RAM & VRAM for 70B AI Models: A Complete Hardware Guide

8/11/2025

So, you’re looking to get into the big leagues of AI & run a 70-billion parameter model. That’s awesome. These models are seriously powerful, capable of everything from writing nuanced marketing copy to powering incredibly sophisticated, human-like chatbots. But as you've probably figured out, they don't exactly run on a potato.

The single BIGGEST hurdle everyone faces is hardware. Specifically, RAM & VRAM. How much do you really need? The answer, honestly, is "it depends." It’s not a simple number, but a sliding scale based on what you want to do, how patient you are, & of course, your budget.

Let's break it all down, from the bare minimum to the dream setup. I'll give you the insider scoop so you can figure out exactly what you need without wasting a ton of cash.

The Core Problem: Why 70B Models Are So Thirsty

First, let's get a handle on why we're even having this conversation. A 70B model has, you guessed it, 70 billion parameters. Think of parameters as the learned "knowledge" of the model—all the little knobs & dials it has tuned during its training. Every single one of these parameters needs to be stored in memory for the model to work.

Here’s the basic math that dictates everything:

VRAM Required = (Number of Parameters × Bytes per Parameter)

This is the absolute baseline. In a perfect world, you'd have enough super-fast VRAM (Video RAM, the memory on your GPU) to hold the entire model. The "bytes per parameter" part is where it gets interesting, because this is where we can start to cheat.

VRAM: The Penthouse Suite for Your LLM

VRAM is the GPU's dedicated, on-board memory. It's like a penthouse suite for your model—it's incredibly fast & everything the GPU needs is right there. When you run an LLM, the ideal scenario is to load all the model's parameters directly into VRAM. This gives you the fastest possible performance, what they call "inference speed," measured in tokens (parts of a word) per second.

So, how much VRAM for a 70B model? This depends entirely on the model's precision.

The Precision Spectrum: FP32, FP16, & The Magic of Quantization

Precision refers to how much data is used to store each parameter. Think of it like the difference between saving a number as 3.14159265 (high precision) versus just 3.14 (lower precision). Lower precision means less memory, but potentially a small hit to the model's "intelligence."

Here’s what that looks like for a 70B model:

FP32 (32-bit floating point): This is the highest precision & is mostly used for training or scientific research where every decimal point matters. Each parameter takes up 4 bytes.
- 70 billion × 4 bytes = ~280 GB of VRAM.
- Honestly, this is INSANE territory. You're talking multiple, top-tier data center GPUs like the NVIDIA A100 or H100, which cost more than a car. For just running a model (inference), this is total overkill.
FP16 (16-bit floating point): This is the standard for most pre-trained models. It halves the memory requirement with a negligible impact on performance for most tasks. Each parameter takes 2 bytes.
- 70 billion × 2 bytes = ~140 GB of VRAM.
- This is still a massive amount of VRAM. A single NVIDIA A100 (with 80GB) can't hold this. You'd need at least two of them, or some other combination of high-end server GPUs. This is the starting point for serious, professional use.

Okay, so 140GB is still out of reach for most people. This is where quantization comes in & saves the day. Quantization is the process of shrinking the model by reducing the precision even further, usually to integers.

INT8 (8-bit integer): Now we’re talking. Each parameter takes just 1 byte.
- 70 billion × 1 byte = ~70 GB of VRAM.
- This is a HUGE reduction. Now we're in the ballpark of a single, high-end GPU like the NVIDIA A100 (80GB) or an AMD Instinct MI250 (128GB). The performance loss is usually very small & often unnoticeable.
INT4 (4-bit integer): This is the holy grail for running big models on less hardware. Each parameter takes up a measly half a byte.
- 70 billion × 0.5 bytes = ~35 GB of VRAM.
- Suddenly, a 70B model is accessible. This puts it within reach of some prosumer cards like an NVIDIA RTX A6000 (48GB) or a tricked-out setup with two consumer GPUs. For example, two NVIDIA RTX 3090s or 4090s (each with 24GB VRAM) linked together could handle this. This is the most common way enthusiasts & smaller businesses run 70B models locally.

