You can have the fanciest GPU on the market, a screaming-fast NVMe array, and enough CPU cores to heat a small town.
Then one Tuesday afternoon your model server starts returning 500s because “CUDA out of memory.”
Not because you ran out of compute. Because you ran out of VRAM at the worst possible moment.
After 2026, that failure mode won’t be a niche problem for AI labs. It’ll be a day-two operational reality for normal companies:
customer support copilots, real-time fraud scoring, creative pipelines, synthetic data, video models, and analytics that “accidentally” turned into deep learning.
VRAM is becoming the new disk capacity planning—except the consequences arrive in milliseconds, not weeks.
VRAM is the budget, not the garnish
VRAM used to be the thing gamers argued about on forums while everyone else shrugged and bought whatever IT approved.
In production AI and graphics, VRAM is now the hard ceiling that decides whether you ship the feature, what your latency looks like,
and whether your incident channel gets noisy at 2 a.m.
The core pattern is simple: compute has scaled faster than “usable memory per workload.” GPUs keep getting more FLOPS.
Meanwhile, models got larger, contexts got longer, batching got smarter, and everyone wants multiple concurrent tenants on the same accelerator.
VRAM is where those ambitions collide.
In SRE terms: VRAM is the new “resource you can’t burst.” CPU can spike. Disk can queue. Network can buffer.
VRAM is a fixed-size room. Once it’s full, you don’t slow down gracefully; you fail, you thrash, or you silently fall back to a slower path
that ruins the SLO and makes your cost model lie to you.
Why this accelerates after 2026
“After 2026” isn’t a magical date. It’s a prediction about when several trends stack hard enough that VRAM becomes the limiting factor for more teams than not.
Here’s what pushes it over the edge.
1) Context windows keep expanding, and KV cache is not free
The industry is moving from chatty, short prompts to long, stateful interactions: customer histories, codebases, policy documents, multi-step agent traces.
For transformer-style inference, long context means a large key/value (KV) cache.
That cache lives in VRAM because it’s on the critical path.
You can compress it, quantize it, page it, shard it—sure. But the center of gravity remains: longer context shifts cost from compute toward memory capacity and memory bandwidth.
2) Multi-modal models eat memory like it’s their job
Text-only already stretched VRAM. Add images, video, audio, and you don’t just add parameters.
You add larger activations, more intermediate states, more pre/post-processing buffers, and more opportunities for fragmentation.
The joke is that “video is just images over time.” The bill is that time multiplies your VRAM footprint.
3) Batching pressure moves from “nice-to-have” to “profit-or-loss”
Most AI serving stacks improve throughput by batching requests. Batching wants VRAM: more sequences concurrently, more KV cache, larger temporary buffers.
After 2026, more companies will run inference as a product, not a demo. That means sustained concurrency, not occasional usage.
4) Reliability expectations rise faster than memory headroom
Internal tools can occasionally fail. Customer-facing AI features can’t. If your VRAM is running at 92% steady-state, you do not have “8% headroom.”
You have an outage scheduled for whenever fragmentation spikes or one request arrives with a longer prompt than you tested.
5) You will share GPUs more aggressively
Dedicated GPUs per service are expensive and politically hard to justify when finance discovers utilization charts.
Expect more multi-tenant GPUs using containers, MIG partitioning, or scheduler-level sharing. Sharing increases variance.
Variance plus hard ceilings equals pager noise.
6) Memory bandwidth becomes the hidden governor
Even if you “have enough VRAM,” you may not have enough bandwidth.
Many inference workloads are memory-bandwidth-bound, not compute-bound.
As models grow, the arithmetic intensity doesn’t always save you.
The operational outcome: after 2026, “how many GPUs” becomes an incomplete question.
The real question is “how much VRAM per GPU, at what bandwidth, under what concurrency, with what fragmentation behavior.”
That’s not procurement trivia. That’s architecture.
