Running a 70B-parameter model locally usually means finding somewhere between 40 and 48GB of memory, once you've quantized it down from its full size. Meta's Llama 3.3 70B at Q4_K_M — the quantization level most people actually run — lands right around 42GB. That number rules out almost every single consumer GPU on its own, which is exactly why '70B locally' has a reputation for being expensive. It doesn't have to be. Here are four real ways to hit that 42GB, priced out with actual August 2026 numbers, cheapest to most convenient.
Path 1: two used RTX 3090s
This is the value pick, and it's not close. A single used RTX 3090 is running $800 to $1,100 depending on condition, per pricing trackers, and two of them give you 48GB of VRAM split across the cards — enough to load a 42GB quantized 70B model with headroom for context. Add a motherboard with two well-spaced PCIe slots, a beefy enough PSU, and a case with real airflow, and you're looking at roughly $2,000 to $2,800 all-in for a rig you own outright. The catch is that splitting a model across two GPUs needs a backend that actually supports tensor-parallel or layer-split inference — see our guide on whether you need two GPUs at all before you commit to this path.
Path 2: rent it by the hour
If you want to run a 70B model today, for one project, without buying anything, this is the answer. A single 80GB Nvidia A100 rents for about $1.39-1.49 an hour on RunPod, with plenty of headroom over the 42GB the model actually needs. Vast.ai runs a competing marketplace where even RTX 3090 instances start around $0.25/hour in off-peak, interruptible listings, though you'd need to rent two of them simultaneously to match the 48GB path above. Either way, an evening of testing costs less than a fast-food meal.
$1.39-1.49/hr
A100 80GB rental
RunPod on-demand
from $0.25/hr
Used RTX 3090 rental
Vast.ai, interruptible
~42GB
70B Q4 model size
Llama 3.3 70B, Q4_K_M
~$2,000-2,800
Dual-3090 rig, one-time
cards + platform
Path 3: a Mac Studio
This is the path that got more expensive this year, not less. Apple raised the Mac Studio M4 Max from $1,999 to $2,499 and the M3 Ultra from $3,999 to $5,299 in a June 2026 price hike tied to the same memory shortage squeezing GPU prices — full breakdown here. The M4 Max now tops out at 64GB of unified memory, which is genuinely tight for a 42GB model plus OS overhead and context; the safer buy is the M3 Ultra's 96GB configuration, which now starts at $5,299 after Apple also deleted its 256GB and 512GB memory tiers this year. It's quiet, it's efficient, and it's the most expensive of the four paths by a wide margin.
Path 4: the cheap-and-ugly option
For buyers who want dedicated VRAM at the lowest possible price and can tolerate real compromises — no display output on some cards, driver friction, higher idle power — a pair of used Tesla P40s is worth a look. We've covered whether a single used P40 is worth it in detail; the short version is 24GB for around $300 with real caveats, which undercuts even the RTX 3090 path if you're willing to deal with them.
Renting a single 80GB A100 is the cheapest way to try a 70B model before buying anything. · Unsplash
The breakeven math
Cost after 6 months of daily use (~4 hrs/day, ~730 hours)
A100 rental (RunPod)~$1,030
Dual RTX 3090 rig~$2,400 (one-time)
Mac Studio M3 Ultra 96GB~$5,299 (one-time)
That table is the whole decision in one picture: at light, occasional use, renting wins outright and the hardware never pays for itself. Cross into daily, heavy use and the dual-3090 rig overtakes rental within the first year, then keeps paying you back for as long as the cards last. The Mac Studio doesn't win on pure cost at any point in this comparison — it wins on noise, power, and desk space, which is a real trade for some buyers and irrelevant to others.
Can I run a 70B model on a single RTX 3090?
Not at Q4 — 24GB isn't enough for a ~42GB model even before context overhead. You'd need to drop to a much more aggressive quantization that hurts quality, or use a smaller model instead; see our quantization guide.
Is quantizing a 70B model down to Q4 a big quality hit?
It's noticeable but generally acceptable for most use cases — Q4_K_M is the community-standard middle ground for exactly this reason, trading some precision for a model that fits on affordable hardware.
What about renting instead of buying long-term?
Below roughly 4-6 hours of daily use, cloud rental is typically the cheaper option over a GPU's useful life; above that, owned hardware wins. Match the choice to how often you'll actually run the model, not how often you think you might.
There's no single right answer here — the cheapest option depends entirely on whether you're testing an idea for one night or running a model in production every day. What's changed in 2026 is that all four paths got more expensive at the same time, for the same underlying reason: memory. If you're leaning toward buying rather than renting, our current GPU picks are the next thing worth reading.
