TUNE THE WHOLE STACK: ENGINE, GPU, RUNTIME, INFRASTRUCTURE
AI and GPU
workload optimization
Akamas continuously analyzes your AI and GPU workloads. It tunes the serving engine, runtime, GPU, and the underlying infrastructure together. The result is AI applications that run within their latency targets at the lowest sustainable cost.
+57.5%
DECODE THROUGHPUT
+54.1%
PREFILL THROUGHPUT
−15.8%
GPU MEMORY USED
THE CHALLENGE
Three pain points every inference team knows
Inference engines and Kubernetes serving were tuned in isolation. Production AI workloads aren’t, and the cost shows up everywhere at once.
Idle GPUs and the GPU optimization gap
GPUs run well below capacity, and autoscaling can't see GPU memory or KV-cache locality. It scales on CPU and memory, so the GPU headroom sitting idle between requests never gets reclaimed. That's the capacity most teams leave on the table.
SLO Drift Under Load
Vanilla Kubernetes load-balances by request count, not by GPU memory or prefix locality. Under burst traffic, TTFT slips and the usual fix is over-provisioning. KV-cache-aware routing roughly doubles throughput versus cache-blind round-robin, with sharply lower TTFT under load.
Single-Layer Tuning
Serving engine, runtime, GPU, and autoscaler, each tuned once and in isolation. Stacking techniques across layers, FP8 quantization, FlashAttention-3, continuous batching, speculative decoding, compounds savings no single-layer change can reach. Tuned separately, that saving never lands.
HOW AKAMAS WORKS
Akamas optimizes AI cost and GPU efficiency across the full stack.
Each layer of the inference stack has its own owner, its own dashboard, its own dial. Leaving them uncoordinated shows up everywhere: TTFT under burst, dollars per token, autoscaling that can’t see queue depth. Akamas continuously closes those gaps, with a single optimization view across the full stack.
DIAGNOSE
Find the inefficiency
before the bill or the pager does
Akamas reads your live inference traffic across three layers:
- The engine, through vLLM metrics (batching, KV-cache, decoding).
- The GPU, through NVIDIA’s Data Center GPU Manager (DCGM).
- The infrastructure, across Kubernetes, high-performance computing (HPC) clusters, and bare-metal GPU.
It ranks issues by what they cost you, not just resource flags. Each finding ties an observable behavior to its cost in latency, throughput, or GPU spend.
A KV-cache is running hot at peak. Prefill traffic landing on pods that should be decoding. A batching parameter set for benchmark numbers, not production load. The pattern bleeding the budget is rarely in the obvious place. Akamas surfaces it where the data already lives.
ANALYZE
See how every layer
pushes on every other
Inference performance is a set of interlocked dials. Batching depth shifts throughput, and TTFT for late arrivals. Quantization shifts cost and accuracy. GPU memory headroom shifts what fits per pod, and what KV-cache prefix reuse can do for you. Akamas plots these interactions against your live workload, so you see:
- Where CPU and memory autoscaling signals disagree with the GPU’s actual load.
- Allocated capacity versus real utilization across compute, high-bandwidth memory (HBM), and KV-cache.
- How the serving-engine queue behaves at peak.
The decisions that were guesses become decisions backed by the trace.
GPU allocation tools see the GPU. Engine tools see the engine. Akamas is the view that holds both in the same plot, against the workload that’s actually running.
OPTIMIZE
Recommendations that
tune the whole stack, not just one layer
Akamas recommends specific changes across every layer of the inference stack: serving-engine parameters, GPU sizing and sharing, Kubernetes resources, autoscaling policies. Each recommendation shows expected impact on TTFT, throughput, and cost per token before anything is applied. The data that drove each recommendation travels with it.
Engineering teams keep the call on what ships. The repeatable workflow replaces one-off tuning runs. As workload drifts, model versions update, or traffic patterns shift, the recommendations adjust. The optimization isn’t a project. It’s how the system stays optimized.
Tuning the engine and tuning the GPU each give you part of the answer. Tuning them jointly, against the same workload and SLO, gives you a different curve.
Inference workloads change faster than configurations do. Akamas keeps the configuration moving with them.
How Akamas Compares
One view across the whole stack
GPU allocation tools optimize the hardware. Engine tuning optimizes the model server. Akamas tunes both, plus the runtime and infrastructure, against a single latency target.
| GPU allocation / rightsizing tools | Manual serving-engine tuning | Akamas | |
|---|---|---|---|
| What it sees | GPU utilization, bin-packing | vLLM engine parameters | Engine, GPU, runtime, and infrastructure together |
| Tunes serving engine (batching, KV-cache) | No | Yes, by hand | Yes |
| Tunes GPU and infrastructure | Partial | No | Yes |
| Tunes all layers jointly against one SLO | No | No | Yes |
| Ties each change to cost per token and latency | Rarely | No | Yes |
| Optimizes for your real workload, not just hardware and model | No | Partial | Yes |
POWERED BY CAPABILITY
Two capabilities, one optimization stack
Two ways to optimize the same workload. Continuous optimization recommends changes from the observability data you already collect on your live production workload. Pre-production optimization searches the full configuration space in a test environment for the deepest tuning before you ship. Both keep every change reviewable before it goes live.
Continuous Optimization
Recommends inference configurations on the observability data you already collect (Datadog, Dynatrace, Prometheus). Low friction, always on, grounded in production behavior.
Pre-production Optimization
Searches the full configuration space in a test environment, with no constraint on which parameters it tunes. The deepest optimization, before you ship. Extends beyond Kubernetes to HPC clusters, bare-metal GPU environments, and traditional infrastructure.
BLOG
FREQUENTLY ASKED QUESTIONS
See for Yourself
Experience the benefits of Akamas autonomous optimization.
No overselling, no strings attached, no commitments.