Is the Nvidia RTX 5070 TI a good option for local AI in 2026? This GPU sits right between the 5060 TI and the 5080 in terms of GPU capability and has
just enough VRAM that in today's age you can run models like Qwen 36 and some really powerful options that previously wouldn't have been possible. I
want to get into what you can actually run on this GPU and if this is worth buying whether you're
planning to use one or up to four of these for local AI. Welcome to AI Flux. Let's get into it. So, the RTX 5070 and specifically the 5070 TI is a
pretty interesting GPU. This GPU initially only came with 12 gigs of VRAM and Nvidia pretty quickly released the 5070 TI which has 16 gigs, which is
the GPU I'll be focusing on today. For gaming, this is a fantastic option and this GPU will also give
you a lot of really good capability for the latest features Nvidia has in their drivers and in CUDA, which is getting much more important as FP4 and
moreover INV FP4 quantization gains significantly more support and momentum as one of the best ways to deliver small models to people with less VRAM.
So, what do the specs of this card actually look like? So, this is a Blackwell GPU and in comparison
to the 5070, you'll see that there are piecemeal improvements, but the degree of performance increase you get is greater than a lot of people think.
So, we're getting about a 25 to 30% bump in AI tops, which mostly comes from the tensor cores and the CUDA core count, which is really what you want
to look for when you're buying a GPU for local AI. The memory bandwidth and the size of the memory bus
between the VRAM is also a little bit larger. So, you're getting slightly faster memory and a bit more width to use that. And part of that is just
because the way they get more RAM onto these GPUs is you have more chips and with more chips you need a wider bus to connect all of those and actually
use them. If we go to full specs, we can see here that in fact the 5070 TI has quite a bit more CUDA
cores and actually quite a few more tensor cores as well, which is again what we want to see. This clocks about as high. Ironically, the 5070 TI in
terms of raw gigahertz is actually slightly less fast than the 5070, but you're getting a significantly better GPU than the 5060 TI with basically
twice the memory bandwidth and much faster memory. So, it's technically the same, but at a high level
this is a better GPU. Now, one thing that was a little disappointing that pretty much had to do with Nvidia realizing that they had to produce way
more GPUs and then just picking AI GPUs as opposed to consumer kind of gaming focused GPUs that happen to be okay for local AI is that we never saw
the rumored 5070 TI Super that was supposed to give us 24 gigs of GDDR7 VRAM at $800. It's really too bad
we didn't see this. If Sam Altman hadn't tried to buy all of the VRAM in the Western Hemisphere, we may have actually seen this, but unfortunately we
haven't. We haven't really seen modded GPUs coming out of China either and even if we did, tariffs would basically make it not make any sense. So,
some of the best information I found about this GPU was on the Hugging Face forums. What's interesting
is most of what is said here is that with Blackwell GPUs of this class, you're getting first-party support. So, even if you're overpaying a little
bit, you know, the tooling is really meant for Nvidia. The best Unsloth quants of Qwen 3.6 and Kimiko 2.6, which are the best for local coding,
generally will always come for Nvidia first and will eventually maybe show up as MLX quants. That's becoming
much more popular. Then again, sorry for my AMD fans, but ROCm support even on Linux is pretty just not great and I would not recommend it. So, what's
cool here is this GPU actually has quite a bit of capability as long as you're willing to do just a bit of offloading. So, one of the really
interesting models that showcases this is the LTX 2.3 INV FP4 model. So, what's cool is vLLM at this point
supports INV FP4 and FP4 quants. This is something that even 6 months ago we weren't sure was going to happen, but the pervasiveness of this format
gotten us here. So, what's interesting is with FP4, you'll see an ability to run this, but with INV FP4, there's a caveat where you won't actually see
a lot of the advances of this method of compression unless you can fit the entire model weights and
activations into VRAM. It's a quirk of INV FP4 and why it's actually faster, but it's cool to see this still happening. And we can hope that with a
few more tricks that Unsloth is able to pull out of their hat that we'll see even more functionality slowly trickling down into the 16 GB GPU class.
