Top Challenges When Deploying GPUs for Inference and How to Solve Them

So, you’ve finally got GPUs in production. Congrats, that’s a big step.

But here’s the truth: the first few weeks usually feel rough. Utilization sits at 30%, latency spikes at random, and the cost graphs look like bad news. You start to wonder if the hype was oversold.

It’s not the hardware. It’s how we use it.

 Serving models on GPUs have its own quirks, batch sizing, memory limits, driver mismatches, even tokenization overhead on CPUs. Most teams learn the hard way. You don’t have to.

Here’s what typically goes wrong (and how to fix it) before the CFO or your users start yelling.

1. Low GPU Utilization but High Latency

This is the classic “our GPU’s asleep, but users are still waiting” problem. 
It happens because most teams treat inferences like training single batch, single stream and never actually feed the card enough work.

Why it happens

• Batch size is tiny, or dynamic batching isn’t configured.

• Only one model instance runs per GPU, so there’s no overlap between copies and compute.

• Tokenization and I/O are stuck on an overworked CPU core.

How to fix it

Turn on dynamic batching with a reasonable queue delay, just a few milliseconds can double throughput without hurting latency. 
Add multiple model instances (two to four per GPU usually hits the sweet spot) to overlap transfers and execution. 
And please, give tokenization some CPU love. It often takes more time than inference itself.

# config.pbtxt 
instance_group { 
kind: KIND_GPU 
count: 2 
} 
dynamic_batching { 
preferred_batch_size: [4, 8, 16] 
max_queue_delay_microseconds: 5000 
} 

Keep an eye on queue time and GPU utilization together. If the queue’s growing while the GPU is idle, something’s off with batching or instance counts.

2. Batching vs. Tail Latency

Batching is magic until it isn’t.

You increase throughput, sure, but if you mix all traffic in one queue, your realtime users will start complaining fast.

How to balance it

Split your traffic.

Run a “fast lane” deployment for interactive requests smaller batches, more instances.

Then have a “bulk lane” for background jobs that can wait an extra 50100ms. 
Autoscale based on queue depth or tokens in flight, not just GPU percentage. It’s a more reliable signal.

3. GPU Sharing: MIG, TimeSlicing, or MPS?

Most orgs overbuy GPUs. You don’t need a full A100 to serve a small model, you just need to share it smartly.

Suggested read: Which Is Better: GPU Time-Slicing Or Passthrough?

Quick rundown

• MIG (MultiInstance GPU) gives hard isolation: predictable performance, less noise, fewer tenants per card.

• Timeslicing packs more pods on one GPU but adds some jitter when neighbors get busy.

• MPS (MultiProcess Service) helps concurrent kernels share better within one slice.

When to use what

• If your workloads have tight SLOs say, lowlatency APIs use MIG.

• If it’s internal tools, testing, or bursty traffic, timeslicing is fine.

• You can mix them too: MIG for production, timeslicing for dev.

And on Kubernetes, install the NVIDIA GPU Operator, label nodes by GPU type or MIG profile, and request those resources directly in your pod spec. Saves a ton of guesswork.

4. LLM Memory Pressure (a.k.a. The KV Cache Monster)

Every LLM team hits this wall eventually.

Your service works fine with short prompts, but as users start sending 4K or 8K tokens, VRAM usage explodes and your model falls over.

Why it happens

The KV cache the memory where past tokens live grows with context length and concurrent users. It eats VRAM fast.

What to do

• Quantize or compress the KV cache (FP16 → INT8 or FP8 if your model supports it).

• Use paged attention or a sliding window to free memory for older tokens.

• Cap concurrent sessions and budget VRAM per user.

• Scale out horizontally when you hit memory limits instead of forcing multiGPU sharding too early.

A good rule: know your bytes per token. Do the math before rolling out, not after a crash.

5. “It Worked Yesterday” Driver and CUDA Drift

This one’s sneaky. Performance tanks out of nowhere, or a container refuses to start. The culprit? A driver or CUDA mismatch.

What usually goes wrong

• Container ships with a newer CUDA runtime than the host driver supports.

• Triton or TensorRT compiled against a different toolkit.

• Cloud image updates quietly change the kernel or driver.

How to prevent it

• Pin tested versions of the driver, CUDA, and runtime in your repo.

• Build on official vendor base images.

• Add a startup probe that verifies driver compatibility and fails early.

• Treat node images like app releases: document, version, and stagerollout them.

These tiny steps save hours of debugging “why is throughput half of last week?”

6. You Can’t Fix What You Can’t See

Most “GPU problems” aren’t GPU problems. They’re thermal throttling, bad batching, or queueing. But you’ll never know without metrics.

