If you’ve ever watched your GPU climb into the 80–90°C range and thought, “This can’t be healthy,” you’re not being precious. Heat is the tax you pay for throughput. Sometimes it’s a normal tax. Sometimes it’s a sign your system is quietly eating itself.
In production, hot GPUs aren’t a vibe. They’re a reliability problem, a performance problem, and occasionally a “why did this node reboot at 3 a.m.” problem. Let’s make the physics intuitive, then turn it into a practical playbook you can actually run.
The sticky explanation: tiny space, huge power, brutal math
GPUs run hot for the same reason busy kitchens do: lots of work happening in a small area, with energy constantly poured in, and only a few exits for that energy to leave.
At the simplest level:
- Electric power goes in. Your GPU might pull 200–600 watts under load.
- Nearly all of it becomes heat. Not “some.” Almost all. The useful output is computation, but computation doesn’t leave the box as energy; it leaves as results. The power still turns into heat.
- The heat must move through a chain of materials. Silicon → package → thermal interface (paste/pad) → heatsink cold plate → fins → air (or liquid) → room → HVAC.
- Any weak link raises temperatures everywhere upstream. Thermal systems are queues. Back up the exit, the whole place gets hotter.
Here’s the part people forget: a modern high-end GPU is a power-dense device. It’s not just “600 W.” It’s “600 W in a palm-sized hotspot area,” with local peaks that matter more than the average.
Another way to remember it: CPUs are sprinters with strong cooling assumptions built into server chassis and socket standards. GPUs are freight trains with a space heater bolted to the side. Same electricity, different packaging and airflow reality.
Short joke #1: A GPU is basically a very expensive calculator that doubles as a room heater—your electricity bill just wants to be appreciated.
What “running hot” actually means (and what numbers matter)
“My GPU is 85°C” isn’t enough information to diagnose anything. You need to know which sensor, what the workload is doing, and whether the GPU is throttling.
The important temperatures
- GPU core temperature: the classic “GPU temp” most tools show. Useful, but often not the first limiter anymore.
- Hotspot / junction temperature: the hottest measured point on-die. This often hits limits first, especially with imperfect contact or aging paste.
- Memory junction temperature (especially GDDR6X): memory can run hotter than the core and trigger throttling. You can have a “fine” core temp and still be in trouble.
- VRM / power stage temperatures: voltage regulation components heat up under high current. They’re the unsung reliability killers.
- Inlet/ambient temperature: the temperature of the air entering the GPU cooler, not the temperature “somewhere in the room.” A 5°C inlet rise is a big deal.
What “too hot” means in practice
GPUs are designed to run hot. Vendors know the silicon can handle high junction temperatures. But “designed to” and “good for your fleet” are different things. For consumer cards, seeing core temps in the 70s to mid-80s under sustained load can be normal depending on model and airflow. For datacenter parts, behavior depends on the cooling design (passive heatsinks rely on chassis airflow) and workload power profiles.
What you should care about operationally:
- Throttling: performance drops because temperature or power limits are hit.
- Error rates: ECC memory errors, PCIe errors, driver resets, application faults.
- Stability margins: a GPU that is “fine” at 22°C ambient might be a disaster at 30°C on a hot day or in a partially clogged filter scenario.
- Component aging: higher temps accelerate wear-out mechanisms. Your MTBF dreams die slowly, then all at once.
One quote to keep your priorities straight: “Hope is not a strategy.”
— General Gordon R. Sullivan
Why GPUs run hotter than CPUs in practice
There’s no single reason. It’s a stack of reasons that line up like bad dominoes.
1) GPUs chase throughput with massive parallelism
A CPU has a handful of complex cores optimized for low-latency decisions. A GPU has thousands of simpler execution units designed to do the same kind of work across huge datasets. That parallelism is great for graphics and machine learning. It also means a lot of transistors switching at once. Switching costs energy. Energy becomes heat.
