Cheapest Way to Run a 70B Model Locally (2026): Dual 3090 vs 5090

The cheapest realistic way to run a 70B model locally at usable speed in 2026 is two used RTX 3090s — 48GB of total VRAM for roughly $1,700-$2,100, the only sub-$2,000 build that holds Llama 3.3 70B at the standard Q4_K_M quant (~42.5GB) entirely in VRAM. A single RTX 5090 is faster per token but only has 32GB, so it physically cannot hold that quant — it has to drop to a tighter 3-bit quant or spill into system RAM. A Mac Studio M3 Ultra (96GB) runs 70B comfortably and silently but starts around $3,999 and has less memory bandwidth than a 3090, which is what sets generation speed. Tesla P40 stacks look tempting at ~$240-$480 a card, but on a dense 70B they crawl — they are a trap for this exact workload.

This guide does the VRAM math first, then compares every cheap path to a local 70B head to head using published specs, street prices, and the memory-bandwidth arithmetic that sets each build's speed ceiling, so you can pick by budget.

How much VRAM does a 70B model actually need?

Start here, because this single number decides everything else. A 70B model's memory footprint is model weights + KV cache, and it scales with the quantization (precision) you pick.

For Llama 3.3 70B, the popular Q4_K_M GGUF weighs about 42.5GB on disk, and you should budget roughly 43-45GB of VRAM once you add a working KV cache for normal context lengths. Longer context grows the KV cache further — at 32K context with an FP16 cache you can add well over 10GB on top of the weights.

The practical takeaways:

1. 48GB is the comfortable floor for a "real" 70B (Q4_K_M with room for context). That is why dual 24GB cards keep coming up.
2. 32GB forces a compromise — you can technically run 70B on a 32GB card, but only at Q3 or below, or by offloading layers to slow system RAM.
3. You do not need an A100. Consumer hardware runs 70B fine; you just need enough total VRAM and enough memory bandwidth to feed it.

Why is dual RTX 3090 the cheapest real 70B build?

Because the used RTX 3090 is the only 24GB card cheap enough to buy two of and still come in under the price of one new flagship. A used 3090 trades around $850-$1,050 in mid-2026, so a pair lands at roughly $1,700-$2,100 and gives you 48GB of pooled VRAM — exactly the comfortable floor for a 70B at Q4_K_M.

What speed to expect, and why: token generation on a local LLM is memory-bandwidth bound — producing one token means reading the entire active weight set out of VRAM. At Q4_K_M that is roughly 0.6GB per billion parameters, so a 70B moves about 42.5GB per token. Divide the card's bandwidth by that figure and you get the arithmetic ceiling. A 3090 has 936 GB/s, so 936 ÷ 42.5 ≈ 22 tok/s, and a layer-split two-card setup inherits that same ceiling, because when the layers are divided between cards only one GPU is reading weights at any instant. Tensor parallelism (what vLLM does) puts both cards to work on the same token, which lifts the paper ceiling toward ~44 tok/s, though cross-card traffic over PCIe eats a meaningful share of it.

Treat both figures as upper bounds you will not hit — they ignore attention over the KV cache, sampling, and framework overhead, and real throughput lands below them. The useful conclusion is that the ceiling is comfortably in interactive territory: text streams faster than you read, even if it is not the instant feel of a 7B on a single card. The 42.5GB model also leaves only a few GB of headroom on 48GB, so keep your context length sane and don't expect to run a second big model alongside it.

What you trade for the low price:

• It is a two-card build. You need a motherboard with two spaced PCIe slots, an 1000W+ PSU, and a case (or open frame) with real airflow. Two 350W cards is ~700W of heat.
• Setup is fiddlier. Tensor parallelism (vLLM) gives the best speed but takes more configuration than single-card Ollama.
• Power draw is real. Under load you are pulling ~700W from the GPUs alone.

Even with those caveats, nothing else matches the dollars-per-usable-70B-token. If your single goal is "run a 70B at a sensible quant for the least money," two used 3090s is the answer. (For why the 3090 is still the value king in general, see our RTX 3090 for local AI deep dive.)

Can a single RTX 5090 run a 70B model?

Not at the standard quant — and this surprises people. The RTX 5090 is a monster on paper: 32GB of GDDR7, about 1,792 GB/s of memory bandwidth (a ~78% jump over the 4090), 21,760 CUDA cores, and a 575W TDP, at a $1,999 MSRP. But that 32GB is the catch. A 70B at Q4_K_M needs ~42.5GB, which does not fit in 32GB.

