GPUFits
Models / Llama 3.3 70B
Llama 3.3 70B VRAM Requirements & GPU Pairing
Llama 3.3 70B is Meta's open dense flagship: 70.6B parameters, 80 layers, 8 KV heads, 128K context, with official instruction-following quality claimed at Llama 3.1 405B level. Its hardware threshold is the classic dividing line on this site: Q4_K_M @8K totals ~47.4GB — only the 48GB RTX A6000 barely holds it single-card. Realistic consumer paths are 2×24GB (3090/4090 layer splitting) or 64GB+ Apple unified memory (M4 Max 64GB tight, M3 Ultra 96GB comfortable).
Speed follows the ~43GB of weights: dual 3090s theoretically ~28 tok/s, a single 4090 cannot fit it, an M3 Ultra ~14 tok/s — readable but not fast. License is llama3.3-community. It is the standard answer to “the strongest dense general model I can run locally”; anything above it enters MoE (gpt-oss-120b) or distilled territory (the R1-Distill family).
Architecture Specs
| Total parameters | 70.6B |
| Active parameters | 70.6B |
| Layers | 80 |
| KV heads | 8 |
| Head dim | 128 |
| Native context | 128K |
| License | llama3.3-community |
| KV bytes per token (fp16) | 320.0 KB |
VRAM by Quant Tier (@8K context, fp16 KV)
| Quant | Weights | Total (incl. 1.5GB runtime overhead) |
|---|---|---|
| Q8_0 | 75.1 GB | 79.3 GB |
| Q6_K | 58.0 GB | 62.2 GB |
| Q4_K_M | 43.2 GB | 47.4 GB |
| Q3_K_M | 35.3 GB | 39.5 GB |
| Q2_K | 28.0 GB | 32.2 GB |
KV cache and runtime overhead do not change across quants; longer contexts grow the KV part linearly — adjust context length in the GPU checker tool.
GPU Verdict Matrix (Q4_K_M @8K)
| GPU | Usable VRAM | Verdict | Recommended quant | Theoretical speed | |
|---|---|---|---|---|---|
| RTX 3060 12GB | 12.0 GB | Not feasible | needs 4 cards | — | Try it → |
| RTX 3090 | 24.0 GB | Not feasible | needs 2 cards | — | Try it → |
| RTX 4070 Ti Super | 16.0 GB | Not feasible | needs 3 cards | — | Try it → |
| RTX 4090 | 24.0 GB | Not feasible | needs 2 cards | — | Try it → |
| RTX 5090 | 32.0 GB | Not feasible | needs 2 cards | — | Try it → |
| RTX A6000 | 48.0 GB | Tight fit | Q4_K_M | ≈13 tok/s | Try it → |
| A100 80GB | 80.0 GB | Comfortable | Q8_0 | ≈20 tok/s | Try it → |
| H100 80GB | 80.0 GB | Comfortable | Q8_0 | ≈33 tok/s | Try it → |
| RX 7900 XTX | 24.0 GB | Not feasible | needs 2 cards | — | Try it → |
| Mac mini M4 Pro (48GB) | 36.0 GB | Not feasible | Q2_K | ≈7 tok/s | Try it → |
| Mac Studio M4 Max (64GB) | 48.0 GB | Tight fit | Q4_K_M | ≈9 tok/s | Try it → |
| Mac Studio M3 Ultra (96GB) | 72.0 GB | Comfortable | Q6_K | ≈11 tok/s | Try it → |
Theoretical speed = bandwidth × 0.75 ÷ per-token weight bytes (active params for MoE); real-world results vary with framework/driver/CPU, ±30%.
FAQ
- Can a single GPU run Llama 3.3 70B?
- Only the 48GB RTX A6000, tightly (Q4_K_M ~47.4GB). A100/H100 80GB run it comfortably; Q8_0 (~79GB) is tight. 24GB/32GB single cards cannot fit it — use 2×24GB multi-GPU or a 64GB+ Mac.
- What is 70B like on dual RTX 3090s?
- Q4_K_M ~47.4GB barely fits across 48GB with layer splitting, theoretically ~28 tok/s (aggregate bandwidth × 0.85 penalty). Readable speed, minimal VRAM margin — keep context moderate.
- Is Llama 3.3 70B worth a hardware upgrade?
- If you already feel the quality ceiling at 24-32B, yes — it is the local dense ceiling. For everyday chat, the gap over 32B does not justify doubling the hardware bill.
Related guides
- Multi-GPU Setup for Local LLMs: NVLink, PCIe & Layer Splitting
- Mac vs GPU for LLM Inference: Unified Memory Explained
- CPU Offload: Is Partial GPU Offloading Worth It?
Try Llama 3.3 70B in the GPU compatibility checker →
Data verified 2026-09-01