2) They operate near their power and thermal limits by design
Modern GPUs use aggressive boost algorithms: they’ll climb frequency and voltage until they hit a limit—temperature, power, or voltage reliability constraints. You aren’t buying a “3.0 GHz GPU.” You’re buying a control system that rides the edge of what cooling and power delivery allow.
3) Board power includes more than the core
CPU discussions often focus on package power. GPU “board power” includes memory, VRMs, and other components. The cooler has to deal with multiple heat sources, not just one neatly packaged die under a socketed cooler.
4) Cooling assumptions are frequently wrong
Server CPUs live in a world where chassis airflow is engineered for them. GPUs often get bolted into “good enough” cases, shoved next to another GPU, and asked to breathe through a ribbon cable and someone’s optimism.
5) Workloads are sustained
Games spike and vary. Training runs and inference pipelines can pin a GPU at high utilization for hours or days. Thermal saturation happens. A cooler that looks fine for 10 minutes can fall apart at minute 45.
6) Heat density is the villain, not absolute watts
A 300 W device spread across a large area can be easier to cool than a 250 W device with a tiny hotspot. Hotspot temperature is where thermal paste, mounting pressure, and micro-scale conduction become your performance limit.
Interesting facts and history that explain today’s heat
Heat problems didn’t show up because engineers got sloppy. They showed up because GPUs won and we started asking them to do everything.
- Early 3D accelerators were modest power devices. Late-1990s add-in cards were a fraction of modern wattage; many used small heatsinks and tiny fans because power density was low.
- Dedicated GPU power connectors became mainstream as board power rose. The move beyond what the PCIe slot could safely deliver forced new connector standards and new failure modes (including, yes, melted connectors when tolerances and handling are bad).
- “Shader cores” unified graphics pipelines—and created easier general compute. This architectural shift helped enable GPU compute later; more compute meant more sustained power draw.
- CUDA (2007) popularized GPGPU. Once developers could treat GPUs as compute devices, workloads stopped being “bursty graphics” and became “24/7 math furnace.”
- HBM showed the industry’s willingness to move memory closer. High Bandwidth Memory stacks memory near the GPU with a wide interface. It improves bandwidth and can change where heat concentrates and how it’s cooled.
- GDDR6X increased memory power density. Faster signaling can mean hotter memory modules, making memory junction temps a frequent limiter on some consumer cards.
- Datacenter GPUs pushed passive cooling hard. Many server GPUs rely on chassis airflow instead of onboard fans; if the server isn’t designed for it, temperatures rocket.
- Boost algorithms got bolder over time. Modern GPUs opportunistically boost until they hit limits, meaning “it runs hot” is often literally the intended operating strategy.
- Multi-GPU layouts created thermal interference. Stacking high-power cards adjacent can cause one card’s exhaust to become another card’s intake, which is basically thermal cannibalism.
The heat path: from transistor to room air
When someone says “my GPU is hot,” your job is to ask: where is the thermal resistance?
Step 1: Power is generated at the die
Dynamic power is dominated by switching activity and voltage. Without dragging you into equations, the key operational truth is: voltage changes hurt more than frequency changes. A small voltage bump can cause a disproportionate power increase, and the heat follows.
Step 2: Heat spreads through the package
Heat has to leave the silicon and move through the package and heat spreader (if present). Imperfections here aren’t user-serviceable, but they show up as “hotspot much higher than core,” especially under load.
Step 3: The thermal interface is a make-or-break layer
Thermal paste and pads fill microscopic gaps. If paste dries out, or a pad is too thick, or mounting pressure is uneven, you get a classic signature: hotspot temperature climbs fast while average core temp looks “okay-ish.”
Step 4: The heatsink must move heat to the air
This is where fin density, fan pressure, and dust matter. A heatsink is only as good as the airflow through it. Air that goes around the fins is marketing airflow, not cooling airflow.
