Llama 70B is the model that separates people who have read the marketing from people who have read the memory requirements. A 70-billion-parameter model sounds like something you can simply download and run, and on a powerful desktop the assumption feels reasonable. Then you try, the loader complains about memory, and you discover that the gap between "I have a strong GPU" and "I can run a 70B model" is wider than almost anyone expects.
This guide is the honest version. We will do the actual VRAM maths, show you the rig that genuinely runs a 70B model locally in Nigeria, and explain the cheaper-but-slower alternative so you can decide with open eyes. If you are still working out your card, start with what GPU VRAM you actually need in 2026 and the used-GPU market for RTX 30 versus 40 series in Nigeria, because both feed directly into the decision below.
The VRAM maths nobody shows you
A model's weight memory is roughly its parameter count multiplied by the bytes used per parameter. At FP16, the format these models are typically trained and released in, each parameter takes 2 bytes. So a 70B model at FP16 needs roughly 70 billion times 2 bytes, which is about 140GB just for the weights. No consumer GPU on earth has 140GB of VRAM. The largest consumer card tops out at 24GB. The maths simply does not close.
This is why quantization is not optional for local 70B, it is mandatory. Quantization stores each weight using fewer bits, shrinking the model dramatically. A 4-bit quant, commonly written as Q4, brings a 70B model down to roughly 35 to 45GB depending on the exact quant variant. Add headroom for the KV cache, which grows with your context length, and you are looking at needing roughly 40 to 48GB of VRAM to run a Q4 70B comfortably on the GPU. If quantization is a new idea, our explainers on quantization for local AI models and GGUF Q4, Q5 and Q8 quants unpack exactly what is being traded away.
The classic local 70B rig: two 24GB cards
Here is the reality that the single-GPU dreamers do not want to hear: a single 24GB card cannot hold a 70B model at a usable quant in VRAM alone. Not an RTX 4090, not an RTX 3090. The model does not fit, full stop. To get to the roughly 48GB you need, you combine two 24GB cards.
This is why a pair of used RTX 3090s has been the budget-favourite local 70B rig for years. Two 3090s give you 48GB of combined VRAM, which holds a 4-bit 70B with comfortable room for context. The 3090 is also one of the few consumer cards with NVLink, and even without it the framework can split layers across both cards. For the full build walkthrough, see our dual-GPU AI rig build, step by step.
The 3090 stays the pick over two 4090s for one stubborn reason: both have 24GB, so two of either gives the same 48GB, and used 3090s cost dramatically less. You are buying VRAM capacity, not raw speed, and on a per-gigabyte basis the 3090 wins decisively on the Nigerian used market.
The offload alternative: cheaper, much slower
If two GPUs are out of budget, there is a second path. With llama.cpp you can run a 70B model by keeping some layers in VRAM and offloading the rest to system RAM. You will want 48 to 64GB or more of system RAM to hold the overflow, and a single 24GB card to take whatever layers fit.
The honest catch is speed. System RAM has far lower memory bandwidth than GPU VRAM, and bandwidth is what determines how fast tokens come out. Every layer that lives in RAM instead of VRAM drags the whole model down to the speed of the slowest path. Our piece on GPU memory bandwidth explains why this gap is so punishing. The trade is simple: offload is cheap but slow, dual-GPU is dearer but genuinely interactive.
Quant level versus quality
You can shrink the VRAM requirement further by dropping to a lower quant, but you pay for it in quality. The general shape is this:
- Q8 stays very close to the full model in quality but is large, too large for a 70B to fit a dual-24GB rig comfortably.
- Q5 is a strong middle ground when you have the VRAM to spare.
- Q4_K_M is the common sweet spot for local 70B: small enough to fit 48GB with context, good enough that quality loss is hard to notice in everyday use.
- Q3 and below shrink things further but the quality drop becomes noticeable, and for a 70B it is rarely worth it when Q4 already fits.
For most people running a 70B on two 3090s, Q4_K_M is the default starting point and there is little reason to deviate.
The three options, compared in Naira terms
Here is how the realistic paths stack up, with rough cost tiers for the Nigerian used market and the power note that NEPA forces you to take seriously:
- Dual RTX 3090, 48GB total — the proper local 70B rig. Highest cost of the three, but the only path that runs a Q4 70B entirely in VRAM at genuinely interactive speed. Two 3090s draw serious wattage, often pulling well past 600W under load between them, so budget for a 1000W or larger quality PSU and a UPS sized to ride out NEPA cuts without a hard shutdown mid-generation.
- Single RTX 4090 plus RAM offload — the compromise. Lower hardware cost than two cards, runs a 70B but slowly because of the offloaded layers. The 4090 is fast, yet it cannot show that speed when most of the model lives in system RAM. Single-card power draw is far easier on your PSU and UPS.
- CPU and RAM offload only — the cheapest, slowest path. No high-end GPU at all, just a lot of system RAM. It will technically run a 70B but at speeds that test your patience for anything interactive. Fine for batch jobs you can leave running, frustrating for chat.
For exact throughput expectations, read the tokens-per-second realities of local LLM benchmarks rather than trusting any single quoted figure, because real speed depends heavily on your context length, quant and exact hardware.
What a single 24GB card should actually run
If you own one RTX 4090 or 3090, do not force a 70B onto it. That card is far better spent on smaller models that fit comfortably in 24GB: anything in the mid-30-billion range and below runs cleanly, quickly and at higher quant. You get faster responses, better quality per parameter and none of the offload pain. Reserve the 70B ambition for when you can add the second card, and in the meantime a single strong GPU is a genuinely capable local AI machine. If you are serving these models to a team, our guide to local LLM serving with vLLM hardware in Nigeria covers the next step up.
Frequently Asked Questions
Can I run Llama 70B on a single RTX 4090? Not entirely in VRAM. A single 4090 has 24GB, and even a 4-bit 70B needs roughly 40 to 48GB. You can run it with llama.cpp by offloading layers to system RAM, but it will be much slower than a proper dual-GPU rig. For pure-VRAM speed on a single 4090, stick to smaller models.
Why two RTX 3090s instead of two 4090s? Both cards have 24GB, so a pair of either gives you the same 48GB of combined VRAM. Since running a 70B is about VRAM capacity rather than raw compute, used 3090s deliver the same memory for far less money on the Nigerian market, making them the long-standing value pick.
How much does quantization hurt the quality? Less than you would fear at Q4 and above. Q4_K_M is the usual balance, with quality loss that is hard to notice in everyday use, while halving the memory versus FP16. Drop to Q3 or lower and the degradation becomes visible, so for a 70B that already fits at Q4 there is rarely a reason to go further.
The One Thing to Remember
Running a 70B locally is a VRAM problem before it is anything else. The model needs roughly 40 to 48GB at a usable 4-bit quant, a single consumer card gives you 24GB, and that gap is why two 24GB cards, classically a pair of used 3090s, is the real answer. Everything else, offload included, is a trade of speed for the VRAM you did not buy.
Not sure which path fits your budget and your power situation? Spec a dual-GPU or single-card local AI build with our configurator, or contact our team and we will size the VRAM, PSU and UPS around the model you actually intend to run.