And where I initially found this was actually on the ComfyUI blog, which is looking at INV FP4
quantization on Blackwell GPUs. So, that's basically any 50 series or Blackwell Pro GPU. And this is important because you don't get this with the
- Of course, the 3090 by price when we look at that, it's going to be apparent that the 3090 is potentially a better option. But it doesn't have
INV FP4 support and in certain cases even has limited FP4 support just with how the latest driver
support looks for the 3000 series of GPUs. So, what's interesting here is this is running Flux 1 dev on the 5070 TI along with Z image and you'll see
that there's a huge speed up from FP8 to INV FP4 especially on generative models with this GPU. So, one thing I will say is if most of your workload
is not necessarily agentic tasks and is a lot of image and video generation using LTX models, this
GPU is an incredible option because you're just getting a bit more performance at the GPU level than you would with the 5060 TI or any older GPU from
Nvidia that might actually have more VRAM and the performance wins here are pretty crazy. Now, obviously the RTX Pro 6000 is just going to be really
fast in any case, but the speed up we're seeing is over 100% for the 5070 TI, which is really, really
cool. Now, there are a lot of technical reasons why this happens. It basically has to do with less being shuffled over the PCIE bus when you're using
the GPU. This is a really common observation with a lot of different models. It's why small models are able to run on GPUs well even with 1X PCIE
risers. So, like the slowest form you can even buy. So, this is actually a Flux 2 benchmark where we
still see pretty incredible gains and you'll see that they're actually still both speeding ups on even older GPUs like the RTX 3070 and the 4070 TI,
which I made a video about yesterday. Qwen image also has great compatibility here and you might think, "Oh, well, I don't do a lot of generative work
per se." But this also bleeds over into agentic use with multimodal models. So, if you're doing a
lot of processing of images to see what's in them and then reasoning on those results, this GPU could also be a great option. A really popular
configuration that I know of two or three researcher friends of mine use are 5070 TIs or 5060 TIs used in groups of two to four GPUs with vLLM.
They've also gotten really great results and usable results with those GPUs as well. And finally, one of the
coolest models I think you can run in today's age is the three-bit or four-bit quant of Qwen 3.6 27B. Fortunately, this is kind of the Achilles heel
of these models. If you have two of these GPUs, that's fantastic. You can do this just fine. But if you have a single GPU with 16 gigs of VRAM, let's
say it's a 5070 TI, unfortunately, you're going to have to offload the last like 1.92 GB or so onto
your system RAM, but as long as you have a relatively recent system, this is actually not that big of an issue. This GPU is a significant improvement
from the 5060 TI and especially from anything in the 4000 series. The availability and just capability of using models that have been quantized at INV
FP4, I think will continue to be a reason why this GPU is a really good option even compared to the
3090, which unfortunately is now pretty expensive and is really getting old in terms of what you can or can't do with these really cool quantization
options we're seeing with really popular and capable models that are actually competing with some of the latest models from Anthropic that were
released just a few months ago. So, let's look at some pricing. So, buying this GPU new, I would say is a
pretty bad deal. If you go and look at Amazon or eBay, this GPU is basically at $1000 or a little over that and I don't really think that's worth it.
Now, this is significantly more worth it than the 4070 TI because the funny thing is the 4070 TI is still well over $600, really over $800 whenever
you find this. So, what's interesting with this GPU is on eBay, there's a really interesting sweet
spot because most of these listings are under $800 for the most part. There are just more of these in circulation. So, because there are more of them
around, these are actually easier to find at better prices. Now, what I wouldn't do is buy a refurbished one for over $900. I think as these continue
to be more numerous, um $700 range is where I would consider buying these. It's a hard sell because
find a 3090 or two for $1000 And at that point, you're basically just treating in VFP for support with a faster GPU. So, if you're doing a generative
stuff, I think the 5070 Ti even with the goofy pricing is probably still a better option if you're willing to look for it. And let me really quickly
look at sold items so we can see So, this is pretty interesting. So, these when they're sold at a
best offer or in an auction, a lot of these are actually going for under $800. If you kind of stay on eBay long enough, I honestly think you'll have a
pretty good experience finding one of these. So, I'm curious what you guys think about this. How many GPUs are you running? Do you think that this is
a great GPU for local AI, especially for agentic work? Do you think that maybe you should still
just continue buying RTX 3090s even though they're getting a bit more expensive and they lack in VFP for support. So, as always, I hope you guys
learned something from this video. Let me know what you think in the comments. Please like, subscribe, and share, and I'll see you in the next one.