Step 5: The case and room have to evacuate the heat
If the case recirculates exhaust, your GPU cooler is forced to use warmer intake air. Same heatsink, worse delta-T, higher temps. In a datacenter, if the hot aisle/cold aisle separation is leaky, your “cold” inlet slowly becomes “lukewarm regret.”
Short joke #2: Thermal troubleshooting is like detective work, except the culprit is always “airflow,” and it always had an alibi.
Fast diagnosis playbook: find the bottleneck in minutes
This is the order that saves time. The goal is to determine whether you’re limited by temperature, power, airflow/ambient, or sensor/telemetry confusion.
First: confirm the GPU is actually throttling (not just “warm”)
- Check clocks and utilization under load.
- Check throttle reasons (thermal, power, voltage, reliability).
- Decision: if it’s not throttling and stability is fine, you may be chasing a number, not a problem.
Second: compare core vs hotspot vs memory temps
- If hotspot is far above core, suspect poor contact/paste/mounting pressure or a localized load region.
- If memory junction is leading, suspect memory cooling (pads, airflow, backplate design) or workload memory pressure.
- Decision: fix the dominant limiter, not the most visible number.
Third: check inlet/ambient and airflow reality
- Measure intake air temperature where the GPU breathes.
- Validate fan RPM and fan curve behavior.
- Decision: if inlet is high or airflow is obstructed, don’t repaste first. Move air first.
Fourth: check power behavior and caps
- Look at power draw, power limit, and performance per watt.
- Decision: in many production cases, a small power cap yields a large temperature drop with minimal throughput loss.
Fifth: check for platform-level issues
- PCIe errors, CPU throttling causing GPU underutilization (and weird thermal patterns), driver resets.
- Decision: if the node is unstable, treat “heat” as a symptom, not the root cause.
Practical tasks with commands: what to run, what it means, what you decide
These are real checks you can run on a Linux host with NVIDIA GPUs. The outputs shown are representative. Your exact fields vary by driver and GPU model. The point is what you read and what you do next.
Task 1: Snapshot GPU thermals, clocks, and power in one view
cr0x@server:~$ nvidia-smi
Tue Jan 13 10:22:41 2026
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.54.14 Driver Version: 550.54.14 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------|
| 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 A10 On | 00000000:3B:00.0 Off | 0 |
| 30% 78C P2 143W / 150W | 10980MiB / 23028MiB | 92% Default |
+-----------------------------------------+------------------------+----------------------+
What it means: Temp is 78°C at 92% utilization, power near cap. Perf state P2 suggests it’s in a high-performance mode. Fan at 30% might be conservative.
Decision: If performance is stable and no throttling is observed, this may be acceptable. If you see frequent throttling, raise fan curve or reduce power limit.
Task 2: Watch live changes to spot throttling patterns
cr0x@server:~$ watch -n 1 nvidia-smi --query-gpu=timestamp,temperature.gpu,utilization.gpu,clocks.sm,power.draw,pstate --format=csv
timestamp, temperature.gpu, utilization.gpu, clocks.sm, power.draw, pstate
2026/01/13 10:23:00, 79, 95, 1695, 149.22, P2
2026/01/13 10:23:01, 81, 96, 1695, 149.80, P2
2026/01/13 10:23:02, 83, 96, 1620, 149.90, P2
What it means: Clocks drop as temp rises while power stays pinned. That’s often thermal or reliability management.
Decision: Confirm throttle reason next. If thermal, improve cooling or cap power. If power cap, adjust power limit or accept the cap.
Task 3: Ask the driver why performance is being limited
cr0x@server:~$ nvidia-smi -q -d PERFORMANCE | sed -n '1,120p'
==============NVSMI LOG==============
Timestamp : Tue Jan 13 10:23:10 2026
Driver Version : 550.54.14
CUDA Version : 12.4
Performance State : P2
Clocks Throttle Reasons
Idle : Not Active
Applications Clocks Setting : Not Active
SW Power Cap : Active
HW Slowdown : Not Active
Thermal Slowdown : Not Active
Sync Boost : Not Active
SW Thermal Slowdown : Not Active
What it means: You’re power-limited, not temperature-limited. The GPU is doing what it’s told: obey the cap.
