Tesla M40 24GB in 2026: Still Worth Buying for AI?

24GB of VRAM at $229, and why that is no longer the bargain it was | Updated September 2026

The Tesla M40 24GB used to have one clear argument: it was the cheapest way to get 24GB of VRAM, at $70-90 on eBay. That argument has weakened considerably. The M40 now starts around $229, while the 16GB Tesla P100 sits near $80 with two and a half times the memory bandwidth, and the Tesla P40, a genuinely better card in every respect, is only $10 more.

That does not make the M40 useless. It makes it a narrower proposition than it was: 24GB of capacity, on 2015 Maxwell silicon, with no native FP16 and a passive cooler you will have to solve. Whether that is worth $229 depends entirely on whether you need the capacity more than the speed. Here are the numbers to decide with.

The Case For the M40

The M40's advantage is capacity: 24GB of VRAM for $229, on a card that modern software still supports. This lets you:

• Run 20B+ parameter models at Q4 that won't fit on 16GB cards
• Run 7B-14B models at higher quantization (Q8, even FP16 for 7B) for better quality
• Use larger context windows without running out of VRAM
• Experiment with fine-tuning small models (LoRA on 7B fits in 24GB)

The key word is capacity. If you need to fit a model larger than 16GB and cannot stretch to a P40 at $239, the M40 is the cheapest card that will hold it. If your models fit in 16GB, the P100 at $80 is both cheaper and dramatically faster: see the comparison below.

Real-World Performance

Here's what you can realistically expect running models on the M40 with llama.cpp:

These speeds are usable but not fast. For comparison, a P40 is about 20-40% faster across the board, and an RTX 3090 is 3-4x faster. If you're accustomed to ChatGPT's response speed, the M40 will feel noticeably slower for larger models.

The Cooling Problem

• 3D-printed fan shroud ($5-10 in materials, or $15-25 printed for you) - Search "Tesla M40 fan shroud" on Printables/Thingiverse. Mounts a 92mm or dual 80mm fans onto the heatsink. This is the best solution.
• Zip-tied fans (free if you have spare fans) - Zip-tie a 92mm Noctua or Arctic fan directly to the card's heatsink. Ugly but effective.
• High-airflow case - A case with strong front-to-back airflow (like a server chassis) can work without modifications, but only if you have genuinely strong fans.
• Open test bench - Running the card on an open bench with a desk fan pointed at it. Works for testing but not a long-term solution.

M40 vs P40: Is the Extra $10 Worth It?

Spec	M40 24GB ($229)	P40 24GB ($239)	Difference
Architecture	Maxwell	Pascal	1 generation newer
Bandwidth	288 GB/s	347 GB/s	+20%
VRAM	24GB GDDR5	24GB GDDR5X	Same capacity, faster memory
INT8 Inference	No	Yes	P40 has INT8 support
tok/s (8B Q4)	~32	~45	~40% faster
tok/s (32B Q4)	~12	~16	~33% faster
Power	250W	250W	Same
Cooling	Passive	Passive	Both need aftermarket
Software support	Good	Better	P40 has wider kernel support

The P40 is 20-40% faster across the board, adds INT8 inference, and has wider kernel support. Whether $10 buys enough of that to matter is a judgement about your own patience. The M40 is not unusable, it is just slower at everything.

M40 vs P100: The Comparison That Changed

This is the one worth thinking hardest about, because it inverted. When this article was first written the M40 was $70-90 and the P100 cost more. Today the M40 starts at $229 and the P100 starts at $80.

Spec	M40 24GB ($229)	P100 16GB ($80)	What it means
VRAM	24GB GDDR5	16GB HBM2	M40 fits larger models
Bandwidth	288 GB/s	732 GB/s	P100 is ~2.5x faster at generation
Native FP16	No	Yes	Matters for training and some runtimes
Architecture	Maxwell (2015)	Pascal (2016)	Both CUDA 12 capable
Cooling	Passive	Passive	Both need aftermarket

The decision is capacity against speed, and the cheaper card is now the faster one. If your models fit inside 16GB, the P100 is faster and costs less, so there is no argument for the M40. If you need more than 16GB, the M40 is still the cheapest card that holds it, and 24GB of slow VRAM beats 16GB you cannot fit into. Work out your model size first; the answer follows from it.

Verdict