How much VRAM does HelpingAI2.5 10B i1 need?

HelpingAI2.5 10B i1 (10B parameters) requires approximately 10.1 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI2.5 10B i1?

The recommended quantization for HelpingAI2.5 10B i1 is Q4_K_M, which balances quality and memory efficiency.

Can it run?

Yes, RTX 5070 12GB can run HelpingAI2.5 10B i1 with a C grade (Tight fit). Expected decode speed: 69.4 tok/s.

CUsable

Tight fit

Using Q4_K_M in Ollama

Capabilities:

Fit status

Tight fit

Decode

69.4 tok/s

TTFT

2789 ms

Safe context

19K

Memory

10.1 GB / 12.0 GB

Weights6.1 GB

KV Cache1.6 GB

Runtime1.2 GB

Headroom1.2 GB

Workload	Grade	Fit	Decode	TTFT	Context
Agentic Coding	C	Runs with offload	69.4 tok/s	4057 ms	33K
Chat	B	Runs well	69.4 tok/s	1522 ms	10K
Coding	C	Tight fit	69.4 tok/s	2789 ms	19K
RAG	C	Runs with offload	69.4 tok/s	5072 ms	33K
Reasoning	C	Tight fit	69.4 tok/s	3297 ms	19K

How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on RTX 5070 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.9 GB	Low	D36
Q3_K_S	3	4.9 GB	Low	D37
NVFP4	4

Upgrade options

Intel Arc A770 16GBBudget pick

C41.3 tok/s decode

~$349 MSRP

RX 9070 16GBBest value

C65 tok/s decode

~$479 MSRP

RTX 5070 Ti 16GBNVIDIA upgrade

B94 tok/s decode

~$749 MSRP

RTX 4080 Super 16GBBiggest leap