Decision: If you need more throughput, raise the power limit (and ensure cooling/PSU headroom). If you need cooler operation, keep or lower the cap and tune for perf/W.
Task 4: Log temps and power over time for correlation (cheap observability)
cr0x@server:~$ nvidia-smi --query-gpu=timestamp,temperature.gpu,power.draw,utilization.gpu,clocks.sm --format=csv -l 5 -f /tmp/gpu_telemetry.csv
# Monitoring GPU 00000000:3B:00.0.
# Logging to /tmp/gpu_telemetry.csv
What it means: You get a time series you can graph or diff between “good” and “bad” runs.
Decision: If temp ramps slowly to a plateau, airflow/ambient is likely. If temp spikes instantly, interface/contact or fan control might be the issue.
Task 5: Check if the GPU is allowed to use the fan behavior you think it is
cr0x@server:~$ nvidia-settings -q GPUFanControlState -q GPUTargetFanSpeed
Attribute 'GPUFanControlState' (server:0[gpu:0]): 0.
Attribute 'GPUTargetFanSpeed' (server:0[gpu:0]): 30.
What it means: Fan control state 0 typically means automatic control. Target is 30% (but actual may differ).
Decision: If temps are high and fan stays low, enable manual control (if policy allows) or fix the fan curve in firmware/software tooling.
Task 6: Verify actual fan RPM and whether a fan is failing
cr0x@server:~$ nvidia-smi --query-gpu=fan.speed,temperature.gpu --format=csv
fan.speed, temperature.gpu
30 %, 83
What it means: Fan is running, but we don’t know if 30% is enough.
Decision: If GPU is temperature-throttling, increase fan speed and retest. If fan speed is high but temps remain high, suspect airflow obstruction, heatsink clogging, or poor thermal contact.
Task 7: Check CPU thermals and throttling (because the platform lies)
cr0x@server:~$ sudo sensors
coretemp-isa-0000
Adapter: ISA adapter
Package id 0: 92.0°C (high = +100.0°C, crit = +105.0°C)
Core 0: 89.0°C
Core 1: 91.0°C
nvme-pci-0100
Adapter: PCI adapter
Composite: +68.9°C (low = -40.1°C, high = +84.8°C, crit = +89.8°C)
What it means: The CPU package is very hot and close to throttling. This can distort GPU workload behavior (lower feeding rate, different utilization, odd thermal cycles).
Decision: Fix chassis airflow and CPU cooling too. A GPU thermal incident is often a whole-node airflow incident.
Task 8: Verify PCIe link health (errors can masquerade as “GPU is acting weird”)
cr0x@server:~$ sudo lspci -s 3b:00.0 -vv | sed -n '1,80p'
3b:00.0 VGA compatible controller: NVIDIA Corporation Device 2236 (rev a1)
Subsystem: NVIDIA Corporation Device 147e
LnkCap: Port #0, Speed 16GT/s, Width x16
LnkSta: Speed 16GT/s, Width x16
DevSta: CorrErr+ NonFatalErr- FatalErr- UnsupReq-
What it means: Correctable errors are being logged. That’s not immediate doom, but it’s signal. Heat can worsen marginal links.
Decision: If you see rising correctable errors correlated with high temps, improve cooling and reseat hardware during maintenance. If errors persist, consider board/slot issues.
