Llama 3.3 70B: VRAM requirements & speed
Llama 3.3 70B (70.55B dense, llama3.3). Native context 128k. Smallest GGUF: 42.52 GB.
Memory needed by quantization and context
GGUF file size plus f16 KV cache (and fixed overhead of ~1 GB per GPU not shown). Source: unsloth/Llama-3.3-70B-Instruct-GGUF.
| Quant | File | +8k ctx | +32k ctx | +128k ctx |
|---|---|---|---|---|
| Q4_K_M | 42.52 GB | 45.2 GB | 53.3 GB | 85.5 GB |
| Q5_K_M | 49.95 GB | 52.6 GB | 60.7 GB | 92.9 GB |
| Q6_K | 57.89 GB | 60.6 GB | 68.6 GB | 100.8 GB |
| Q8_0 | 74.98 GB | 77.7 GB | 85.7 GB | 117.9 GB |
KV cache size by context length
Computed from the model's attention layout.
| Context | f16 | q8_0 | q4_0 |
|---|---|---|---|
| 8k | 2.68 GB | 1.43 GB | 0.75 GB |
| 32k | 10.74 GB | 5.70 GB | 3.02 GB |
| 128k | 42.95 GB | 22.82 GB | 12.08 GB |
Which hardware runs Llama 3.3 70B? · 16k context
| Hardware | Quant | Fit | Memory | Tok/s | Speed source |
|---|---|---|---|---|---|
| Mac Studio M5 Ultra 256GB | Q4_K_M | fits fully | 47.9 GB | 17 | estimated |
| Mac Studio M5 Ultra 512GB (ships late Oct 2026) | Q4_K_M | fits fully | 47.9 GB | 17 | estimated |
| AMD Ryzen AI Max+ 395 (Strix Halo) 128GB | Q8_0 | fits fully | 80.3 GB | 16 | scaled from measured |
| RTX PRO 6000 96GB | Q8_0 | fits fully | 80.3 GB | 15 | estimated |
| Mac Studio M5 Max 128GB | Q4_K_M | fits fully | 47.9 GB | 12 | scaled from measured |
| MacBook Pro 14/16 M5 Max 128GB | Q4_K_M | fits fully | 47.9 GB | 12 | scaled from measured |
| 2x RTX 3090 (48GB total) | Q4_K_M | partial offload (slow) | 47.9 GB | 10 | estimated |
| Mac Studio (2025) M3 Ultra 96GB | Q4_K_M | fits fully | 47.9 GB | 8 | estimated |
| MacBook Pro 14/16 (2024) M4 Max 128GB | Q4_K_M | fits fully | 47.9 GB | 6 | estimated |
| NVIDIA DGX Spark 128GB | Q4_K_M | fits fully | 47.9 GB | 5 | estimated |
| AMD Ryzen AI Max+ PRO 495 (Ryzen AI Halo) 192GB | Q4_K_M | fits fully | 47.9 GB | 5 | estimated |
| RTX 5090 32GB | Q4_K_M | partial offload (slow) | 47.9 GB | 3 | estimated |
| RTX 3090 24GB | - | does not fit | - | - | |
| RTX 4090 24GB | - | does not fit | - | - | |
| RTX 3060 12GB (32GB RAM) | - | does not fit | - | - | |
| 8GB VRAM class (RTX 4060 8GB, 32GB RAM) | - | does not fit | - | - | |
| 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM) | - | does not fit | - | - | |
| RTX 5070 12GB | - | does not fit | - | - | |
| RTX 4070 12GB | - | does not fit | - | - | |
| RTX 5070 Ti 16GB | - | does not fit | - | - | |
| RTX 5080 16GB | - | does not fit | - | - | |
| AMD RX 7900 XTX 24GB | - | does not fit | - | - | |
| Mac mini / iMac M4 16GB | - | does not fit | - | - | |
| Mac mini (2024) M4 Pro 64GB | - | does not fit | - | - | |
| MacBook Pro 14/16 M5 Pro 48GB | - | does not fit | - | - | |
| Mac mini M6 32GB | - | does not fit | - | - | |
| MacBook Air M5 32GB | - | does not fit | - | - | |
| RTX 5060 Ti 16GB | - | does not fit | - | - |
Discrete GPUs are modelled with 32 GB of system RAM available for offload. Does not fit even with offload: RTX 3090 24GB, RTX 4090 24GB, RTX 3060 12GB (32GB RAM), 8GB VRAM class (RTX 4060 8GB, 32GB RAM), 16GB VRAM class (RTX 4060 Ti 16GB, 32GB RAM), RTX 5070 12GB, RTX 4070 12GB, RTX 5070 Ti 16GB, RTX 5080 16GB, AMD RX 7900 XTX 24GB, Mac mini / iMac M4 16GB, Mac mini (2024) M4 Pro 64GB, MacBook Pro 14/16 M5 Pro 48GB, Mac mini M6 32GB, MacBook Air M5 32GB, RTX 5060 Ti 16GB.
MLX conversions for Apple Silicon
| Repo | Bits | Size | Runs with |
|---|---|---|---|
| mlx-community/Llama-3.3-70B-Instruct-4bit | 4-bit (4bit) | 39.7 GB | mlx-lm |
| mlx-community/Llama-3.3-70B-Instruct-8bit | 8-bit (8bit) | 75 GB | mlx-lm |
Verified via the Hugging Face API on 2026-10-20. Compare with the GGUF sizes above (Q4_K_M 42.52 GB, Q8_0 74.98 GB). Mac hardware pages show the MLX command; there are no MLX speeds on this site yet. MLX vs GGUF.
Links
Model card · GGUF files · Open in the advisor · last verified 2026-10-20
FAQ
How much VRAM does Llama 3.3 70B need?
The smallest quant (Q4_K_M) is 42.52 GB, so you need roughly 46 GB for an 8k context including KV cache and overhead. At 128k context the f16 KV cache alone is 42.9 GB.
Can I run Llama 3.3 70B on a Mac Studio M5 Ultra 256GB?
Yes: on Mac Studio M5 Ultra 256GB it fits fully as Q4_K_M at about 17 tok/s (estimated). 10 of 28 hardware configurations fit it fully at 16k context.
What is the maximum context for Llama 3.3 70B?
128k tokens natively. Memory, not the model, is usually the limit: see the KV cache table above.
All models: model index · by memory: 8 GB · 12 GB · 16 GB · 24 GB · 32 GB · 48 GB · 64 GB · 96 GB · 128 GB · 192 GB · 256 GB · 512 GB. Numbers use the same fit math as the advisor; sources and methods.