How much VRAM does Llama 3.3 70B need?

Llama 3.3 70B (70B parameters) requires approximately 65.2 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?

Yes, MacBook Pro M2 Max 96GB can run Llama 3.3 70B with a C grade (Tight fit). Expected decode speed: 6.1 tok/s.

CUsable

Tight fit

Using Q4_K_M in Ollama

Capabilities:

Fit status

Tight fit

Decode

6.1 tok/s

TTFT

31795 ms

Safe context

17K

Memory

65.2 GB / 69.1 GB

Weights42.7 GB

KV Cache10.9 GB

Runtime1.2 GB

Headroom10.4 GB

Workload	Grade	Fit	Decode	TTFT	Context
Agentic Coding	D	Very compromised (needs ~3.9 GB host RAM)	5.3 tok/s	53304 ms	29K
Chat	C	Tight fit	5.4 tok/s	19436 ms	9K
Coding	C	Tight fit	6.1 tok/s	31795 ms	17K
RAG	D	Very compromised (needs ~3.9 GB host RAM)	5.3 tok/s	66630 ms	29K
Reasoning	C	Tight fit	5.4 tok/s	42111 ms

How Llama 3.3 70B (70B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	D38
Q3_K_S	3	34.3 GB	Low	C40
NVFP4	4

Ollama

ollama run llama-3.3-70b

HuggingFace

huggingface-cli download llama-3.3-70b

Upgrade options

MacBook Pro M3 Max 128GBBudget pick

C5.6 tok/s decode

~$2,499 MSRP

Mac Studio M2 Ultra 128GBBest value

C10.9 tok/s decode

~$3,999 MSRP