Llama 3.3 70B Hardware Requirements
GPT-4-class quality in a dense 70B you can own — if you have ~44GB of VRAM. Modern MoE models deliver similar quality in a third of the memory, but the 70B remains the classic dual-3090 flex.
VRAM needed (Q4, 8k context)
46.1 GB
No single consumer GPU fits this model — see multi-GPU and Mac options below.
Updated July 2026. Estimates — see methodology below.
VRAM by Quantization
Weights + KV cache at 8k context + 1.2GB system overhead. Q4_K_M is the community default — quality loss is negligible for most use.
| Quantization | Weights | KV cache (8k) | Total VRAM | Cheapest GPU that fits |
|---|---|---|---|---|
| Q4_K_M Recommended — near-lossless for most use, half the size of Q8 | 42.4 GB | 2.5 GB | 46.1 GB | Multi-GPU / Mac territory |
| Q5_K_M Slightly higher quality than Q4 for ~18% more VRAM | 49.9 GB | 2.5 GB | 53.6 GB | Multi-GPU / Mac territory |
| Q8_0 Effectively lossless — use if you have VRAM to spare | 74.4 GB | 2.5 GB | 78.1 GB | Multi-GPU / Mac territory |
| FP16 Full precision — only for fine-tuning or maximum fidelity | 140 GB | 2.5 GB | 144 GB | Multi-GPU / Mac territory |
Longer context costs VRAM
KV cache grows linearly with context: 8k → 2.5 GB · 32k → 10.0 GB · 128k → 40.0 GB. If you plan to feed whole documents or codebases, size your GPU for the context you actually need, not just the weights.
GPU Compatibility (Q4, 8k context)
Every GPU in our database, scored against Llama 3.3 70B. Speed is estimated decode rate — memory-bandwidth-bound, so VRAM and bandwidth matter more than shader count.
| GPU | VRAM | Verdict | Est. speed | Price | |
|---|---|---|---|---|---|
| RX 7900 XTX | 24 GB | Partial offload | — | ~$838 used | |
| RTX 3090 | 24 GB | Partial offload | — | ~$1,150 used | |
| RTX 4090 | 24 GB | Partial offload | — | ~$2,375 used | |
| RTX 5090 | 32 GB | Partial offload | — | from $2,800 | |
| RTX 3060 | 12 GB | Not enough VRAM | — | ~$238 used | |
| Arc B570 | 10 GB | Not enough VRAM | — | from $225 | |
| RTX 4060 | 8 GB | Not enough VRAM | — | ~$275 used | |
| RX 7600 | 8 GB | Not enough VRAM | — | from $250 | |
| Arc B580 | 12 GB | Not enough VRAM | — | from $250 | |
| RX 6700 XT | 12 GB | Not enough VRAM | — | ~$315 used | |
| Arc A770 | 16 GB | Not enough VRAM | — | from $300 | |
| RTX 4060 Ti | 8 GB | Not enough VRAM | — | ~$338 used | |
| RTX 3070 | 8 GB | Not enough VRAM | — | ~$338 used | |
| RTX 5060 | 8 GB | Not enough VRAM | — | from $325 | |
| RX 7700 XT | 12 GB | Not enough VRAM | — | ~$415 used | |
| RX 6800 XT | 16 GB | Not enough VRAM | — | ~$438 used | |
| RTX 3080 | 10 GB | Not enough VRAM | — | ~$463 used | |
| RX 7800 XT | 16 GB | Not enough VRAM | — | ~$488 used | |
| RTX 4070 | 12 GB | Not enough VRAM | — | ~$500 used | |
| RTX 4070 SUPER | 12 GB | Not enough VRAM | — | ~$563 used | |
| RX 7900 XT | 20 GB | Not enough VRAM | — | ~$588 used | |
| RTX 5060 Ti | 16 GB | Not enough VRAM | — | from $550 | |
| RX 9070 | 16 GB | Not enough VRAM | — | from $575 | |
| RTX 5070 | 12 GB | Not enough VRAM | — | from $600 | |
| RX 9070 XT | 16 GB | Not enough VRAM | — | from $600 | |
| RTX 4070 Ti SUPER | 16 GB | Not enough VRAM | — | ~$750 used | |
| RTX 4080 SUPER | 16 GB | Not enough VRAM | — | ~$900 used | |
| RTX 5070 Ti | 16 GB | Not enough VRAM | — | from $900 | |
| RTX 5080 | 16 GB | Not enough VRAM | — | from $1,250 |
Run it in one command
With Ollama installed, this pulls the default quant and starts chatting:
Frequently Asked Questions
How much VRAM do I need to run Llama 3.3 70B?+
At the recommended Q4_K_M quantization with 8k context, Llama 3.3 70B needs roughly 46.1GB of VRAM (42.4GB weights + KV cache + overhead). Q8 needs about 78.1GB and full FP16 about 144GB.
Can any single consumer GPU run Llama 3.3 70B?+
No single consumer GPU currently has enough VRAM to run Llama 3.3 70B fully. Realistic options: a multi-GPU rig (e.g. dual 24GB cards), a Mac with enough unified memory, or a 128GB Ryzen AI Max "Strix Halo" mini-PC — the 2026 favorite for exactly this class of model.
Can I run Llama 3.3 70B on an RTX 3060?+
No — 12GB is well below what Llama 3.3 70B needs even at Q4 quantization.
Can I run Llama 3.3 70B on a Mac?+
Yes, if the Mac has enough unified memory: budget roughly 46.1GB of RAM for the Q4 version (plus what macOS itself uses). Apple Silicon runs GGUF models well via Ollama or LM Studio.
Can I run Llama 3.3 70B on CPU only?+
Technically yes with enough system RAM, but a dense 70B model on CPU is slow — usually a few tokens/sec at best. Fine for testing, painful for daily use.
Is Llama 3.3 70B free for commercial use?+
Yes. Llama 3.3 70B is released under the Llama 3.3 Community License, which permits commercial use.
Related Models
How we calculate these numbers
VRAM = model weights (parameters × bits per weight ÷ 8) + KV cache (architecture-specific bytes per token × context length) + ~1.2GB runtime overhead. Speed estimates assume decode is memory-bandwidth-bound at ~50% utilization (lower for MoE models, which pay routing overhead), matching typical llama.cpp performance on consumer cards; real results vary with runtime, drivers, and settings. Quant sizes reflect GGUF K-quants, which keep some layers at higher precision. Figures are estimates for planning, not guarantees — when in doubt, buy more VRAM than you need today. Prices shown are launch MSRP; mid-2026 street prices often run well above MSRP due to the ongoing memory shortage, and used 24GB cards are holding their value unusually well.