Nothing ruins a calm on-call shift like a GPU fleet that looks “fine” on core temperature, yet mysteriously drops hashrate, frame rate, inference throughput—or just starts rebooting nodes like they’re bored.
The culprit is often memory. Specifically GDDR6X, which can run blisteringly hot while the GPU core sits there wearing a smug 65°C grin. If you only watch “GPU Temp,” you’re flying on instruments that don’t include the cliff.
Why GDDR6X is different (and why it runs so hot)
GDDR6X isn’t just “GDDR6 but faster.” It changed how it signals data. And that one design choice echoes all the way into your ops dashboards.
PAM4: when “more bits per cycle” means “more analog problems per watt”
GDDR6X uses PAM4 (pulse-amplitude modulation with four levels). Instead of two signal levels (0/1), you get four. That lets you carry two bits per symbol and push bandwidth without doubling clock the same way you would with NRZ signaling.
In practice, PAM4 makes the signaling chain more sensitive. You’re dealing with smaller voltage margins, more equalization, and more work to keep a clean eye diagram. More work means more power burned in the memory interface—on both the GPU side (memory controllers and PHY) and the memory chips themselves.
The result is a familiar production pattern: the GPU core can be under control while memory junction temperatures march toward the danger zone, because the heat sources are physically distributed around the package and often cooled by different (worse) paths.
Memory heat is “edge heat,” and edge cooling is annoying
Most GPU coolers are optimized for the GPU die. It’s the big obvious hotspot, mechanically central, and the reason the product exists. Memory chips sit around the perimeter of the PCB, frequently relying on thermal pads to connect to the main heatsink or backplate.
Thermal pads are convenient for manufacturing. They are also a great way to turn “should conduct heat” into “actually insulates heat” if thickness, compression, or placement is off by a millimeter. And the older the card, the more that pad behaves like stale chewing gum.
Memory temps don’t have the same “feel” as core temps
Core temperature is often tightly regulated with aggressive fan curves and predictable heatsink contact. Memory junction temperature is a different beast: high local density, weaker conduction paths, and less airflow. It’s why you can see a GPU at 70°C with memory at 104–110°C and think you’re safe because 70 sounds reasonable.
Operational rule: for GDDR6X, treat memory temperature as a first-class metric. Not “nice to have.” First-class. If you don’t have it, you’re blind in one eye and surprised you keep walking into doors.
Joke #1: GDDR6X memory temps are like your data center’s “temporary” cabling—ignored until it becomes the main storyline.
What “memory temperature” even means
Sensor names: “memory temp,” “mem junction,” “hotspot,” and why they disagree
On many modern NVIDIA cards, what you want is memory junction temperature—the hottest point inside the memory package that the sensor model can estimate or measure. It’s not the same as the PCB temperature near the chip, and it’s not the same as “GPU hotspot” (which refers to the GPU die’s hottest region).
Vendors expose this in different ways:
- GPU Temperature: the core sensor, typically controlled and “reasonable.”
- GPU Hotspot: the hottest portion of the GPU die. Useful, but not your memory problem.
- Memory Junction Temperature: what usually goes critical first on GDDR6X.
Different tools may show different labels. Some only show core temp and leave you to guess. That’s where you get fleets that are “stable” until they’re not.
Why junction temperature is scarier than it sounds
Junction temperature is close to the silicon’s reality. If your memory junction is 106°C, the little world inside that chip is living hard. Silicon can survive high temperature, but reliability is a game of probabilities, not promises. Heat accelerates aging mechanisms. You might not see an immediate crash; you might see a slow increase in correctable errors, timing margin loss, and “random” instability under specific workloads.
Throttle behaviors: the GPU protects itself, not your SLA
Thermal protection is there to keep hardware from killing itself immediately, not to preserve your throughput target. When memory hits its limit, you can see:
- Memory clock reductions (performance drops without obvious core temp issues)
- Power limit behavior changes (board-level control loops compensating)
- Driver resets under sustained load (especially with borderline pads/contact)
Interesting facts and short history (8 quick points)
- GDDR “won” the consumer GPU world largely because it scaled bandwidth without the packaging complexity of HBM for most price points.
- PAM4 wasn’t invented for GPUs; it’s a signaling technique used broadly in high-speed links when you need more throughput without proportionally higher frequency.
- GDDR6X debuted in consumer GPUs as a bandwidth leap without a full architecture rewrite—great for performance per dollar, spicy for thermals per square centimeter.