So, for VRAM, the answer is a range: You need anywhere from ~35GB on the absolute low end (with 4-bit quantization) to 140GB for full-precision FP16.

But Wait, There's More: The KV Cache

Just when you thought the math was simple, there's another VRAM hog you need to account for: the KV Cache.

Without getting too technical, the KV cache is a chunk of memory the model uses to remember the conversation it's having. The longer your prompt & the longer its response, the bigger the KV cache gets. For very long conversations or large batch sizes (running multiple prompts at once), the KV cache can become a SIGNIFICANT consumer of VRAM, sometimes adding several gigabytes to your total.

This is a super important point. You might have just enough VRAM to load the model, but as soon as you start a long conversation, you'll run out of memory & everything will crash. As a rule of thumb, you want at least a 1.2x to 1.5x buffer on top of the model's base size to account for this.

System RAM: The Overlooked Hero

While everyone obsesses over VRAM, system RAM (the regular RAM in your computer) is just as critical. If you don't have enough system RAM, you won't even be able to load the model in the first place, let alone run it.

So, how much do you need?

Here’s the thing: system RAM plays a few different roles.

Loading the Model: Before the model even gets to your GPU, it has to be loaded from your hard drive into your system RAM. Even if you're using fancy quantization tools, some of them need a good chunk of RAM to do their work. For some quantization processes, you might need 40-50GB of RAM just for the conversion step.
The CPU's Workspace: Your operating system, background apps, & the program you're using to run the LLM all consume system RAM. You need to have enough headroom for all of that on top of the model itself.
The "Spillover" Zone (This is the important one): What happens if you don't have enough VRAM to hold the entire model? The system tries to be clever & offloads some of the model's layers to your system RAM. This is both a blessing & a curse.
- The Blessing: It allows you to run a model that is technically too big for your GPU. For example, you could run a 4-bit quantized 70B model (~35GB) on a single 24GB GPU by offloading about 11GB of the model to your system RAM.
- The Curse: PERFORMANCE. PLUMMETS. System RAM is connected to the GPU via the PCIe bus, which is DRASTICALLY slower than the GPU's own VRAM. When the model needs a layer that's in system RAM, it has to be shuffled over to the GPU, used, & then maybe shuffled back. This process is incredibly slow. Your token generation speed can drop from a zippy 50-100 tokens/second to a painful 2-5 tokens/second. It feels like going from a fiber internet connection to dial-up.

So, what’s the recommendation for system RAM?

Minimum: 32GB is probably the absolute floor, and you'll be pushing it. You might get away with it for some very specific, optimized setups, but you'll likely run into bottlenecks.
Sweet Spot: 64GB to 128GB is the most common recommendation. This gives you enough room to comfortably load the model, run your OS & other apps, & have some buffer if you need to offload a few layers from VRAM.
Power User / Fine-Tuning: If you're doing any sort of fine-tuning (which is WAY more memory-intensive than just running the model), you'll want 128GB or even more. Training requires memory for not just the model weights, but also gradients, optimizer states, & the training data itself.

My honest advice? Don't skimp on system RAM. If you're investing in the GPUs to run a 70B model, getting at least 64GB of fast DDR4 or DDR5 RAM is a no-brainer.

Putting It All Together: Real-World Scenarios

Let's move from theory to practice. What does this look like for different kinds of users?

Scenario 1: The Enthusiast / Hobbyist

You have a powerful gaming PC with a single high-end GPU, like an NVIDIA RTX 4090 with 24GB of VRAM. Can you run a 70B model?

YES! But with compromises.

You'll need to use a 4-bit quantized model (like a GGUF or GPTQ version) which is around 35GB. Since you only have 24GB of VRAM, you'll have to offload about 11GB of the model to your system RAM. This means you ABSOLUTELY need at least 64GB of system RAM to handle the spillover plus your OS. Your performance won't be blazing fast due to the offloading, but it will be usable, especially for generating text where you don't need instant, real-time responses.