Task 9: Confirm there isn’t a simple airflow obstruction (the classic “why are we like this”)
cr0x@server:~$ sudo lsblk -o NAME,HCTL,SIZE,MODEL
NAME HCTL SIZE MODEL
sda 0:0:0:0 447.1G Samsung SSD 860
nvme0n1 1.8T SAMSUNG MZVL21T0HCLR-00B00
What it means: This isn’t an airflow command. It’s a reminder: don’t tunnel on the GPU. NVMe at 69°C and CPU at 92°C suggests overall chassis airflow is under-designed or blocked.
Decision: Inspect filters, fan walls, cable routing, blanking panels, and whether the server is installed in a rack with proper cold aisle intake.
Task 10: Check kernel logs for thermal or GPU driver events
cr0x@server:~$ sudo journalctl -k -b | egrep -i 'nvrm|pcie|thermal|throttl' | tail -n 20
Jan 13 10:20:11 server kernel: nvidia-modeset: Allocated GPU:0 (GPU-2d3a...)
Jan 13 10:22:58 server kernel: NVRM: Xid (PCI:0000:3b:00): 79, GPU has fallen off the bus.
Jan 13 10:23:00 server kernel: pcieport 0000:00:03.1: AER: Correctable error received: 0000:3b:00.0
What it means: “Fallen off the bus” and AER errors are serious stability indicators. Heat can be a contributor, but power integrity, PCIe seating, or firmware can also be responsible.
Decision: Treat as an incident: reduce load, increase cooling, verify PSU headroom, reseat GPU, update firmware/driver, and consider hardware replacement if recurrent.
Task 11: Measure GPU power draw and enforce a sensible power cap
cr0x@server:~$ sudo nvidia-smi -pl 130
Power limit for GPU 00000000:3B:00.0 was set to 130.00 W from 150.00 W.
What it means: You just reduced the maximum board power. This usually drops temperatures quickly.
Decision: Run your workload and compare throughput. If you lose 2–5% performance but gain 10°C and stability, that’s a trade you take in production without arguing.
Task 12: Confirm the power cap is applied and observe thermals after the change
cr0x@server:~$ nvidia-smi --query-gpu=temperature.gpu,power.draw,clocks.sm,utilization.gpu --format=csv
temperature.gpu, power.draw, clocks.sm, utilization.gpu
74, 129.12, 1620, 96
What it means: Temperature dropped from the low 80s to mid 70s while utilization stays high. Clocks may be slightly lower, but stable.
Decision: Keep the cap as a policy for this thermal environment, or use it as a stopgap while you fix airflow.
Task 13: Check if persistence mode is set (reduces churn, helps predictability)
cr0x@server:~$ sudo nvidia-smi -pm 1
Enabled persistence mode for GPU 00000000:3B:00.0.
All done.
What it means: The driver keeps the GPU initialized, which can reduce latency spikes and weird power/clock transitions between jobs.
Decision: In shared production nodes, persistence mode often improves predictability. If your security or multi-tenant model forbids it, document the trade-off.
Task 14: Validate that your process isn’t causing needless heat (runaway utilization)
cr0x@server:~$ nvidia-smi pmon -c 1
# gpu pid type sm mem enc dec command
# Idx # C/G % % % % name
0 28741 C 92 40 0 0 python
What it means: A single process is driving SM usage high. That may be expected. It also means the heat is workload-driven, not “mystery background.”
Decision: If this should be idle, kill or reschedule the job. If it’s intentional, tune power limits/fan curves and ensure airflow capacity.
Task 15: Check for CPU-side bottlenecks that cause bursty GPU behavior (and odd thermals)
cr0x@server:~$ mpstat -P ALL 1 3
Linux 6.8.0 (server) 01/13/2026 _x86_64_ (64 CPU)
10:25:01 AM CPU %usr %nice %sys %iowait %irq %soft %steal %idle
10:25:02 AM all 92.11 0.00 6.44 0.73 0.00 0.11 0.00 0.61
What it means: CPU is heavily loaded. If your GPU pipeline depends on CPU preprocessing, you might get spiky GPU utilization, which can create weird fan behavior (fans lag behind spikes) and thermal cycling.
