How much VRAM does Llama 3.3 70B need?

Llama 3.3 70B (70B parameters) requires approximately 56.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.3 70B?

The recommended quantization for Llama 3.3 70B is Q4_K_M, which balances quality and memory efficiency.

Can it run?

No, Llama 3.3 70B requires more memory than RTX 4080 Super 16GB provides.

FWon't run

Too heavy

Using Q4_K_M in Ollama

Capabilities:

Fit status

Too heavy

Decode

12.4 tok/s

TTFT

15600 ms

Safe context

Memory

56.4 GB / 16.0 GB

Weights42.7 GB

KV Cache10.9 GB

Runtime1.2 GB

Headroom1.6 GB

Workload	Grade	Fit	Decode	TTFT	Context
Agentic Coding	F	Too heavy	12.4 tok/s	22691 ms	8K
Chat	F	Too heavy	12.4 tok/s	8509 ms	4K
Coding	F	Too heavy	12.4 tok/s	15600 ms	5K
RAG	F	Too heavy	12.4 tok/s	28364 ms	8K
Reasoning	F	Too heavy	12.4 tok/s	18436 ms	5K

How Llama 3.3 70B (70B params) fits at each quantization level on RTX 4080 Super 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	F0
Q3_K_S	3	34.3 GB	Low	F0
NVFP4	4

Upgrade options

MacBook Pro M3 Max 128GBBudget pick

C5.6 tok/s decode

~$2,499 MSRP

MacBook Pro M4 Max 96GBBest value

C8.1 tok/s decode

~$2,499 MSRP

NVIDIA H100 80GBBiggest leap