Interesting facts and historical context (you can use in a meeting)
- Fact 1: Early consumer GPUs often shipped with “enough VRAM for textures,” because the dominant workload was raster graphics, not giant persistent state.
- Fact 2: The shift from fixed-function pipelines to programmable shaders made GPUs more general, but VRAM still mattered mostly for frame buffers and assets—until deep learning hijacked the hardware.
- Fact 3: CUDA (2007) made GPU compute mainstream; it also made GPU memory management an application concern rather than a driver curiosity.
- Fact 4: Mixed precision (FP16/BF16) didn’t just speed up compute—it reduced parameter and activation footprints, effectively “manufacturing VRAM” through representation.
- Fact 5: The introduction of HBM (High Bandwidth Memory) was a bandwidth story first, but it also shaped capacity tiers: not all VRAM grows equally across product lines.
- Fact 6: Unified Virtual Memory and managed memory promised simplicity, but many production teams learned the hard way that “paging to host” can turn a 50 ms request into a multi-second disaster.
- Fact 7: Attention mechanisms made model quality jump, but they also made memory behavior more complex: KV caches, dynamic shapes, and longer contexts become first-class capacity planning variables.
- Fact 8: Multi-instance GPU (MIG) and similar partitioning features are a response to economics—yet they turn VRAM into a schedulable resource with hard boundaries and new failure modes.
What actually fills VRAM in modern workloads
People talk about “model size” as if VRAM is just parameters. In production, VRAM is a messy apartment:
the couch is the model weights, but the hallway is full of boxes you didn’t remember ordering.
1) Model weights (obvious, but still misunderstood)
Weights live in VRAM for speed. Quantization can shrink them. Sharding can spread them.
But every “clever trick” usually increases overhead elsewhere: metadata, packing, kernel constraints, or additional buffers.
2) KV cache (the quiet killer)
For autoregressive generation, you keep past keys/values so you don’t recompute attention from scratch.
KV cache cost grows with:
- context length
- batch size / number of concurrent sequences
- hidden dimension and number of layers
- precision (FP16/BF16/FP8/int8)
This is why a model that “fits” can still fall over under load. You sized for weights, then traffic sized your KV cache for you.
3) Activations and temporary buffers
Training folks know activations dominate memory. In inference, you still get temporaries: attention workspaces,
fused kernel buffers, tokenization and embedding staging, and per-request overhead.
Some frameworks allocate these opportunistically and keep them around to avoid allocator churn.
That’s good for latency until it becomes bad for peak usage.
4) Memory fragmentation and allocator behavior
Fragmentation is the gap between “free memory” and “free memory you can actually use.”
You can have gigabytes free and still fail to allocate a contiguous block needed by a kernel.
This is one reason VRAM incidents look irrational: the dashboard says 20% free, but the job dies anyway.
5) Multi-tenancy overhead
If multiple processes share a GPU, each may load its own copy of weights.
If you use MIG or strict isolation, you get predictable boundaries but less flexibility.
If you do not use isolation, one tenant’s spike becomes everyone’s incident.
6) “Support services” on the GPU
Some stacks run pre/post-processing on the GPU: image resizing, decoding, feature extraction, safety filters.
That’s great for throughput. It also means your VRAM budget is now shared with code you don’t think of as “the model.”
Capacity vs bandwidth vs latency: the three ways VRAM ruins your day
VRAM matters in three distinct ways. Mixing them up leads to bad purchases and worse incidents.
VRAM capacity: can you fit the working set?
If you can’t fit weights + KV cache + overhead, you crash, or you page, or you spill to CPU, or you silently switch to a smaller batch size.
Capacity problems are easy to see once you accept that “fits on my dev box” is not a capacity plan.
VRAM bandwidth: can you feed the compute?
Many inference workloads are limited by how fast you can move data from VRAM to SMs and back.
This is why two GPUs with similar VRAM sizes can behave wildly differently under the same model.
Bandwidth issues often show up as “GPU utilization looks low, but latency is bad.”
VRAM latency and access patterns: can you avoid stalls?