- HBM’s thermal story is different: stacked memory near the GPU package can be hot too, but cooling paths and integration differ; GDDR6X spreads heat around the board and into pads and backplates.
- Memory junction sensors became mainstream only after users started correlating unexplained throttling with VRAM heat; telemetry evolved because failure was visible and annoying.
- Mining workloads made VRAM thermals famous because they sustain high memory bandwidth continuously—perfect for revealing bad pad contact and weak airflow.
- Backplates changed role from “stiff metal pretty cover” to “secondary heatsink” once vendors started adding thermal pads to couple memory heat through the PCB.
- Fan curves historically chased core temp, which is why memory often overheats: the control loop is watching the wrong patient.
Failure modes: throttling, errors, and the slow death of “it’s fine”
1) Soft throttling: the silent performance haircut
This is the most common. Your GPU looks healthy in generic monitoring. But a workload that is memory-bandwidth-heavy—training, inference with large activations, rendering, mining, compression kernels—starts underperforming after a few minutes.
What’s happening: memory junction climbs, firmware/driver reduces memory clocks to stay within a thermal envelope, and your throughput falls off a cliff that no one correlates because “GPU Temp” stayed stable.
2) Uncorrectable errors: the “random crash” that isn’t random
As margin shrinks, you can see driver resets, CUDA errors, corrupted outputs, or application crashes. In enterprise environments you’ll often see correctable error counters tick up first—if you’re collecting them. In less instrumented environments, you just see jobs failing “sometimes.”
3) Long-term reliability: heat is an accelerant
High temperature increases the speed of wear-out mechanisms. You don’t need to turn this into a materials science lecture to act on it: if you run memory at the edge for months, you should expect earlier degradation than a fleet running 20°C cooler.
And no, your warranty does not care about your quarterly targets.
4) Secondary effects: VRM and board hotspots
Memory heat doesn’t exist alone. In cramped chassis layouts, the same airflow constraints that punish VRAM also punish VRMs. Sometimes you fix memory by increasing fan speed, only to discover you’ve moved the pain to noise budgets or fan wear. Engineering is compromise. Pick the compromise deliberately.
One quote, paraphrased idea: “Hope is not a strategy.” — paraphrased idea often attributed to engineers in reliability and operations circles. Treat it as a reminder, not a bumper sticker.
Fast diagnosis playbook
This is the “you have 10 minutes and a pager” sequence. The goal is to identify whether you’re memory-thermal-limited, core-thermal-limited, power-limited, or something else.
First: confirm you can see the right sensor
- Check whether memory junction temperature is available in your tooling.
- If you can’t see it, treat that as the immediate incident blocker: you cannot diagnose what you cannot observe.
Second: correlate temperature with clocks and throttling reasons
- Watch memory temp, memory clock, and throttle/perf states under sustained load.
- If memory temp climbs and memory clock drops while core temp remains stable, you’ve found your bottleneck.
Third: determine whether it’s environmental, mechanical, or configuration
- Environmental: case airflow, intake temp, clogged filters, rack layout, adjacent hot exhaust.
- Mechanical: pad contact, pad thickness, backplate coupling, heatsink seating.
- Configuration: fan curves tied to core temp, power limits too high, memory overclocks, undervolt choices.
Fourth: pick the lowest-risk mitigation
- Increase airflow and fan speed before you disassemble hardware.
- Cap power or reduce memory clock before you start swapping pads across a fleet.
- Only repad/repaste when evidence points to contact issues or when you need a permanent fix.
Practical tasks: commands, outputs, and decisions (12+)
These are real tasks you can run on Linux GPU nodes. Each includes: command, what the output means, and what decision you make.
Task 1: Check whether your driver exposes memory junction temperature
cr0x@server:~$ nvidia-smi -q -d TEMPERATURE
==============NVSMI LOG==============
Temperature
GPU Current Temp : 66 C
GPU Shutdown Temp : 95 C
GPU Slowdown Temp : 90 C
GPU Max Operating Temp : 88 C
Memory Current Temp : 104 CMeaning: “Memory Current Temp” is present. Good—this is the sensor you should alert on for GDDR6X.
Decision: If this field is missing, you need a driver/tooling upgrade or alternative telemetry path. No excuses.
