About this model
Llama 3.3 70B brings near-405B-class quality to a size that enthusiasts can actually host. It is a large model by any consumer standard: even the Q2 build needs around 24 GB, so realistic local setups involve dual 24 GB GPUs, a high-memory Mac, or significant CPU offload.
What it is good for
- High-quality general reasoning and writing
- Complex multi-step tasks where 8B models fall short
- Local evaluation and comparison against larger hosted models
Limitations
- Not practical on single consumer GPUs at good quality
- Speed is bandwidth-bound: expect single-digit tok/s on typical two-GPU Q3/Q4 setups
- Heavy quantization (Q2) degrades quality meaningfully
Memory by quantization (estimates)
Weights at a given bit-width plus estimated runtime and context overhead. These are estimates — see the methodology.
| Quantization | Approx. memory | Fits a 24 GB card | Recommended class |
|---|---|---|---|
| Q2 (GGUF) | ~24.5 GB | Tight | 25 GB+ |
| Q3 (GGUF) | ~33 GB | Tight | 33 GB+ |
| Q4 (GGUF) | ~41.5 GB | Tight | 42 GB+ |
GPUs that can run it
FAQ
What hardware does a 70B model need?
Roughly 40 GB at Q4, 33 GB at Q3, 24 GB at Q2. Practical local targets: two 24 GB GPUs, a 48 GB+ Mac, or a used server card. One 4090 only manages Q2 with almost no context headroom.
Can I run it with CPU offload?
Yes — with 64 GB of RAM, a 24 GB GPU can offload most layers, but speed drops to a few tokens per second. It works for occasional use, not daily driving.
Is it worth it over a 32B model?
If your hardware can host it at Q3+, yes — reasoning quality is a clear step up. If 32B Q4 already strains your memory, a well-quantized 32B is usually the better trade.
Does it support tool calling?
Yes, Llama 3.3 supports function calling and structured outputs, which is why it is a popular backend for local agent frameworks.