Not all memory access is equal. Kernel fusion, tensor layouts, and caching behavior matter.
Some optimizations reduce compute but increase memory traffic, and the GPU politely punishes you.
Here’s the uncomfortable truth: after 2026, procurement decisions that treat VRAM as a checkbox (“80 GB good, 40 GB bad”)
will lose to teams that treat VRAM as a budget with line items and risk reserves.
The failure modes you will see in production
You don’t need more theory. You need to recognize the smell of VRAM trouble quickly.
1) Sudden OOMs after a deploy, even though model and GPU didn’t change
Often caused by a change in default max context length, batch size policy, or a new feature that keeps more state per request.
Another classic: a framework update that changes allocator behavior.
2) Latency spikes under load, with “free VRAM” reported
Usually fragmentation, paging, or a fallback path (smaller kernels, less fusion, CPU offload).
Watch for request-size variance: one long prompt can poison the cache behavior for everyone.
3) Throughput plateaus far below theoretical GPU capability
Often bandwidth-bound or blocked by synchronization and memory copies.
If you are saturating memory throughput but not SMs, your bottleneck is VRAM bandwidth, not “need more GPUs.”
4) Multi-tenant “noisy neighbor” incidents
One service loads a second model, another bumps batch size, a third starts doing GPU-side preprocessing.
The shared GPU now behaves like a shared database: everyone denies doing anything.
5) “It works, but only after we reboot the pod”
That’s fragmentation or a memory leak, sometimes in the framework, sometimes in your own code.
Rebooting frees VRAM and resets allocator state. It is not a fix; it’s an amnesty program.
One short joke, because we’ve earned it: VRAM is like office parking—there are always “plenty of spaces” until you try to park.
Fast diagnosis playbook
When an inference service is slow or failing, you need a deterministic sequence. Not vibes.
First: confirm whether it’s capacity, fragmentation, or bandwidth
- Check VRAM used vs total (per GPU, per process). If you’re near the ceiling, treat it as capacity risk.
- Check OOM / allocator logs. If OOM happens with free memory reported, suspect fragmentation or large contiguous allocations.
- Check memory bandwidth utilization. If bandwidth is pegged and SM utilization is moderate, you’re bandwidth-bound.
Second: isolate whether the problem is per-request variance or steady-state
- Compare p50 vs p99 latency. If p99 explodes while p50 is fine, you likely have request-size variance triggering KV growth or paging.
- Inspect prompt/token distribution. If one team shipped “let’s include the whole customer history,” you have your culprit.
- Look at concurrency and batcher behavior. Batching that adapts to load can accidentally adapt into OOM.
Third: decide the emergency mitigation
- Cap max tokens / context length temporarily. It’s blunt, but it stops the bleeding.
- Reduce max batch size / concurrency on the GPU. Throughput drops; availability returns.
- Move a tenant off the GPU or enforce isolation (MIG or node pool split) if noisy neighbor is confirmed.
- Restart to defragment only as a last-resort mitigation—and file a follow-up to remove the need.
Paraphrased idea from John Allspaw: “Reliability comes from learning in the messy reality of production, not from pretending systems behave ideally.”
Practical tasks with commands: what to run, what it means, what you decide
These are the tasks I actually want on your runbook. Each one includes: a command, what the output means, and what decision you make.
Commands assume Linux with NVIDIA GPUs; adapt if you’re on a different stack.
Task 1: Snapshot VRAM usage per GPU and per process
cr0x@server:~$ nvidia-smi
Tue Jan 21 10:14:03 2026
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 555.42 Driver Version: 555.42 CUDA Version: 12.5 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA A100-SXM4-80GB On | 00000000:3B:00.0 Off | 0 |
| 35% 62C P0 285W / 400W | 74210MiB / 81920MiB | 71% Default |
+-------------------------------+----------------------+----------------------+
| Processes: |
| GPU PID Type Process name GPU Memory |
|=============================================================================|
| 0 23144 C python3 72112MiB |
| 0 24701 C /usr/bin/tritonserver 1820MiB |
+-----------------------------------------------------------------------------+
What it means: GPU 0 is at ~74 GB used; one python process owns most of it.