Task 2: Watch memory temp and clocks live under load
cr0x@server:~$ nvidia-smi --query-gpu=timestamp,index,temperature.gpu,temperature.memory,clocks.sm,clocks.mem,pstate,power.draw --format=csv -l 2
timestamp, index, temperature.gpu, temperature.memory, clocks.sm, clocks.mem, pstate, power.draw
2026/01/21 10:14:01.123, 0, 67, 102, 1560, 9501, P2, 240.12 W
2026/01/21 10:14:03.124, 0, 68, 106, 1560, 8100, P2, 239.88 W
2026/01/21 10:14:05.125, 0, 68, 108, 1560, 7001, P2, 238.77 WMeaning: Memory clock drops as memory temp rises; core temp is steady. That’s classic VRAM thermal throttling.
Decision: Stop tuning the core. Focus on memory cooling, airflow, power cap, or memory clock limits.
Task 3: Check throttling reasons (when supported)
cr0x@server:~$ nvidia-smi -q -d PERFORMANCE
Performance
Performance State : P2
Clocks Throttle Reasons
Idle : Not Active
Applications Clocks Setting : Not Active
SW Power Cap : Not Active
HW Slowdown : Active
HW Thermal Slowdown : Active
HW Power Brake Slowdown : Not ActiveMeaning: Hardware thermal slowdown is active. This often correlates with memory junction exceeding threshold even if core temp is not extreme.
Decision: Treat as a thermal incident, not a driver bug. Move to airflow/power cap checks.
Task 4: Confirm power limit and current draw
cr0x@server:~$ nvidia-smi -q -d POWER | sed -n '1,80p'
Power Readings
Power Management : Supported
Power Draw : 241.05 W
Power Limit : 250.00 W
Default Power Limit : 250.00 W
Enforced Power Limit : 250.00 W
Min Power Limit : 125.00 W
Max Power Limit : 300.00 WMeaning: You’re running close to limit. Reducing power can reduce memory controller/IO heating and sometimes memory temperature indirectly.
Decision: If you’re thermally bound, test a lower power limit (next task) before touching hardware.
Task 5: Apply a conservative power cap (safe, reversible)
cr0x@server:~$ sudo nvidia-smi -pl 220
Power limit for GPU 00000000:01:00.0 was set to 220.00 W from 250.00 W.Meaning: Board power cap reduced. This usually reduces heat across GPU+memory subsystems.
Decision: Re-run Task 2; if memory temp drops materially with minimal throughput loss, keep the cap and document it as policy.
Task 6: Force a higher fan speed to test airflow sensitivity
cr0x@server:~$ nvidia-settings -a "[gpu:0]/GPUFanControlState=1" -a "[fan:0]/GPUTargetFanSpeed=85"
Attribute 'GPUFanControlState' (server:0[gpu:0]) assigned value 1.
Attribute 'GPUTargetFanSpeed' (server:0[fan:0]) assigned value 85.Meaning: Fan control overridden. If memory temps respond strongly, you likely have airflow/headroom issues rather than pure contact problems.
Decision: If +20% fan yields -10°C memory junction, you have a cooling path that can be improved with chassis airflow changes.
Task 7: Validate PCIe slot spacing and topology (heat neighbors matter)
cr0x@server:~$ nvidia-smi topo -m
GPU0 GPU1 CPU Affinity
GPU0 X PHB 0-15
GPU1 PHB X 0-15Meaning: Topology doesn’t show physical spacing directly, but it tells you whether GPUs are likely adjacent on the same root complex. Adjacent cards often recirculate heat.
Decision: If one GPU’s memory temp is consistently worse, check its physical position: “middle card syndrome” is real.
Task 8: Check system ambient and inlet temperatures (don’t guess)
cr0x@server:~$ sudo sensors
coretemp-isa-0000
Adapter: ISA adapter
Package id 0: 54.0°C (high = +80.0°C, crit = +100.0°C)
nvme-pci-0100
Adapter: PCI adapter
Composite: +48.9°C (low = -10.1°C, high = +84.8°C, crit = +89.8°C)Meaning: Not a perfect ambient reading, but rising system and NVMe temps often indicate poor chassis airflow or high inlet temperature.
Decision: If everything is warm, fix room/rack airflow first. GPU repadding won’t beat a 35°C inlet.
Task 9: Identify whether workload is memory-bandwidth bound
cr0x@server:~$ nvidia-smi dmon -s pucm -d 2 -c 5
# gpu pwr gtemp mtemp sm mem enc dec
# Idx W C C % % % %
0 230 67 104 35 92 0 0
0 232 68 106 34 95 0 0
0 228 68 108 33 96 0 0
0 225 68 108 30 97 0 0
0 221 67 107 28 96 0 0Meaning: “mem %” is high while SM utilization is moderate. That’s a memory-heavy workload—the exact kind that punishes GDDR6X thermals.