To run 70B on a single 5090 you have to either:

• Drop to a 3-bit quant (Q3_K_M, ~30-34GB) — fits, with little headroom, at a real quality cost versus Q4.
• Go more aggressive (IQ2, ~26-28GB) — fits comfortably but degrades the model noticeably.
• Offload layers to system RAM — keeps Q4 quality but tanks speed, because the CPU/RAM path is far slower than VRAM.

Where the 5090 shines is everything that actually fits in 32GB: 32B-class models at Q4 (or even Q8), plus image and video generation, where its huge bandwidth and Blackwell compute make it the fastest single consumer card available. So the honest framing is: the 5090 is the best single-card LLM GPU you can buy, but 70B is the one model class where its 32GB ceiling bites. A pair of 3090s costs about the same money, runs slower per token, but holds the full Q4 70B that the 5090 cannot.

Run the same bandwidth ÷ bytes-per-token arithmetic across all four and the speed ordering falls out, using each vendor's published bandwidth figure:

• 2x RTX 3090 — 936 GB/s per card ÷ 42.5GB = ~22 tok/s ceiling layer-split, higher with tensor parallelism.
• 1x RTX 5090 — 1,792 GB/s, but it has to run a ~32GB Q3 quant, so ~56 tok/s ceiling at lower quality. Nothing consumer touches it on raw bandwidth.
• Mac Studio M3 Ultra — 819 GB/s ÷ 42.5GB = ~19 tok/s ceiling, just under a 3090 pair.
• 2x Tesla P40 — 346 GB/s ÷ 42.5GB = ~8 tok/s ceiling, and Pascal's missing Tensor Cores mean it falls well short of even that.

These are arithmetic upper bounds, not measurements. Every real setup lands below them; the value of the numbers is the ratio between builds, which is what should drive the buying decision.

Is a Mac Studio a better 70B machine?

For a lot of people, yes — just not the cheapest one. The Mac Studio M3 Ultra starts at $3,999 with 96GB of unified memory (the 60-core-GPU, 28-core-CPU base), and that unified pool means the GPU can address the full 96GB. A 70B at Q4_K_M (~42.5GB) fits with enormous headroom, so you can even step up to Q5 or Q8 for better quality, and run other models alongside it.

The trade-off is speed. Apple publishes the M3 Ultra's memory bandwidth as 819 GB/s, below a 3090's 936 GB/s and far below the 5090's 1,792 GB/s — and because token generation is bandwidth-bound, that spec is the ranking. Running the same arithmetic (819 ÷ 42.5GB) puts the Mac's ceiling at about 19 tok/s on a Q4_K_M 70B, a shade under a 3090 pair and the slowest of the three "real 70B" options here. Note that the ceiling moves with the quant: step up to Q5 or Q8 for the quality the extra memory buys you and you are moving more bytes per token, so the ceiling drops proportionally. Apple's MLX framework generally extracts more from the same hardware than llama.cpp's Metal backend, so it is worth trying first.

So the Mac Studio wins on everything except price and raw speed: it is silent, sips power (no 700W space heater), needs zero multi-GPU setup, fits a ton of VRAM in a tiny box, and runs 70B at higher quality than a 48GB build because it has room to spare. If your budget reaches ~$4,000 and you value simplicity and quiet over the last few tokens per second, it is arguably the better machine — it is just not the cheapest way to a 70B.

Are Tesla P40 stacks the cheapest path?

On paper they look unbeatable: a used Tesla P40 gives you 24GB for roughly $240-$480, so two of them is 48GB for as little as ~$500-$960 — cheaper than a single 3090. For small models that math is great. For a dense 70B, it falls apart.

Start with the bandwidth. NVIDIA's P40 datasheet lists 346 GB/s, roughly a third of a 3090's 936 GB/s, which alone caps a Q4_K_M 70B at about 8 tok/s before anything else is accounted for. Then the architecture makes it worse: the P40 is Pascal silicon from 2016 with no Tensor Cores and very weak FP16 throughput, so it misses nearly every modern inference optimization and falls well short of even that 8 tok/s ceiling. Prompt processing suffers hardest, because that phase is compute-bound rather than bandwidth-bound — which is exactly where the missing Tensor Cores bite. Add slow PCIe interconnect and heavy cross-GPU traffic on a two-card split and a dense 70B becomes something you wait on rather than converse with. The P40's real sweet spot is models up to about 8B (and MoE models, which activate only a slice of their weights per token); dense models past ~13B hit hard diminishing returns.

