# Recomendations please: NVMe M.2 vs Optane?



## Seattle Dan (Jul 9, 2017)

Hello,

I’m planning to purchase a new PC for editing in LR & PS cc. (My old laptop is too slow, always has been!) I’m wondering if anyone has recommendations for hardware considerations, especially NVMe M.2 and how that compares to the new Intel Optane drive, again specifically for photo editing purposes.

Thanks for your thoughts!

Dan


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## clee01l (Jul 9, 2017)

Welcome to the forum.  Drive speed has little to do with LR performance.   LR for the most part does not reference the original image file after develop processing is complete.  It is only used for Exports and printing and in a few other instances.  The rest of the time LR relies upon the Previews folder and the Smart Previews folder. 
While a fast SSD is important for the OS, A fast multi core CPU, supported Video card with 2-4GB of VRAM and adequate working storage are helpful in getting the most out of LR.


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## Linwood Ferguson (Jul 9, 2017)

Optane and NVMe address a similar issue - faster access to disk -- but do it differently.  I have done no tests on Optane, but it is not all that different from various Intel caching solutions in prior iterations of Intel Rapid Storage Technology.

The biggest difference is that Optane is a cache, whereas NVMe is, at least out of the box, storage.

So all of NVMe is accessible at the same speed, for both read and write.

Optane, on the other hand, begins with nothing, and has to bring things in as they are accessed (at least I do not think anything is pre-fetched, like indexes, but it could be).  So NVMe accessing a random thing (maybe an image) is always just as fast.  Optane is slow the first time, and fast the second (assuming it has not been replaced by something more "hot"). 

Optane has the potential to accelerate any size of storage, with the effectiveness of that acceleration based on how big the storage is (smaller is more effective) (Note below).

There's no reason you cannot use both, by the way, Optane for speeding up spinning drives and NVMe for frequently accessed stuff like system disks.

Note: The effectiveness of caches like this is not exactly proportional to storage size, but rather to the size of the "locality of reference" over any given time frame.  So for example if you were reading every image you had, you are referencing a huge area over a small time, and caches like Optane are not helpful.  On the other hand, if you were doing lots and lots of accesses and updates to the catalog, which is small and likely fits completely in the cache, this can provide a great amount of acceleration.  Different programs, indeed different operations within a program, are going to have vastly different locality of reference and so will govern how effective Optane is, often in unpredictable ways.  This makes it, unfortunately, one of those things that you almost have to try to know how much it helps.


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## Linwood Ferguson (Jul 9, 2017)

Ferguson said:


> The biggest difference is that Optane is a cache, whereas NVMe is, at least out of the box, storage.


I probably should explain that statement.  Intel Rapid Storage Technology (IRST) has long had the ability to turn an SSD into a cache.  I THINK it can do that with NVMe though I have not tried it.

IRST caching and Optane are the same basic concept, just different (probably not all that different) implementations, though it sounds like Optane may have some efficiency shortcuts built in to make it faster.

I have tried using SSD as cache to spinning disk without significant improvements, notably in lightroom, and switched to dedicated SSD for preview cache and catalog with better results.  All caching schemes like Optane rely on an algorithm to decide what should be in the cache (and fast) and what not in the cache (and slow). That's great if you do not already know what should be cached; if you do, just using SSD (or NVMe) as storage is a better solution. 

It's all about whether you are smarter than your computer.  (this is a question  -- your computer has already submitted that answer to Google and others  )


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