HomeCan I run it?Llama 3.3 70BLlama 3.3 70B on an RTX 4060 Ti 16GB
No. Llama 3.3 70B at Q4_K_M needs about 50.2 GB, and the GeForce RTX 4060 Ti 16GB has 16.0 GB usable at a ~4k context.
Can I run Llama 3.3 70B on an RTX 4060 Ti 16GB?
Llama 3.3 70B at Q4_K_M needs about 50.2 GB, and the GeForce RTX 4060 Ti 16GB has 16.0 GB usable. That figure is 16 GB of VRAM on the spec sheet, of which 16.0 GB is usable for weights and cache, at 288 GB/s memory bandwidth. Meta’s flagship 70B instruct model, the usual target for a high-end local chat box. These are estimates from our formula, not measured benchmarks.
- Quant that fits
- None of Q4, Q8 or FP16 needs 50.2 GB
- Memory needed
- 50.2 GB at Q4_K_M, ~4k context
- Usable VRAM
- 16.0 GB of 16 GB VRAM
- Est. tokens/sec
- n/a does not fit
Memory at each quant
| Quant | Need vs 16.0 GB | Verdict |
|---|---|---|
| Q4_K_M | 50.2 GB | Q4Won’t fit |
| Q8_0 | 87.2 GB | Q8Won’t fit |
| FP16 | 163.3 GB | FP16Won’t fit |
Estimated tokens per second
No speed estimate: Llama 3.3 70B does not fit in the 16.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.
Context headroom
Even at a short ~4k context, the weights do not fit. Extending context would only increase the gap.
| Context | KV cache (FP16) | Total at Q4_K_M | Verdict |
|---|---|---|---|
| 4k | 1.3 GB | 50.2 GB | Won’t fit |
| 8k | 2.7 GB | 51.5 GB | Won’t fit |
| 16k | 5.4 GB | 54.2 GB | Won’t fit |
| 32k | 10.7 GB | 59.6 GB | Won’t fit |
| 64k | 21.5 GB | 70.3 GB | Won’t fit |
| 128k | 42.9 GB | 91.8 GB | Won’t fit |
What to do instead
- Run gpt-oss-20b instead (15.6 GB at Q4). It is a different model, not a drop-in, but it does fit this machine.
- To hold Llama 3.3 70B at Q4, you need about 50.2 GB. The MacBook Pro M5 Max 128GB has 96.0 GB usable ($6699 Apple list price, 14-inch M5 Max 18C CPU/40C GPU, 128GB/2TB).
- CPU offload can load the weights anyway, but it is slow. We do not publish an offload tokens/sec figure.
- See the best GPU for this model family and the Llama-3.3-70B-Instruct model page.
Other hardware for Llama 3.3 70B
- Llama 3.3 70B on an RTX 3060 12GBWon’t fit
- Llama 3.3 70B on an RTX 3070 TiWon’t fit
- Llama 3.3 70B on an RTX 3080Won’t fit
- Llama 3.3 70B on an RTX 3090Won’t fit
- Llama 3.3 70B on an RTX 4070 Ti SuperWon’t fit
- Llama 3.3 70B on an RTX 4080 SuperWon’t fit
Other models on an RTX 4060 Ti 16GB
- Qwen3-32B on an RTX 4060 Ti 16GBWon’t fit
- Qwen3-30B-A3B on an RTX 4060 Ti 16GBWon’t fit
- Qwen3.8-27B on an RTX 4060 Ti 16GBWon’t fit
- Gemma 3 27B on an RTX 4060 Ti 16GBWon’t fit
- Gemma 4 31B on an RTX 4060 Ti 16GBWon’t fit
- gpt-oss-20b on an RTX 4060 Ti 16GBTight
Quick answers
- Can I run Llama 3.3 70B on an RTX 4060 Ti 16GB?
- No. Llama 3.3 70B at Q4_K_M needs about 50.2 GB, and the GeForce RTX 4060 Ti 16GB has 16.0 GB usable.
- How much VRAM does Llama 3.3 70B need?
- About 50.2 GB at Q4_K_M, 87.2 GB at Q8_0 and 163.3 GB at FP16, estimated for a ~4k context.
- How fast is Llama 3.3 70B on an RTX 4060 Ti 16GB?
- We do not estimate tokens/sec when the model does not fit. CPU offload is possible but slow.
- What context length can I use?
- The weights already do not fit, so longer context is not the limiting factor.