Scenario 2: The Small Business or Startup

You want to use a 70B model to power a customer service chatbot on your website or to automate internal workflows. You need reliable performance & decent speed.

Here, you're looking at a dedicated machine with two NVIDIA RTX 3090s or 4090s. This gives you a combined 48GB of VRAM. This is a GREAT setup because you can load a 4-bit quantized 70B model (~35GB) entirely into VRAM with room to spare for that all-important KV cache. You'll get fantastic inference speeds, making it perfect for real-time applications. For system RAM, 64GB or 128GB is the way to go to ensure everything runs smoothly.

This is where a solution like Arsturn becomes really powerful. You might have the hardware to run the model, but you still need the software to make it useful. For a business, you don't just want a command line interface; you want a polished chatbot that can interact with your website visitors. Arsturn helps businesses create custom AI chatbots trained on their own data. You can feed it your product information, FAQs, & knowledge base, & it uses that to power a 70B model (or another model of your choice) to provide instant customer support, answer questions, & engage with visitors 24/7. It handles the whole user-facing side of things, turning your powerful local model into a real business asset.

Scenario 3: The AI Researcher / Developer

You're fine-tuning a 70B model on your own custom dataset. This is a whole different ballgame.

For fine-tuning, the memory requirements skyrocket. A common technique called LoRA (Low-Rank Adaptation) helps a lot, but you're still looking at some serious hardware. Even with optimizations, fine-tuning a 70B model at FP16 could require around 200GB of VRAM. For full Adam fine-tuning, you're looking at 500GB or more.

This is firmly in the territory of multi-GPU server setups with four or even eight NVIDIA A100s or H100s. And for system RAM, you'd want 128GB at an absolute minimum, with 256GB or more being ideal. This is NOT a casual undertaking.

The Business Case for Getting it Right

For businesses, the question of RAM & VRAM isn't just a technical one; it's a business decision. Under-provisioning your hardware leads to slow, frustrating experiences for your customers. Imagine a chatbot that takes 30 seconds to answer a simple question—that's a lost customer.

This is why many businesses choose a hybrid approach or use platforms that handle the complexity for them. Building and managing the hardware is a significant, ongoing effort. This is another area where platforms can bridge the gap. For instance, Arsturn helps businesses build no-code AI chatbots trained on their own data to boost conversions & provide personalized customer experiences. Whether you're running the model on your own hardware or using a cloud provider, Arsturn provides the crucial conversational AI platform that allows you to build meaningful connections with your audience, turning your powerful LLM into an automated lead generation & support engine.

Final Thoughts & Key Takeaways

So, how much RAM & VRAM do you really need for a 70B model? Here's the cheat sheet:

VRAM is King: Your main goal is to fit the model + KV cache into VRAM.
Quantization is Your Best Friend: For anyone without a data center budget, 4-bit quantization is the key. It brings VRAM needs down from an impossible 140GB to a manageable ~35-40GB.
VRAM Target: For a good experience, aim for at least 48GB of VRAM (e.g., 2x 24GB consumer GPUs). This lets you load the whole quantized model without slow "spillover."
System RAM is the Safety Net: Don't go below 32GB. 64GB is the comfortable minimum, & 128GB is the recommended sweet spot for a smooth experience.
Inference vs. Training: The numbers in this guide are for inference (running the model). Training or fine-tuning requires 4-5x more VRAM & system RAM.

Honestly, diving into the world of 70B models is a super exciting journey. They open up so many possibilities, from creating smarter business automation to just having a ridiculously powerful AI to experiment with. Getting the hardware right is the first & most important step.

Hope this was helpful! It's a complex topic, but once you understand the trade-offs between precision, VRAM, & system RAM, you can make a much more informed decision. Let me know what you think or if you have any questions about your own setup.