How much VRAM does Llama 2 7B Chat need?

Llama 2 7B Chat (7B parameters) requires approximately 7.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 2 7B Chat?

The recommended quantization for Llama 2 7B Chat is Q4_K_M, which balances quality and memory efficiency.

Can it run?

Yes, RTX 4060 Ti 8GB can run Llama 2 7B Chat with a C grade (Tight fit). Expected decode speed: 48.6 tok/s.

CUsable

Tight fit

Using Q4_K_M in Ollama

Capabilities:

Fit status

Tight fit

Decode

48.6 tok/s

TTFT

3987 ms

Safe context

17K

Memory

7.4 GB / 8.0 GB

Weights4.3 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom0.8 GB

Workload	Grade	Fit	Decode	TTFT	Context
Agentic Coding	C	Runs with offload (needs ~0.2 GB host RAM)	47.0 tok/s	5995 ms	30K
Chat	C	Tight fit	49.2 tok/s	2145 ms	9K
Coding	C	Tight fit	48.6 tok/s	3987 ms	17K
RAG	C	Runs with offload (needs ~0.2 GB host RAM)	47.0 tok/s	7494 ms	30K
Reasoning	C	Tight fit	49.2 tok/s	4647 ms

How Llama 2 7B Chat (7B params) fits at each quantization level on RTX 4060 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	D37
Q3_K_S	3	3.4 GB	Low	D39
NVFP4	4

Upgrade options

Intel Arc B580 12GBBudget pick

C51.3 tok/s decode

~$249 MSRP

RTX 3060 12GBBest value

C55.6 tok/s decode

~$329 MSRP

RTX 3080 10GBNVIDIA upgrade

B135.3 tok/s decode

~$699 MSRP

RTX 2080 Ti 11GBBiggest leap

B93.8 tok/s decode

~$999 MSRP