How much VRAM does HelpingAI2 9B i1 need?

HelpingAI2 9B i1 (9B parameters) requires approximately 9.2 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI2 9B i1?

The recommended quantization for HelpingAI2 9B i1 is Q4_K_M, which balances quality and memory efficiency.

Can it run?

Yes, RTX 2080 Ti 11GB can run HelpingAI2 9B i1 with a C grade (Tight fit). Expected decode speed: 80.8 tok/s.

CUsable

Tight fit

Using Q4_K_M in Ollama

Capabilities:

Fit status

Tight fit

Decode

80.8 tok/s

TTFT

2396 ms

Safe context

19K

Memory

9.2 GB / 11.0 GB

Weights5.5 GB

KV Cache1.4 GB

Runtime1.2 GB

Headroom1.1 GB

Workload	Grade	Fit	Decode	TTFT	Context
Agentic Coding	C	Runs with offload	72.9 tok/s	3861 ms	33K
Chat	B	Runs well	72.9 tok/s	1448 ms	10K
Coding	C	Tight fit	80.8 tok/s	2396 ms	19K
RAG	C	Runs with offload	72.9 tok/s	4826 ms	33K
Reasoning	C	Tight fit	72.9 tok/s	3137 ms	19K

How HelpingAI2 9B i1 (9B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	D36
Q3_K_S	3	4.4 GB	Low	D37
NVFP4	4

Upgrade options

Intel Arc B580 12GBBudget pick

C39.9 tok/s decode

~$249 MSRP

RTX 3060 12GBBest value

C43.3 tok/s decode

~$329 MSRP

RTX 3080 12GBBiggest leap

B126.3 tok/s decode

~$799 MSRP

RTX 3080 Ti 12GBNVIDIA upgrade

B122.9 tok/s decode

~$1,199 MSRP