Decision: If this is near steady-state, you have no headroom. Cap tokens/batch or move workload to a larger VRAM tier before adding traffic.
Task 2: Capture utilization and memory pressure over time
cr0x@server:~$ nvidia-smi dmon -s pucm -d 1 -c 10
# gpu pwr gtemp mtemp sm mem enc dec mclk pclk
# Idx W C C % % % % MHz MHz
0 290 63 - 68 92 0 0 1215 1410
0 296 63 - 70 93 0 0 1215 1410
0 287 62 - 66 94 0 0 1215 1410
0 281 62 - 62 95 0 0 1215 1410
0 275 61 - 58 95 0 0 1215 1410
What it means: Memory utilization is consistently very high while SM is moderate.
Decision: You may be bandwidth-bound. Consider faster VRAM tier, kernel optimization, or reducing memory traffic (quantization, better batching, fused attention).
Task 3: Identify which GPU a container is actually using
cr0x@server:~$ docker exec llm-serving env | grep -E 'CUDA|NVIDIA|VISIBLE'
NVIDIA_VISIBLE_DEVICES=0
CUDA_VISIBLE_DEVICES=0
What it means: This container is pinned to GPU 0.
Decision: If GPU 0 is overloaded while GPU 1 is idle, fix scheduling/placement before buying hardware.
Task 4: Check per-process VRAM and compute usage in a compact view
cr0x@server:~$ nvidia-smi pmon -c 1
# gpu pid type sm mem enc dec command
# Idx # C/G % % % % name
0 23144 C 64 91 0 0 python3
0 24701 C 5 2 0 0 tritonserver
What it means: One process dominates both compute and memory.
Decision: If multi-tenancy was expected, your isolation is failing (or you accidentally loaded weights twice). Consider separate GPUs/MIG slices per service.
Task 5: Detect ECC or hardware-level memory issues (rare, but ugly)
cr0x@server:~$ nvidia-smi -q -d ECC | sed -n '1,120p'
==============NVSMI LOG==============
ECC Mode
Current : Enabled
Pending : Enabled
ECC Errors
Volatile
Single Bit
Device Memory : 0
Double Bit
Device Memory : 0
Aggregate
Single Bit
Device Memory : 2
Double Bit
Device Memory : 0
What it means: Some corrected errors occurred historically.
Decision: If counts climb quickly or you see double-bit errors, treat it as hardware risk; drain the node and get it checked.
Task 6: Confirm PCIe / NVLink topology (bandwidth and contention)
cr0x@server:~$ nvidia-smi topo -m
GPU0 GPU1 CPU Affinity NUMA Affinity
GPU0 X NV2 0-31 0
GPU1 NV2 X 0-31 0
What it means: GPUs are connected via NVLink (good for sharding/parallelism).
Decision: If you planned tensor parallel across GPUs, this supports it. If you see “PHB” or weak connectivity, re-think sharding or expect latency.
Task 7: Verify host RAM and swap (GPU paging often drags the host into it)
cr0x@server:~$ free -h
total used free shared buff/cache available
Mem: 503Gi 392Gi 21Gi 11Gi 90Gi 101Gi
Swap: 32Gi 18Gi 14Gi
What it means: Swap is actively used.
Decision: If GPU managed memory or CPU offload is in play, swap activity is a red flag; reduce memory pressure or disable/limit paging paths.
Task 8: Spot GPU-related OOM and Xid errors in kernel logs
cr0x@server:~$ sudo dmesg -T | grep -E "NVRM|Xid|Out of memory|oom" | tail -n 20
[Tue Jan 21 10:11:22 2026] NVRM: Xid (PCI:0000:3b:00): 13, pid=23144, Graphics Exception
[Tue Jan 21 10:11:23 2026] Out of memory: Killed process 23144 (python3) total-vm:14223312kB, anon-rss:9123456kB
What it means: A GPU error plus the host OOM killer: bad combination.