There are also hidden costs: P40s are passively cooled server cards, so you need to rig 3D-printed shrouds and blower fans, and they want extra PCIe power adapters. Add that to the time you will spend fighting the setup, and the "cheap" 48GB stack is a false economy for this workload.

Verdict: P40 stacks are a fun budget play for 7B-13B experimentation, but they are the wrong tool for a 70B. If you genuinely want 70B, the extra money for dual 3090s buys you 2.7x the memory bandwidth plus the Tensor Cores that Pascal never had — a different class of machine, not a slightly faster one. (We dig into where the P40 does make sense in our Tesla P40 local LLM guide.)

Which 70B build should you buy at your budget?

• Under ~$1,000 — don't force a 70B. A single used 3090 (24GB, ~$850-$1,050) runs 32B-class models beautifully and is the smarter buy. A P40 stack will "fit" 70B but run it at unusable speed. Honestly, run a great 32B and revisit 70B later.
• ~$1,700-$2,100 (the value pick) — two used RTX 3090s. 48GB, full Q4_K_M 70B in VRAM, a ~22 tok/s bandwidth ceiling. The cheapest path to a real, usable local 70B. Best dollars-per-token by a wide margin.
• ~$2,000 single card — RTX 5090. Buy this if your main workloads are 32B LLMs, image, or video, and you treat 70B as occasional (at Q3). Fastest single card by a distance; just know 32GB can't hold a full-quality 70B.
• ~$3,500-$4,500, want simplicity — Mac Studio M3 Ultra 96GB. Silent, low-power, one box, runs 70B at higher quality (Q5/Q8) with room to spare — at a slightly lower bandwidth ceiling than a 3090 pair. Pay more, fuss less.
• Building a multi-GPU rig anyway — scale 3090s. Three or four 3090s (72-96GB) open up 70B at Q8 or even 100B+ models, still at a fraction of datacenter-card prices.

The blunt summary: for the cheapest usable local 70B in 2026, buy two used RTX 3090s. Step up to a Mac Studio if you want quiet and simplicity, reach for a 5090 if 70B isn't really your main job, and skip P40 stacks for dense 70B entirely.

Key Takeaways

1. 48GB is the comfortable floor for a real 70B. A 70B at Q4_K_M is ~42.5GB and wants ~43-45GB of VRAM with KV cache — so you need 48GB to run it well.
2. Dual used RTX 3090 is the cheapest usable 70B build — ~$1,700-$2,100 for 48GB, with a ~22 tok/s bandwidth ceiling on Llama 3.3 70B Q4_K_M that tensor parallelism (vLLM) pushes higher than a layer split (Ollama) does.
3. A single RTX 5090 cannot hold a 70B at Q4 (32GB vs ~42.5GB needed). It is the fastest single card and superb for 32B models and image/video — but 70B forces Q3 or offloading.
4. Mac Studio M3 Ultra 96GB runs 70B at higher quality and zero fuss but starts at ~$3,999 and its 819 GB/s of bandwidth puts it just below a 3090 pair — the slowest of the three "real 70B" options.
5. Tesla P40 stacks are a trap for dense 70B. Cheap 48GB (~$500-$960), but 346 GB/s of bandwidth and no Tensor Cores make a 70B crawl — the card is only worth it up to ~8B.

Next Steps

• Read why a used 3090 is the value king before you buy two: RTX 3090 for local AI.
• See the whole consumer lineup ranked by VRAM and tok/s in Best GPUs for AI, from the RTX 3060 up to the 5090.
• Planning to actually run it? The Llama 3.3 70B model page has the quant sizes and hardware notes for this exact model.
• Tempted by a cheap P40 stack? Read the honest limits first: Tesla P40 local LLM guide.
• Not sure which card fits your budget and models? Use our Which GPU to buy interactive picker.

For raw specs from the source, NVIDIA publishes the full RTX 3090 spec sheet, and the open-source llama.cpp project is the easiest way to benchmark any of these builds yourself with consistent quantization.