the FinOps side of that choice, and its AI development cost breakdown puts serving in the context of total spend.
Teams serving models on Databricks can also apply the controls in Kanerika’s Databricks best practices guide and its Databricks cost optimization playbook.
Governance, Security and Reliability Guardrails
Even when they only target speed, these changes are still production changes. Each one touches data handling, output quality or both, so each one needs the same controls as any other release.
Keep Caches and Logs Inside Trust Boundaries
Shared prefix caches can expose information across tenants if they are not scoped. For that reason, scope caches per tenant or per security domain. Traces and benchmark logs can also hold real prompts, so mask sensitive fields before storing them.
Ship Performance Changes as Controlled Releases
Version the model, the runtime, the quantized artifact and the prompt together, so any result can be traced to its exact setup. Then roll out through a canary, watch quality and latency, and keep a one-step rollback. Kanerika’s guide to LLMOps observability covers the monitoring side.
The NIST AI Risk Management Framework , released on January 26, 2023 for voluntary use, gives these controls a shared vocabulary. Teams formalizing them can draw on Kanerika’s AI governance services .
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Watch the Webinar → How Kanerika Approaches LLM Inference Optimization
Kanerika’s AI consulting services cover the full AI lifecycle, from strategy and build to governance and continuous optimization. That work runs on the platforms clients already use, including Microsoft Fabric, Azure AI, Databricks, Snowflake and AWS.
On the model side, Kanerika’s LLM development services include fine-tuning, which the service page describes as a way to cut inference costs over time. The same page lists deployment on the client’s own infrastructure, the setup that keeps prompts, caches and logs inside the client’s boundary.
An LLM Workload Kanerika Took to Production
Kanerika has not published a case study on inference optimization itself. The closest example is an LLM-driven ticket response system built for a B2B SaaS company that serves SMB clients in over 40 countries. It reached 80% auto-response of tickets, a 50% decrease in ticket resolution time and a 70% reduction in staffing cost.
Those are business results of an LLM workload in production rather than latency or cost-per-token figures. Still, they are the kind of task-level business outcome that serving costs should be weighed against.
Three practitioner pitfalls also apply on any platform. Quantizing before measuring makes a gain impossible to prove, and benchmarking on toy prompts hides prefill costs. Sharing caches across tenants, finally, creates a data exposure risk.
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Explore AI Consulting Start With One Measurable Bottleneck
LLM inference optimization works best as a loop, not a one-off tuning sprint. First, measure TTFT, TPOT, goodput and cost per successful task. Then find the bottleneck those numbers point to, apply its matching fix and run the same benchmark again.
Teams that want a second view on their serving costs can talk to Kanerika’s AI team .
Whatever the stack, the first optimization to try is the one your measurements point to. The one to keep is the one that leaves quality and p95 latency intact.
Frequently Asked Questions
What is LLM inference optimization?
LLM inference optimization is the work of serving an already trained language model faster and cheaper without lowering output quality. It covers model changes such as quantization, serving changes such as continuous batching and KV-cache paging, and application changes such as shorter prompts, prompt caching and routing simple requests to smaller models.
What is the difference between prefill and decode?
Prefill is the phase where the model reads the whole prompt and processes all input tokens in parallel, which is compute-heavy and sets the time to first token. Decode is the phase where the model writes output tokens one at a time. Decode is memory-bound, so it sets the time per output token.
How can you reduce LLM inference latency?
Start by finding which latency is slow. A high time to first token calls for shorter prompts, prompt or prefix caching and chunked prefill. Slow streaming calls for quantization or speculative decoding. Long tail latency calls for admission control, chunked prefill or more replicas. Change one setting at a time and re-measure.
Does quantization reduce LLM accuracy?
It can, and the size of the loss depends on the method, the bit width and the task. FP8 is documented by vLLM as having minimal accuracy impact, and published 4-bit methods such as GPTQ report negligible loss in their tests. Always score the quantized model on your own evaluation set before shipping it.
What is continuous batching in LLM inference?
Continuous batching schedules work at every generation step instead of waiting for a whole batch to finish. Finished requests leave and new ones join immediately, so the GPU stays busy. It was introduced as iteration-level scheduling in the Orca paper and, as of November 2026, is built into serving engines such as vLLM.
How does KV caching speed up LLM inference?
The KV cache stores the keys and values of tokens the model has already processed, so it does not recompute them for each new token. That saves compute during decode. The trade-off is memory, since the cache grows with batch size and sequence length, which is why paging and cache quantization matter.
When does speculative decoding help?
Speculative decoding helps when decode speed is the bottleneck and a small draft model, or a method such as n-gram matching, predicts the large model’s tokens well. The large model verifies the drafts in one pass. The speedup depends on the acceptance rate, so test it on your own traffic first.
Should you self-host LLM inference or use a hosted API?
It depends on utilization and data requirements. Hosted APIs charge per token and suit spiky or low-volume traffic. Self-hosting can cost less when GPUs stay busy, and it keeps prompts inside your own environment. There is no universal break-even point, so compare cost per successful task for your real workload.
How do you measure the ROI of LLM inference optimization?
Measure cost per successful task before and after each change. Divide total serving spend, including idle GPU time, retries and fallbacks, by the number of tasks that passed your quality check. A change pays off when that figure falls while quality and p95 latency stay within their targets.