Decision: Cooling strategy should prioritize memory; consider memory clock caps with minimal impact on compute-limited tasks, but expect impact here.
Task 10: Check kernel and driver logs for Xid resets (symptom of instability)
cr0x@server:~$ sudo journalctl -k -n 50 | egrep -i "nvrm|xid"
Jan 21 10:12:44 server kernel: NVRM: Xid (PCI:0000:01:00): 79, GPU has fallen off the bus.
Jan 21 10:12:44 server kernel: NVRM: GPU 0000:01:00.0: RmInitAdapter failed!Meaning: “Fallen off the bus” can be power, PCIe, or thermal/instability induced. When it appears after sustained load and correlates with high memory junction, suspect VRAM thermal or board-level thermal stress.
Decision: Reduce power limit and memory clocks, validate airflow, then investigate physical cooling. Also check PSU/PCIe cabling separately.
Task 11: Verify persistence mode (prevents some clock/telemetry weirdness)
cr0x@server:~$ sudo nvidia-smi -pm 1
Enabled persistence mode for GPU 00000000:01:00.0.Meaning: Persistence mode keeps the driver initialized; it can stabilize monitoring and reduce re-init churn between jobs.
Decision: Enable it fleet-wide on compute nodes unless your environment explicitly forbids it.
Task 12: Set application clocks (if supported) to reduce memory heat
cr0x@server:~$ sudo nvidia-smi -ac 8100,1500
Applications clocks set to "(MEM 8100, SM 1500)" for GPU 00000000:01:00.0Meaning: You’re pinning memory and SM clocks. Lower memory clocks often drop memory junction temperature significantly on GDDR6X.
Decision: Use this as a targeted mitigation for memory-bound workloads or thermally constrained chassis.
Task 13: Validate fan tach and failures (because “fan 0 is fine” is a lie)
cr0x@server:~$ nvidia-smi --query-gpu=fan.speed,temperature.gpu,temperature.memory --format=csv
fan.speed, temperature.gpu, temperature.memory
32 %, 66, 104Meaning: Fan speed is low while memory is high. If your fan curve is tied to GPU core, it may never ramp enough for VRAM.
Decision: Adjust fan policy to consider memory temp (via daemon or vendor tool), or set a minimum fan floor during memory-heavy workloads.
Task 14: Check physical throttling by observing clocks over time after mitigation
cr0x@server:~$ nvidia-smi --query-gpu=temperature.memory,clocks.mem,power.draw --format=csv -l 5
temperature.memory, clocks.mem, power.draw
108, 7001, 222.14 W
102, 8100, 220.90 W
98, 8100, 221.33 W
96, 8100, 221.05 WMeaning: Memory temp dropped and memory clock recovered at the same power cap. You’ve proven the bottleneck and the fix’s effectiveness.
Decision: Roll the change into config management; schedule hardware remediation only for outliers.
Three corporate mini-stories from the trenches
Mini-story 1: The incident caused by a wrong assumption
One company rolled out a new GPU batch into an existing inference cluster. They’d done the usual checks: core temps looked great, power stayed under the rack budget, and the first smoke tests passed. So they declared victory, rolled to production, and went home.
Two weeks later, intermittent job failures started. Not the clean kind—half the batch would finish, the other half would return nonsense outputs or crash with driver resets. The on-call rotation did the predictable dance: blame the model, blame CUDA, blame the kernel. Then blame each other. Standard corporate cardio.
The wrong assumption was simple: “If core temperature is stable, the GPU is thermally stable.” It wasn’t. Under real traffic patterns, the models hit long periods of high memory bandwidth. Memory junction temperatures were quietly pegging near their thermal limit and triggering memory clock drops and occasional instability.
They didn’t see it because their monitoring stack only scraped “GPU Temp.” Memory temp wasn’t collected, and nobody noticed the memory clock drifting down. Once they added memory junction telemetry, the correlation was embarrassing and immediate.
The fix wasn’t exotic. They set a conservative power cap and a minimum fan floor on nodes running those models. Then they scheduled repadding for the worst offenders. The incident ended the moment they stopped assuming the GPU is a single temperature.