Decision: Treat as stability incident: cap workload, check for driver issues, and ensure the host has sufficient RAM if offloading is enabled.
Task 9: Confirm your process is not silently spilling tensors to CPU (PyTorch example)
cr0x@server:~$ python3 - <<'PY'
import torch
print("cuda available:", torch.cuda.is_available())
print("device:", torch.cuda.get_device_name(0))
print("mem allocated:", torch.cuda.memory_allocated()//(1024**2), "MiB")
print("mem reserved:", torch.cuda.memory_reserved()//(1024**2), "MiB")
PY
cuda available: True
device: NVIDIA A100-SXM4-80GB
mem allocated: 61520 MiB
mem reserved: 74240 MiB
What it means: Reserved > allocated implies allocator caching and potential fragmentation risk.
Decision: If reserved keeps climbing across requests, you may need allocator tuning, periodic worker recycling, or more consistent shapes.
Task 10: Check PyTorch allocator summary for fragmentation clues
cr0x@server:~$ python3 - <<'PY'
import torch
torch.cuda.init()
print(torch.cuda.memory_summary(device=0, abbreviated=True))
PY
|===========================================================================|
| PyTorch CUDA memory summary, device ID 0 |
|---------------------------------------------------------------------------|
| CUDA OOMs: 1 | cudaMalloc retries: 0 |
| Memory Allocated: 61.1 GiB | Memory Reserved: 72.5 GiB |
|---------------------------------------------------------------------------|
| Largest block: 512.0 MiB | Total reserved: 72.5 GiB |
|===========================================================================|
What it means: There was at least one OOM; largest contiguous block is 512 MiB.
Decision: If your next allocation needs >512 MiB contiguous, you can OOM despite “free” VRAM. Fix by reducing peak temp buffers, standardizing shapes, or restarting workers strategically.
Task 11: Validate model server metrics endpoint locally (latency vs memory correlation)
cr0x@server:~$ curl -s localhost:8000/metrics | grep -E 'gpu_memory|request_latency_seconds_bucket' | head
gpu_memory_used_bytes{gpu="0"} 7.775e+10
request_latency_seconds_bucket{le="0.5"} 8123
request_latency_seconds_bucket{le="1"} 9012
request_latency_seconds_bucket{le="2"} 9401
What it means: Memory used is high; latency distribution is visible.
Decision: If latency worsens as gpu_memory_used_bytes rises, you’re memory-pressure-bound; adjust batching/concurrency and token caps.
Task 12: Measure token distribution from logs (proxy for KV cache growth)
cr0x@server:~$ awk -F' ' '/tokens_in=/{for(i=1;i<=NF;i++) if($i ~ /^tokens_in=/) {split($i,a,"="); print a[2]}}' /var/log/llm-serving/access.log | sort -n | tail -n 10
4096
6144
8192
8192
12288
16384
16384
24576
32768
65536
What it means: You have extremely long inputs.
Decision: If you didn’t explicitly plan for this, enforce max input tokens, introduce summarization, or route long-context requests to a dedicated high-VRAM tier.
Task 13: Verify MIG configuration (is VRAM partitioned as you think?)
cr0x@server:~$ nvidia-smi -L
GPU 0: NVIDIA A100-SXM4-80GB (UUID: GPU-aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee)
MIG 1g.10gb Device 0: (UUID: MIG-aaaaaaaa-bbbb-cccc-dddd-000000000001)
MIG 1g.10gb Device 1: (UUID: MIG-aaaaaaaa-bbbb-cccc-dddd-000000000002)
MIG 2g.20gb Device 2: (UUID: MIG-aaaaaaaa-bbbb-cccc-dddd-000000000003)
What it means: VRAM is sliced into multiple smaller instances.
Decision: If your model barely fits in 20 GB, don’t put it in a 10 GB slice and hope. Either resize slices or change model/quantization.
