How much VRAM does Llama 3.3 70B need?

Llama 3.3 70B (70B parameters) requires approximately 67.6 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, AMD Instinct MI250 128GB can run Llama 3.3 70B with a C grade (Runs well). Expected decode speed: 54.0 tok/s.

CUsable

Runs well

Using Q4_K_M in Ollama

Capabilities:

Fit status

Runs well

Decode

54.0 tok/s

TTFT

3588 ms

Safe context

30K

Memory

67.6 GB / 128.0 GB

Weights42.7 GB

KV Cache10.9 GB

Runtime1.2 GB

Headroom12.8 GB

Workload	Grade	Fit	Decode	TTFT	Context
Agentic Coding	C	Runs well	51.0 tok/s	5526 ms	52K
Chat	C	Runs well	51.0 tok/s	2072 ms	16K
Coding	C	Runs well	54.0 tok/s	3588 ms	30K
RAG	C	Runs well	51.0 tok/s	6907 ms	52K
Reasoning	C	Runs well	51.0 tok/s	4490 ms	30K

How Llama 3.3 70B (70B params) fits at each quantization level on AMD Instinct MI250 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	D34
Q3_K_S	3	34.3 GB	Low	D35
NVFP4	4

Ollama

ollama run llama-3.3-70b

HuggingFace

huggingface-cli download llama-3.3-70b

Upgrade options

NVIDIA H200 141GBBudget pick

C94.4 tok/s decode

~$30,000 MSRP

NVIDIA H200 PCIe 141GBBest value

C94.4 tok/s decode

~$30,000 MSRP