Running 70B Models Locally — Exact VRAM by Quantization

📚 More on this topic: VRAM Requirements Guide · Quantization Explained · Multi-GPU Guide · Mac vs PC for Local AI · Used RTX 3090 Buying Guide

Running a 70B model locally is the line between “hobby” and “serious local AI.” On the other side of that line is reasoning that competes with GPT-4 and the ability to process complex problems without sending your data to the cloud.

The barrier is VRAM. A 70B model at full precision needs 141GB of memory. No consumer GPU comes close to that. Quantization brings it down to 43GB at Q4, which still won’t fit on a single RTX 4090 or 3090. You need either two GPUs, a Mac with enough unified memory, or a workstation-class card.

This guide gives you exact VRAM numbers at every quantization level, which hardware setups actually work, realistic speed expectations, and an honest assessment of when 70B is worth the investment versus running a 32B model instead.

The 70B Math

The formula is simple:

VRAM (GB) = Parameters (billions) × Bytes per parameter

At FP16 (2 bytes per parameter): 70B × 2 = 140GB. That’s the model weights alone. Context and framework overhead are extra.

Quantization compresses those weights:

*Overhead includes KV cache at 4K context, framework memory, and CUDA/Metal context. Real GGUF files are slightly larger than the theoretical minimum due to metadata and mixed-precision layers.

The theoretical calculation gets you in the ballpark. Real file sizes are what matter. See the next section.

Exact VRAM: Llama 3.3 70B and Qwen 2.5 72B

These are the two 70B-class models most people run locally. Numbers from actual GGUF builds on HuggingFace:

Llama 3.3 70B Instruct

Qwen 2.5 72B Instruct

Qwen 2.5 72B is about 10-15% larger than Llama 3.3 70B at the same quantization because it has 72 billion parameters versus 70.6 billion, plus slightly different architectural choices. Both produce similar quality at the same quant level.

For a deeper understanding of what these quantization levels mean and how they affect output quality, see our quantization explainer.

Context Length Eats Your VRAM

The tables above assume 4K or 8K context. But Llama 3.3 supports 128K tokens and Qwen 2.5 72B supports 128K too. The KV cache (where the model stores attention state for the conversation) grows linearly with context length.

KV Cache VRAM at 70B Scale

At 32K context with FP16 KV cache, you’re adding 14GB on top of the model weights. On a dual RTX 3090 setup (48GB total) running Llama 70B Q4_K_M (42.5GB file), that leaves about 5.5GB for everything. With a 14GB KV cache, you’re already over.

This is why most 70B setups run with 4K-8K context and why the 128K advertised context length is mostly theoretical for consumer hardware. You can extend it with quantized KV cache (Ollama and llama.cpp both support this), but even then, 32K+ context on 48GB total VRAM is tight.

Hardware That Can Actually Run 70B

Single Consumer GPUs: Mostly Can’t

The RTX 5090 is the only consumer GPU that can load a 70B model at all. Q3_K_M (34.3GB file) fits with about 4K context, but quality degrades noticeably at Q3 and you have zero headroom. It’s a proof-of-concept, not a daily driver.

Dual GPU Setups

This is where 70B becomes practical on consumer hardware. Two GPUs pool their VRAM.

Dual RTX 3090s ($1,700 total) is the budget path. 48GB runs Llama 3.3 70B at Q4_K_M with 4-8K context. You get 16-21 tokens per second, which is readable but noticeably slower than the 40+ tok/s you’d get from a 32B model on a single card. See our multi-GPU guide for setup instructions.

Dual RTX 5090s ($4,000+) with 64GB total opens up longer context. Q4_K_M with 16-32K tokens is comfortable, and Q5_K_M becomes viable for better quality.

Both setups require a motherboard with two PCIe x16 slots (or at least x16 + x8), a 1000W+ power supply, and good airflow. Two 3090s at full inference draw 700+ watts combined.

Workstation / Datacenter GPUs

The A6000 at $2,200 used gives you the same 48GB as dual 3090s in a single card, no multi-GPU hassle. But it’s slower for inference (smaller memory bandwidth) and costs $500 more.

Mac (Unified Memory)

This is where Macs win. Unified memory lets the entire RAM pool serve as model memory.

Mac speeds are slower than dual NVIDIA GPUs because unified memory bandwidth (273-546 GB/s) is lower than GDDR6X (936 GB/s per 3090). But the Mac loads the model at all, which a single 24GB GPU can’t. And it does it silently, at 15 watts idle.

The M4 Max 128GB at $4,100 is the most comfortable 70B experience. Q6_K with long context, no fan noise, no multi-GPU setup. The tradeoff is speed. See our Mac vs PC comparison for the full breakdown.