Task 14: Check GPU clock throttling (memory-bound workloads still suffer from power/thermals)
cr0x@server:~$ nvidia-smi --query-gpu=clocks.current.memory,clocks.max.memory,temperature.gpu,power.draw,power.limit --format=csv
clocks.current.memory [MHz], clocks.max.memory [MHz], temperature.gpu, power.draw [W], power.limit [W]
1215, 1593, 63, 292.15, 400.00
What it means: Memory clock is below max.
Decision: If consistently low under load, investigate power/thermal limits or admin policies; you may be leaving bandwidth on the table.
Task 15: Confirm driver/CUDA stack consistency across nodes (variance causes “works on one node” bugs)
cr0x@server:~$ (nvidia-smi --query-gpu=driver_version --format=csv,noheader | head -n1; nvcc --version | tail -n 2) 2>/dev/null
555.42
Cuda compilation tools, release 12.5, V12.5.52
What it means: Driver and toolkit versions are visible.
Decision: If clusters are mixed, you can get different allocator/perf behavior and inconsistent VRAM headroom. Standardize, or at least schedule workloads by stack version.
Second short joke (and that’s it): When someone says “we’ll just add more batch,” a GPU quietly starts drafting its resignation letter.
Three corporate mini-stories (all true in spirit)
Mini-story 1: The incident caused by a wrong assumption
A mid-sized SaaS company rolled out an internal “case assistant” for support engineers. The model lived behind an API gateway.
The team did the usual load test: average prompt size, average response length, average concurrency. It looked fine.
They shipped and went home like responsible adults.
Two weeks later, the assistant became popular with the escalation team. Escalations meant long tickets.
People started pasting entire email threads, logs, and screenshots (OCR’d into text). The average didn’t change much,
but the tail got vicious. One request every few minutes hit a context length that was 10–20× larger than the original test.
The system didn’t just slow down. It fell over. The model server OOM’d, restarted, and reloaded weights.
During reload, the health checks failed. The gateway marked the instance unhealthy, shifted traffic, and caused a thundering herd on the remaining pods.
Within minutes, a “non-critical” internal tool had become a productivity outage.
The wrong assumption wasn’t “VRAM is too small.” It was “prompt distribution is stable.” It wasn’t.
The fix wasn’t just buying bigger GPUs either. They implemented:
- a hard cap on input tokens with a friendly error
- a separate long-context queue routed to a high-VRAM node pool
- an SLO that tracked tail token counts, not just tail latency
After that, they stopped arguing about whether the model “fits.” They started budgeting VRAM for tails and failure recovery.
That shift is what keeps your pager quiet.
Mini-story 2: The optimization that backfired
A fintech team ran fraud scoring with a GPU-accelerated model. They wanted to reduce p95 latency.
The engineer on-call (smart, well-meaning) enabled a more aggressive batching strategy in the model server.
The first graphs looked great: fewer kernel launches, higher throughput, slightly improved p50 latency.
Then the real traffic pattern arrived: bursty, multi-tenant, with a couple of clients sending large feature vectors.
The batcher eagerly packed requests together. Peak VRAM usage climbed. Not linearly—stepwise.
Every time a batch happened to include several large requests, the service flirted with the VRAM ceiling.
Soon they saw a new behavior: no hard OOM, but periodic latency cliffs.
The framework started doing additional allocations, the allocator couldn’t find big contiguous blocks,
and the system spent time in memory management instead of inference.
Their “optimization” had created a new p99 problem.
The rollback was immediate. The permanent fix was more nuanced:
- batch by shape buckets (or token buckets) instead of “whoever arrives”
- cap batch size by predicted KV growth, not just request count
- reserve VRAM headroom explicitly (yes, waste some) to avoid allocator death spirals
The lesson: throughput optimizations often spend VRAM like a credit card. The statement arrives later, with interest.