What to track

• GPU health: utilization, memory, temperature, power, throttling.

• Serving metrics: request rate, queue delay, batch size, perroute latency.

• App layer: tokenizer time, tokens/sec, error rates.

Tooling that works

• DCGM exporter for lowlevel GPU stats.

• Prometheus for scraping metrics.

• Grafana for dashboards.

• Alerts on thermal throttling, queue delays, P95 spikes, or OOMs.

Example alert idea:

Queue time > 10ms and GPU utilization < 30% for 5 minutes 
→ probably batching misconfig. 

Get those basics right, and half your “GPU tuning” becomes datadriven instead of gutdriven.

7. Cost Creep (Paying for Idle FLOPs)

Your bill won’t lie. Idle GPUs are expensive, and faster hardware doesn’t automatically mean cheaper inference.

Easy wins

• Run models in FP16 or INT8 sometimes FP8 if your stack supports it.

• Distill or quantize large models for common use cases.

• Pick the right card: A10s and older PCIe GPUs still crush small models for a fraction of the cost.

• Autoscale based on queue depth or requests in flight, not raw utilization.

• Cache frequent responses at the edge when possible.

Rule of thumb

Optimize throughput per dollar, not just latency per request. 
Raw speed is cool; cost efficiency keeps you alive.

8. The Hidden CPU and I/O Bottlenecks

You’ve probably seen it: GPU sits idle; CPU pegged at 100%. That’s tokenization, decompression, or I/O blocking.

How to spot and fix it

• Colocate tokenization with the model server.

• Give it real CPU cores, not shared scraps.

• Keep a tokenizer and model in the same container or node.

• Use persistent connections and compress payloads.

• Cache common requests it’s boring but effective.

Most “slow GPUs” are just waiting for data that should’ve arrived a second ago.

9. Rollouts, Versioning, and A/B Safety

Models aren’t static anymore. You’ll ship updates weekly maybe daily. Treat them like software, not artifacts frozen in time.

Keep it sane

• Version both the model weights and serving config.

• Shadow deploy before switching traffic.

• Canary by percentage; compare latency, cost, and quality before full rollout.

• Always have a rollback plan that clears pods and resets endpoints fast.

• Log why a model was released saves pain later.

A broken deploy at 2 AM hurts less when rollback is one command.

10. A Simple, Reliable Stack That Works

You don’t need a massive MLOps setup to serve models, right. 
Start small, add what you need, and grow from there.

Here’s a setup that just works:

• Serving: Triton or vLLM (with TensorRT backend if needed).

• GPU Control: NVIDIA GPU Operator in Kubernetes.

• Metrics: Prometheus + DCGM exporter.

• Dashboards: Grafana.

• Routing: Two paths fast lane for realtime, bulk lane for heavy jobs.

• Autoscaling: Driven by queue length and tokens per second.

Example Kubernetes snippet:

resources: 
requests: 
nvidia.com/gpu: 1 
cpu: "4" 
memory: "16Gi" 
limits: 
nvidia.com/gpu: 1 
nodeSelector: 
nvidia.com/gpu.product: "A100PCIE40GB" 

If you use MIG, request the specific slice like nvidia.com/mig1g.10gb: 1 and label nodes accordingly. 
Keep it explicit guessing costs hours later.

FAQs

Do I need NVLink for inference? 
Nope. Unless you’re splitting one model across multiple GPUs, PCIe is fine.

What’s a healthy utilization target? 
Anything above 70% with stable latency. If it’s lower, tune batch sizes or add concurrent instances.

How do I pick batch sizes? 
Benchmark with a tool like Model Analyzer. Start with 416 and adjust until latency stops improving.

How do I stop LLM costs from exploding? 
Quantize, cap context lengths, and offer “fast” vs. “full” tiers. Measure cost per 1k tokens and track it like an SLO.

A Quick Checklist Before You Go

• Turn on dynamic batching and multiple instances per GPU

• Separate fast and bulk inference routes

• Install DCGM exporter + Prometheus + Grafana

• Alert on throttling, OOMs, queue time, and P95

• Pin driver/CUDA versions and validate on startup

• Choose MIG for SLOcritical workloads

• Quantize models and cache frequent prompts

Final Thought

Deploying GPUs isn’t about buying power it’s about using it right. 
Once you understand how batching, memory, and observability tie together, you’ll stop chasing “why is it slow?” and start focusing on “how fast can we scale?”

If you’d rather skip the yak shaving, AceCloud can help you spin up a clean GPU environment Triton, vLLM, monitoring, the works tuned for your exact model and SLA. 
Tell us what you’re running and what latency you need, and we’ll help you get there without guesswork.