Decision: Profile the pipeline. Offload preprocessing, increase CPU capacity, or smooth batch scheduling to stabilize thermal load.
Task 16: Verify cgroup/container limits aren’t creating accidental thermal behavior
cr0x@server:~$ systemd-cgls --no-pager | head -n 25
Control group /:
-.slice
├─system.slice
│ ├─docker.service
│ │ ├─4121 /usr/bin/dockerd -H fd://
│ │ └─...
│ └─ssh.service
└─user.slice
└─user-1001.slice
└─user@1001.service
└─app.slice
What it means: You’re seeing the workload placement. Thermal incidents sometimes come from job collocation: two “medium” GPU jobs land on the same node, turning it into a toaster.
Decision: Fix scheduling constraints (one heavy GPU job per node), or enforce power caps per job class.
Three corporate mini-stories from the thermal trenches
Mini-story 1: The incident caused by a wrong assumption
The team had a fresh batch of GPU servers delivered—same chassis model as last quarter, same GPU model, same rack layout. The deployment runbook was “copy/paste.” It always is until it isn’t.
Within hours, training jobs started failing in a way that looked like software: random CUDA errors, occasional driver resets, and once in a while the host would log PCIe correctable errors. Nothing screamed “overheat” because core temperatures weren’t outrageous—mid 70s, sometimes 80°C. The incident commander focused on versions and rollbacks.
After too much time and not enough coffee, someone checked memory junction temperatures. They were ugly. Not “warm,” ugly. The wrong assumption was that “GPU temp” in dashboards reflected the true limiting sensor. It didn’t. Memory was throttling hard, then the driver would fall over under sustained stress.
The root cause ended up being mundane: the vendor had revised the GPU’s memory pad spec mid-cycle, and the preinstalled pads in this batch had slightly different compression. It wasn’t a conspiracy. It was supply chain reality. Contact wasn’t great, memory ran hot, and the error rates climbed under sustained workloads.
The fix was equally mundane: re-pad during a controlled maintenance window, plus a temporary power cap in the interim. The longer-term fix was procedural: baseline all relevant sensors (core, hotspot, memory) and alert on deltas, not just absolute core temperature.
Mini-story 2: The optimization that backfired
A different company, different problem. They were paying a lot for colocation and wanted to cut power. Someone proposed an “efficiency mode”: reduce datacenter fan speeds and raise cold-aisle setpoint a couple degrees. The vendor said it was within spec. Management loved it because it showed up as immediate savings.
At first, nothing caught fire. In fact, the GPU core temperatures looked only slightly higher. So the change rolled out broadly. Then the weirdness began: intermittent performance regressions. Not full outages—those are easy. This was slower epoch times, sporadic job overruns, and occasional SLA misses.
The backfire came from a detail: GPU boost behavior is a control system. Raise inlet temperature and you reduce thermal headroom. The GPUs spent more time in thermal management states, bouncing clocks. The average utilization stayed high, but effective throughput dropped. Meanwhile, the higher steady-state temperature increased the rate of correctable memory errors on certain nodes, which triggered retraining retries upstream. “Power optimization” turned into “compute tax.”
The rollback wasn’t total. The team kept part of the change, but only after segmenting: some racks had better airflow and containment and could handle the new setpoints with power caps. Others couldn’t. The lesson wasn’t “never optimize.” It was “optimize with guardrails and telemetry that reflects throughput, not just temperature.”
Mini-story 3: The boring but correct practice that saved the day
A startup running GPU inference had a habit that looked almost too basic to matter: every new node went through a 30-minute burn-in with a standardized workload, and they recorded a baseline for core temp, hotspot, memory junction, fan speed, and power at steady state.
Six months later, they started seeing a few nodes running 8–12°C hotter under the same job. Nothing was “broken” yet, but it was drifting. Because they had baselines, they didn’t argue about what “normal” means. They had receipts.
