Tesla K80 vs P40: Which Should You Buy for AI?
Buy the P40
The K80 costs $60 and the P40 costs $239, so the K80 looks like the obvious budget pick. It is not, for one reason that has nothing to do with benchmarks: the K80's advertised 24GB is two separate 12GB GPUs, and a single model cannot use both as one pool. The P40's 24GB is one contiguous block.
On top of that, the K80 is Kepler: compute capability 3.7, which current CUDA releases have dropped. You are not choosing between a slow card and a fast one. You are choosing between a card that runs today's software and one that fights it.
The specs, side by side
| Spec | Tesla K80 ($60) | Tesla P40 ($239) | What it means |
|---|---|---|---|
| VRAM (advertised) | 24GB GDDR5 | 24GB GDDR5X | Looks identical on a listing |
| VRAM (usable by one model) | 12GB | 24GB | The K80 is two GPUs on one board |
| Architecture | Kepler (CC 3.7) | Pascal (CC 6.1) | Kepler is dropped from current CUDA |
| CUDA cores | 4,992 (2 × 2,496) | 3,840 | K80's total is split across two dies |
| Bandwidth | 480 GB/s (240 per GPU) | 347 GB/s | Per-GPU is what a model sees |
| FP32 | 8.7 TFLOPS (4.4 per GPU) | 12 TFLOPS | P40 wins outright on one die |
| INT8 inference | No | Yes (47 TOPS) | Useful for quantized serving |
| TDP | 300W | 250W | K80 draws more for less usable work |
| Cooling | Passive | Passive | Both need an aftermarket fan |
| $/GB (usable) | $5.00 | $9.96 | Closer than the sticker suggests |
Why "24GB" is the whole story
This is the part that catches people, because the listing is not lying. The board really does carry 24GB. It carries it as two GK210 GPUs with 12GB each, connected by a PCIe switch. Your operating system sees two devices. So does CUDA, and so does llama.cpp.
The practical consequence: a model that needs 20GB does not load at all. Not slowly, not with swapping. It does not load, unless you split it across both GPUs and accept a PCIe hop at every layer boundary that crosses between them. For LLM inference, where every token walks the full depth of the model, that hop is paid constantly.
What actually fits
- K80 (12GB per GPU): 7-8B models at Q4-Q8. A 14B at Q4 is tight. Nothing in the 20B+ class without splitting.
- P40 (24GB contiguous): the same 7-8B models with far more context, 14B comfortably, 32B at Q4, and 33B coding models at a squeeze.
If your models are all 7B, the capacity difference genuinely may not matter to you. Read on: that is the one case where this gets less one-sided.
The Kepler problem
Compute capability 3.7 has been removed from current CUDA toolkit releases. That does not mean nothing runs; it means you are pinned to older toolchains and older builds, and the gap widens with every release. In practice you can expect to compile things yourself, hunt for legacy wheels, and find that a runtime everyone else installs with one command needs an afternoon.
The P40's Pascal (CC 6.1) is old too, and it will eventually go the same way. It has not yet. Today it is a card you install drivers for and use.
What the prices actually are
The sticker gap is $179. Two things narrow it:
- Both cards are passive. Neither has a fan. A 3D-printed shroud and a 92mm fan is $15-25, and without it either card throttles within minutes in a desktop case. The K80's real cost is nearer $75.
- The K80 draws 300W to the P40's 250W. Running a few hours a day, that difference is real money over a year, and it buys you less usable VRAM.
Neither card has display output, so both need something else driving your monitors. That is not a differentiator, but it surprises first-time datacenter-card buyers.
When the K80 is defensible
Being honest about the narrow cases, because "never" is rarely true:
- It is free. Pulled from a decommissioned server, it costs you a fan and some patience. Use it.
- You specifically want a Kepler target. If you are learning CUDA and want to understand older compute capabilities, this is cheap real hardware.
- You only ever run 7B models and $60 is genuinely your ceiling. 12GB holds a 7B at Q8 comfortably. You will fight the software, but it can be made to work.
Outside those, the money saved buys a card that may not run your stack.
If the P40 is more than you want to spend
The K80 is not the only cheaper option, and it is not the best one:
- Tesla P100 16GB at $80: cheaper than the P40, and roughly twice as fast thanks to 732 GB/s HBM2. 16GB rather than 24GB is the trade. If your models fit in 16GB, this is the card to buy, not the K80. See the P100 review.
- Tesla M40 24GB at $229: slower than the P40 but a real contiguous 24GB on Maxwell, which current CUDA still supports. The honest middle option. See the M40 review.
Both are better answers to "I want to spend less than $239" than the K80 is.
Verdict
The P40, and it is not close
Most GPU comparisons are trade-offs where the right answer depends on your models and your budget. This one is not. The P40 gives you 24GB a model can actually use, on an architecture current software supports, for $179 more than a card that gives you neither.
If $239 is out of reach, buy a P100 at $80 or an M40 at $229 instead. The K80 makes sense when it is free, and rarely otherwise.
Whichever you choose, budget $15-25 for a shroud and fan. All three are passive server cards, and none of them will run quietly (or at all) without one.
Compare Live Prices
GPUDojo tracks both cards by $/GB VRAM with live used prices.
View P40 ListingsAlso see the full K80 review, the full P40 review, and the cheapest 24GB GPUs.