Mini-story 3: The boring but correct practice that saved the day
A media company ran GPU rendering and some light ML inference on the same fleet. They had a rule nobody loved:
every GPU node had a “VRAM budget file” in config management—documented expected memory usage per service,
and a required headroom percentage for worst-case jobs.
It was tedious. Engineers had to update the budget when they changed resolution, model version, max tokens, or enabled a new GPU-side filter.
Reviews included a question: “Show me the VRAM delta.” People rolled their eyes. People always roll their eyes at prevention.
Then a vendor library update changed memory behavior. On a subset of workloads, VRAM usage increased modestly,
but fragmentation got worse. The nodes didn’t OOM immediately; they started failing jobs after a few hours of churn.
The symptoms were classic: sporadic allocation failures, only at high concurrency, fixed by restart.
Because they had budgets and headroom policies, the blast radius was limited.
Schedulers refused to co-locate two high-VRAM services. Canary nodes caught the regression before the whole fleet updated.
The incident was a nuisance, not a crisis.
The boring practice: treat VRAM like a first-class capacity dimension, track it in config, and enforce headroom via scheduling rules.
It didn’t make anyone famous. It did keep revenue features alive.
Common mistakes: symptom → root cause → fix
This is the part where I tell you what to stop doing.
1) Symptom: “CUDA out of memory” only when traffic is high
Root cause: KV cache growth with concurrency; you sized for single-request fit.
Fix: Cap max tokens, cap concurrency per GPU, adopt token-aware batching, or route long-context requests to separate nodes.
2) Symptom: OOM despite reporting gigabytes free
Root cause: Fragmentation or large contiguous allocation requirement.
Fix: Standardize input shapes/buckets, reduce peak workspace allocations, tune allocator settings, or restart workers periodically (with a plan, not panic).
3) Symptom: Latency spikes, GPU utilization low, memory utilization high
Root cause: Memory bandwidth-bound workload.
Fix: Use more bandwidth-capable GPUs, use quantization or kernels that reduce memory traffic, increase compute intensity (fusion), or lower batch if it increases memory thrash.
4) Symptom: Performance varies wildly between “identical” nodes
Root cause: Different driver/CUDA/framework versions, different MIG partitions, or different thermal/power limits.
Fix: Standardize images, enforce node labels and scheduling constraints, and monitor clocks/power states.
5) Symptom: Service degrades slowly over hours, fixed by restart
Root cause: Memory leak in GPU allocations or allocator cache growth; fragmentation accumulation.
Fix: Add per-worker lifetime limits, track VRAM over time, reproduce with soak tests, and pin framework versions until verified.
6) Symptom: Multi-tenant GPU is fine until one team deploys
Root cause: No isolation; one tenant loads extra weights or increases batch size; “noisy neighbor” VRAM theft.
Fix: Enforce GPU allocation policies: MIG slices, dedicated GPU pools, per-tenant quotas, or model sharing via a single serving layer.
7) Symptom: Copying data to GPU is suddenly a big fraction of latency
Root cause: Host↔device transfers increased; pinned memory not used; preprocessing moved off-GPU; or NUMA/PCIe path is suboptimal.
Fix: Pin memory, move preprocessing to GPU (carefully), fix CPU affinity/NUMA placement, and avoid unnecessary tensor copies.
8) Symptom: “We upgraded to a bigger model” and throughput cratered
Root cause: The model fits, but KV cache and batching no longer fit at desired concurrency; bandwidth demand increased.
Fix: Recalculate VRAM budget including KV cache; lower concurrency, add sharding, or deploy a smaller/quantized variant for high-concurrency paths.
Checklists / step-by-step plan
Checklist A: Capacity planning VRAM like an adult
- Define the workload envelope: max input tokens, max output tokens, target concurrency, and expected traffic burstiness.
- Budget VRAM line items: weights + KV cache at max envelope + temporary buffers + framework overhead + safety margin.
- Pick a headroom policy: don’t run steady-state above ~80–85% VRAM if you care about tail latency and allocator health.