Mini-story 2: The optimization that backfired
Another org was chasing noise and power savings in a mixed-use lab. Someone proposed a “smart” fan policy: keep fans low unless the GPU core crosses 78°C. It looked good in demos—quiet, civilized, and it kept the headline number under control.
They deployed it broadly, including on systems doing memory-heavy batch work overnight. The next morning, throughput was down. Not catastrophically, but enough to miss internal deadlines and cause the usual “why is the cluster slow” Slack archaeology.
The policy worked exactly as designed. That was the problem. Core temps never exceeded the threshold, so fans didn’t ramp. Memory junction temperature climbed into the throttling region, memory clocks fell, and jobs ran longer. Longer jobs meant longer heat soak. Heat soak meant even higher memory temps. The optimization became a feedback loop of polite underperformance.
They reverted the fan policy and replaced it with something less clever: a minimum fan floor when memory utilization stays high for more than a short window, plus alerts on memory junction. Noise went up slightly. Throughput returned. Nobody wrote a blog post about the victory, because boring solutions are rarely celebrated.
Mini-story 3: The boring but correct practice that saved the day
A third team ran GPUs in a data center environment where hardware changes were slow and audits were plentiful. They couldn’t “just repad” cards without change control. So they treated thermal management as policy, not heroics.
Every node had standardized telemetry: core temp, memory junction, hotspot (when available), fan speed, power draw, and clocks. They had alerts not only on absolute memory temperature, but also on delta: if memory temp rose faster than normal for a given workload profile, the node was flagged for inspection.
When a batch of jobs suddenly started running slow, they didn’t guess. Dashboards showed memory clocks dipping on a subset of nodes while the rest stayed stable. Those nodes also had a slightly higher inlet temperature, traced to a rack airflow change after unrelated maintenance.
They corrected the airflow issue, and performance normalized without touching a single GPU. The “boring” part was the win: consistent instrumentation and a simple policy that assumed memory could be the limiting factor.
Joke #2: The only thing more sensitive than PAM4 signaling is a postmortem where someone says “we didn’t think we needed that metric.”
Common mistakes: symptom → root cause → fix
1) Symptom: core temp is fine, performance still drops after 5–15 minutes
Root cause: memory junction hits thermal limit; memory clock throttles.
Fix: monitor memory junction; raise airflow/fan floor; cap power; reduce memory clocks; consider repadding if temps are unusually high for the model/chassis.
2) Symptom: “random” CUDA errors or driver resets under sustained load
Root cause: thermal margin loss (often memory), sometimes combined with aggressive memory OC or insufficient power stability.
Fix: remove memory overclocks, reduce power limit, validate airflow, check logs for Xid patterns, then inspect physical cooling and PCIe power cabling.
3) Symptom: one GPU in a multi-GPU box runs much hotter memory temps than peers
Root cause: physical placement (middle card), recirculated exhaust, obstructed intake, or uneven pad contact.
Fix: adjust spacing or slot assignment; add chassis fans or ducting; set per-GPU fan/power policy; repad if it’s uniquely bad across multiple chassis.
4) Symptom: changing core undervolt doesn’t improve memory temps
Root cause: memory temperature is driven by memory IO/power and cooling path; core undervolt helps some, but not always.
Fix: target memory: reduce memory clock, power cap board, improve pad/backplate contact and airflow over memory zones.
5) Symptom: repasting the GPU core did nothing
Root cause: you fixed the wrong interface; the memory pads are the limiting factor, not the core paste.
Fix: inspect/replace thermal pads with correct thickness and compression; ensure heatsink/backplate pressure is even; validate with before/after telemetry.
6) Symptom: memory temps improved briefly after cleaning, then got worse again
Root cause: dust was part of it, but fan curve or room temperature drift is pushing you back to the edge; also possible pad pump-out/aging.
Fix: implement fan floors for memory-heavy workloads; verify inlet temperature; schedule preventive pad replacement for high-hour cards.
7) Symptom: memory temp readings are missing or always zero
Root cause: driver/tool mismatch, unsupported GPU/firmware path, or using a tool that only reads core sensors.
Fix: upgrade driver; use nvidia-smi -q for ground truth; update monitoring exporters; don’t build policies on absent data.
Checklists / step-by-step plan
Step-by-step: stabilize a hot GDDR6X system without touching hardware
- Collect memory junction temps and memory clocks. If you can’t, stop and fix telemetry.
- Run a 10–15 minute sustained load. Watch for memory clocks stepping down while core temp stays stable.