The team pulled one node and found the GPU heatsink fins partially clogged—not with dramatic dust bunnies, just a thin mat that reduced airflow enough to matter. Another node had a slightly misrouted internal cable that was impinging airflow near the GPU intake. Boring stuff.
They cleaned, corrected routing, re-ran burn-in, and put the nodes back. No incident. No emergency maintenance. No weekend page. The glamorous part of SRE is writing clever automation. The part that keeps you employed is catching boring degradation before it becomes a failure.
Common mistakes: symptom → root cause → fix
1) Symptom: “GPU temp is fine, but performance is inconsistent”
Root cause: Hotspot or memory junction temperature is throttling, not core temperature.
Fix: Monitor hotspot and memory temps. Improve memory cooling (pads, airflow), adjust fan curves, or apply a power cap to reduce heat density.
2) Symptom: “Temps spike instantly when load starts”
Root cause: Poor thermal contact (dried paste, uneven mounting pressure, shifted pads) or fan control lag.
Fix: Confirm fan response under step load; if fans respond but temps spike, plan a repaste/re-pad with correct thickness and torque patterns. Don’t guess pad sizes.
3) Symptom: “One GPU in a multi-GPU box is always hotter”
Root cause: Thermal recirculation or placement effects (top card eating exhaust, blocked intake).
Fix: Reorder cards if possible, add ducting/blanking, increase chassis airflow, or enforce per-slot power limits. Treat rack airflow as part of the system.
4) Symptom: “Fans are loud, temps still high”
Root cause: Heatsink fins clogged, poor case pressure, or airflow bypass (air taking the easy path around the fins).
Fix: Clean fins and filters, ensure proper shrouds/blanking, verify intake/exhaust separation. Airflow without direction is just turbulence.
5) Symptom: “Driver resets / Xid errors during heavy jobs”
Root cause: Can be thermal, power delivery instability, PCIe issues, or marginal hardware that only fails hot.
Fix: Correlate logs with temps/power. Reduce power limit, improve cooling, check PCIe seating and AER errors, update firmware/driver, and quarantine flaky hardware.
6) Symptom: “GPU runs hotter after a case change or ‘cleanup’”
Root cause: Cable management blocked an intake, missing blanking panels, or fans oriented incorrectly.
Fix: Validate airflow direction physically. Use smoke/streamers if you must. Put blanking panels back. Don’t trust aesthetics over airflow.
7) Symptom: “Temperatures are stable, but throughput dropped after tuning”
Root cause: Over-aggressive power cap or fan curve that keeps temps low but forces lower clocks.
Fix: Tune for performance per watt. Increase power limit gradually while observing throttle reasons and throughput, not just temperature.
8) Symptom: “Only fails on hot days / high ambient”
Root cause: No headroom. Cooling system is right at the edge; small inlet changes push it over.
Fix: Build headroom: lower power limit, increase airflow, improve containment, schedule heavy workloads during cooler periods if you’re constrained.
Checklists / step-by-step plan
Step-by-step: stabilize a hot GPU node (production-safe order)
- Confirm throttling and limiter: use
nvidia-smi -qthrottle reasons; identify thermal vs power vs something else. - Check sensor spread: core vs hotspot vs memory junction (if available). Identify the leading indicator.
- Check inlet conditions: validate chassis fans, filters, and rack intake temperature.
- Apply a temporary power cap: reduce by 10–20% and observe throughput impact.
- Increase fan curve if allowed: aim for stable temps, not oscillation.
- Look for stability signals: kernel logs for Xid, AER errors, unexpected resets.
- Clean and re-test: filters, heatsinks, and obstructions. Re-run the same workload for comparison.
- Plan corrective maintenance: repaste/re-pad only after airflow and power are sane; do it in a controlled window.
- Document a baseline: record steady-state temps and power so you can detect drift later.