Speed Expectations

70B models are slow. Set your expectations accordingly.

For comparison, Qwen 3 32B at Q4_K_M on a single RTX 3090 runs at 35-45 tok/s. A 70B model on dual 3090s runs at about half that speed while costing twice the hardware.

CPU offloading (splitting the model between GPU and system RAM) technically works but is painfully slow. The PCIe bus becomes the bottleneck, dropping generation to 1-5 tok/s. At that speed, you’re waiting 10-20 seconds for a single sentence. It’s fine for testing. It’s not usable for daily work.

Quality vs Quantization at 70B

Good news: 70B models tolerate quantization better than smaller models. Research confirms that models above 30B parameters retain ~99% of FP16 accuracy at 4-bit quantization, while 7B models lose 2-5%.

Quality by Quant Level

Q4_K_M is the recommendation for almost everyone running 70B locally. The 3-5% quality loss versus FP16 is barely perceptible in normal use. You’d need benchmark suites to measure the difference reliably. The VRAM savings (142GB down to 43GB) make it the only practical option on consumer hardware.

Q3_K_M is where you start noticing. Math problems that Q4 handles cleanly will occasionally fail at Q3. Multi-step reasoning chains break more often. If you’re running on an RTX 5090 and Q3 is your only option, it works. Just know you’re leaving quality on the table.

Q2_K is not worth running. At 70B, even the higher quantization tolerance can’t save Q2 from significant output degradation. If Q2 is your only option, run a 32B model at Q4 instead. You’ll get better results.

When 70B Is Worth It

Run 70B For:

Complex reasoning. Multi-step logic problems, mathematical proofs, scientific analysis. The gap between 32B and 70B is widest here. A 70B model at Q4 catches errors and follows chains of reasoning that a 32B model misses.

Deep research and analysis. Summarizing long documents, comparing multiple sources, identifying inconsistencies. 70B models have broader knowledge and make fewer factual errors.

Nuanced writing. When you need precise tone control, subtle arguments, or professional-grade output. 70B models handle ambiguity and subtext better.

Skip 70B For:

Quick chat and Q&A. A 32B model answers “what’s the capital of France” just as correctly, 3-4x faster.

Simple code generation. For boilerplate, function scaffolding, and straightforward coding tasks, 32B coding models (Qwen 2.5 Coder 32B, DeepSeek-Coder-V2) are more than sufficient and much faster.

Anything speed-sensitive. If you need responses in under 2 seconds, 70B won’t deliver. A 32B model at 40 tok/s starts generating immediately. A 70B model at 15 tok/s has noticeable latency.

The 32B Alternative

This is the honest question: in 2026, do you need 70B?

The gap has narrowed. Qwen 3 32B and DeepSeek-R1-Distill-Qwen-32B compete with 70B models on many benchmarks while using less than half the VRAM. On creative writing, Qwen 3 32B actually gets 85% human preference over larger models. On coding, DeepSeek R1 Distill 32B leads Llama 3.3 70B on several benchmarks.

70B still wins on complex multi-step reasoning and factual depth. If that’s your primary use case, the hardware investment is justified. For everything else, a 32B model on a single GPU is faster and cheaper, with nearly the same quality.

Bottom Line

Running 70B locally requires either dual GPUs (2× RTX 3090 at $1,700), a Mac with 64GB+ unified memory ($2,000+), or a datacenter card. Q4_K_M is the quantization sweet spot: 43GB for Llama 3.3 70B, excellent quality retention. Below Q4, quality drops noticeably. Below Q3, don’t bother.

The practical setup for most people: dual RTX 3090s with Llama 3.3 70B at Q4_K_M. You get 16-21 tok/s with 4-8K context. It’s slower than a 32B model and costs twice the hardware. But for complex reasoning and research, the quality difference is real.

If you’re not sure whether you need 70B, start with Qwen 3 32B on a single 24GB GPU. It handles 80-90% of tasks just as well. Upgrade to 70B when you consistently hit the quality ceiling on reasoning-heavy work.

Related Guides

• How Much VRAM Do You Need? — full VRAM chart for every model size
• What Quantization Actually Means — how Q4, Q6, Q8 affect quality
• Multi-GPU Local AI — tensor vs pipeline parallelism, setup guides
• Used RTX 3090 Buying Guide — the best 24GB GPU for budget AI
• Mac vs PC for Local AI — unified memory vs discrete VRAM
• Llama 3 Guide: Every Size — which Llama to pick
• Building a Distributed AI Swarm — multi-node alternative
• What Can You Run on 24GB VRAM? — the 32B sweet spot