- Force fans to a high fixed speed for 5 minutes. If memory temp drops quickly, airflow is a major lever.
- Apply a conservative power cap. Re-test; keep the cap if throughput impact is acceptable.
- Set a minimum fan floor in production for memory-heavy workloads. Tie it to workload type or GPU utilization patterns, not just core temp.
- Remove memory overclocks. If you’re overclocking VRAM in production, you’re choosing drama.
Step-by-step: decide whether to repad (and how to avoid making it worse)
- Prove it’s a contact problem. Compare the same model in similar chassis; if one card is an outlier by 10–20°C, suspect pads/contact.
- Check warranty and change control. Don’t turn a thermal fix into a compliance incident.
- Document pad thicknesses before removal. Wrong thickness is how you trade memory heat for a warped heatsink mount.
- Replace pads with correct thickness and appropriate conductivity. High conductivity doesn’t matter if compression is wrong.
- Validate with telemetry. You want before/after memory junction temps under the same load.
- Roll changes slowly. One chassis, one card type, one pad recipe at a time.
Operational checklist: what to alert on for GDDR6X
- Memory junction temperature: alert on high absolute values and on sustained time above your chosen threshold.
- Memory clock drops: alert when memory clock deviates from expected under steady workload.
- Fan speed anomalies: low fan at high memory temp is usually policy trouble or fan failure.
- Thermal slowdown flags: if available, treat as actionable, not informational.
- Error logs: Xid events or repeated resets correlate with instability; investigate thermals alongside power and PCIe.
FAQ
1) Why does GDDR6X run hotter than GDDR6?
Because PAM4 signaling and the associated PHY/equalization generally increase power in the memory subsystem at a given bandwidth. More bandwidth, more heat, and the cooling path is often worse than the GPU die’s.
2) What’s a “safe” GDDR6X memory temperature?
It depends on the specific card and its throttle points, but operationally: don’t live near the throttle threshold. Aim to keep memory junction comfortably below where clocks start dropping under sustained load.
3) Why is my GPU core at 65°C but memory is over 100°C?
Different heat sources, different cooling paths. The core has direct heatsink contact with paste; memory relies on pads and often less airflow. The core temp doesn’t represent the board’s hottest components.
4) Will undervolting the GPU core fix VRAM temperatures?
Sometimes a bit, often not enough. If memory is the bottleneck, you usually need to address memory clocks, board power, airflow, or pad contact.
5) Do backplates help GDDR6X temps?
They can—if there are thermal pads coupling hot regions to the backplate and the backplate has airflow or mass to sink heat. A decorative backplate with no coupling is mostly a vibe.
6) Why did increasing fan speed help memory temps more than core temps?
Core temps are tightly coupled to the main heatsink and regulated. Memory temps are often airflow-limited around the card’s edges. More airflow can disproportionately help VRAM and VRM areas.
7) Should I repad every GDDR6X card preemptively?
No. Repadding is invasive, risks warranty/compliance issues, and can be done wrong. Use telemetry to identify outliers or chronically throttling systems, then target those.
8) Why does my memory temperature spike only on certain workloads?
Because some workloads saturate memory bandwidth or keep memory controllers busy continuously. Those workloads create sustained memory heat even when SM utilization is moderate.
9) Can thermal pads “age out” and cause rising memory temps over time?
Yes. Pads can harden, creep, or lose effective contact pressure over time and through thermal cycling. The symptom is creeping memory junction temps for the same workload and ambient conditions.
10) What’s the single best metric to add if I can only add one?
Memory junction temperature. If you also can, add memory clock and throttle reasons so you can prove cause-and-effect.
Conclusion: the next steps that actually move the needle
GDDR6X turns “GPU thermals” into a two-body problem. You don’t get to manage just the die anymore. You have to manage the memory ecosystem: pads, airflow, fan policy, power limits, and workload behavior.
Do this next, in order:
- Get memory junction temperature into your monitoring and alerting, alongside memory clock.
- Run a sustained load test and confirm whether memory clocks drop as temps rise.
- Apply the lowest-risk mitigation first: fan floors, airflow improvements, and a conservative power cap.
- Only then consider hardware remediation (repadding/inspection) for outliers or fleets that still throttle under sane operating conditions.
Once you treat memory as a first-class thermal domain, the “mystery throttling” story mostly disappears. Not because the hardware got nicer—but because you stopped asking one sensor to explain an entire board.