- Decide your “tail routing”: what happens to very long requests—reject, truncate, summarize, or route to a specialized pool.
- Codify it: config files, Helm values, Terraform variables, whatever you use. No tribal knowledge.
Checklist B: Serving configuration that avoids self-inflicted VRAM pain
- Cap max batch size by tokens/shape, not just request count.
- Use shape/token bucketing to reduce allocator churn and fragmentation.
- Set explicit max context length aligned with business needs, not model marketing.
- Prefer a single shared model server per GPU over multiple processes loading identical weights (when isolation requirements allow).
- Track VRAM metrics as first-class SLO signals: used, reserved, allocation failures, and time-to-first-token under load.
Checklist C: Incident response for VRAM-related failures
- Confirm: capacity vs fragmentation vs bandwidth using the fast diagnosis playbook.
- Mitigate quickly: cap tokens, reduce concurrency, drain noisy neighbors, restart only if needed.
- Preserve evidence: capture nvidia-smi, allocator summaries, and token distribution snapshots.
- Fix forward: enforce new caps/routing and schedule soak tests before removing mitigations.
- Post-incident: update VRAM budgets and admission control so the same request tail can’t knock you over again.
FAQ
1) Is VRAM capacity more important than GPU compute after 2026?
For many inference workloads, yes—because you can’t compute what you can’t keep resident.
Compute helps when you’re compute-bound. Many production workloads become memory-bound first.
2) If a model fits in VRAM, why do I still get OOM?
Because weights are not the whole story. KV cache grows with context and concurrency, and fragmentation can prevent large allocations even with “free” memory.
3) Does quantization solve the VRAM problem?
It helps a lot for weights and sometimes for KV cache, but it can introduce overhead, kernel constraints, and accuracy tradeoffs.
Treat it as a tool, not a religion.
4) What’s the simplest knob to reduce VRAM usage immediately?
Cap max tokens (input and output) and reduce concurrency per GPU.
If you need a fast emergency brake, those two are usually the cleanest.
5) Is paging KV cache to host memory a good idea?
It can be, if your latency tolerance and workload profile match the tradeoff.
In many real-time services, paging turns tail latency into a horror story. Benchmark with production-like tails, not averages.
6) How much VRAM headroom should I keep?
If you care about stability: keep meaningful headroom. A common operational target is ~15–25% free under typical peak,
but the right number depends on fragmentation behavior and request variance.
7) Should I use MIG to isolate tenants?
If you have noisy neighbors and strict isolation needs, MIG is often worth it.
The tradeoff is reduced flexibility: stranded capacity and less ability to “borrow” VRAM across workloads.
8) Why does “VRAM reserved” differ from “VRAM allocated”?
Many allocators reserve chunks to reuse them for performance. Reserved memory can improve latency but increases peak footprint and fragmentation risk.
Track both, and watch trends over time.
9) Do faster disks or more host RAM compensate for low VRAM?
Not reliably. They can support offload strategies, but offload usually increases latency variance and adds new failure modes.
Use offload deliberately, not as a coping mechanism.
10) What’s the most common purchasing mistake?
Buying based on “model fits” rather than “model fits at required concurrency with required max context, with headroom, under realistic tails.”
The second statement is the one your customers pay for.
Conclusion: next steps you can actually do
VRAM after 2026 isn’t a spec-sheet flex. It’s a production constraint that shapes reliability, cost, and throughput.
Treat it like you treat disk IOPS, database connections, or network egress: measured, budgeted, and defended with policy.
Practical next steps:
- Add VRAM metrics to your standard dashboards: used, reserved, allocation failures, and per-request token counts.
- Implement token-aware admission control: cap inputs/outputs and route long-context requests intentionally.
- Run a soak test with production-like tail prompts, not averages. Watch fragmentation over hours.
- Decide your isolation model: shared GPU with strict quotas, MIG slices, or dedicated pools—then enforce it.
- Write and maintain a VRAM budget per service. Yes, it’s boring. That’s why it works.