Checklist: what to capture in an incident ticket
- GPU model, driver version, firmware if relevant
- Workload description (utilization pattern, duration, batch size)
- Core temp, hotspot temp, memory junction temp (and which tool read it)
- Power draw, power cap, pstate, clocks
- Throttle reasons from
nvidia-smi -q - Fan speed and chassis fan state
- Inlet temperature measurement location
- Kernel logs for Xid/AER/thermal events
- Before/after results from a power cap test
Checklist: decisions that usually beat “repaste everything”
- Cap power first if you need immediate stability.
- Fix airflow second (it helps everything in the node).
- Only then consider repasting/re-padding—because it’s invasive, variable, and easy to do wrong.
- Alert on deltas and throttle reasons, not just a single temperature threshold.
FAQ
1) Is it normal for a GPU to run at 80–85°C?
Often, yes—depending on GPU model, cooler design, ambient temperature, and workload. “Normal” means “not throttling, stable, and within vendor limits.” In production, you still want headroom.
2) What’s the difference between GPU temperature and hotspot/junction temperature?
GPU temperature is usually an averaged or representative core sensor. Hotspot/junction is the maximum on-die reading. Hotspot is the one that finds bad paste, bad mounting pressure, and local heat density.
3) Why is my memory junction temperature higher than the core?
Because the memory is its own heat source and sometimes has worse cooling contact. High-bandwidth memory traffic and certain GDDR types can run very hot. If memory junction leads, you have a memory cooling problem, not a core cooling problem.
4) Should I undervolt or power-limit my GPU?
Power limiting is usually the safer, more repeatable production move: set a cap, measure throughput, keep the best perf/W point. Undervolting can work, but it’s more fragile across silicon variance and driver/firmware changes.
5) My GPU fans are at 100% and temps are still high—what now?
That usually means airflow isn’t going through the heatsink fins (clogging, bypass, bad shrouding), ambient/inlet is too hot, or the thermal interface is poor. Cleaning and airflow verification come before repasting.
6) Why do multi-GPU systems run hotter even if each GPU is “within spec”?
Because the system-level airflow and recirculation matter. One card’s exhaust becomes another’s intake. The chassis fans may not be sized for the combined heat load. “Within spec” per component doesn’t guarantee a stable combined system.
7) Does thermal throttling damage the GPU?
Thermal throttling is a protective mechanism; it’s trying to prevent damage. The risk is that you’re operating close to limits, increasing the chance of instability and accelerating aging over time.
8) Why does performance sometimes get worse after improving cooling?
If your “improvement” changed fan curves or power limits too aggressively, you may have lowered clocks or increased power-limit throttling. Validate with throttle reasons and throughput metrics, not just temperature.
9) What’s the single most effective change to reduce GPU temperature quickly?
In many real fleets: reduce the power limit by 10–20%. It’s immediate, reversible, and often costs less performance than you’d expect. Then fix airflow to recover headroom.
Conclusion: next steps that actually move the needle
GPUs run hot because they turn a lot of electrical power into computation inside a tiny piece of silicon, and the heat has to escape through a long chain of “pretty good” materials and airflow assumptions. When that chain weakens anywhere—paste, pads, fins, fans, chassis, rack, HVAC—you get higher temps, throttling, and eventually instability.
Do this next, in order:
- Stop guessing: check throttle reasons and the right sensors (core, hotspot, memory junction).
- Buy stability with a power cap: test a 10–20% reduction and measure throughput impact.
- Make airflow boring and correct: clean, unblock, shroud, and validate inlet temperature where the GPU actually breathes.
- Baseline everything: record steady-state temps/power/clocks so you can detect drift before it pages you.
- Only then do invasive work: repaste/re-pad during maintenance, with correct materials and repeatable procedure.
Heat isn’t moral failure. It’s accounting. Your job is to balance the books: watts in, heat out, performance delivered, failures prevented.