Can HelpingAI2.5 10B i1 run on RX 9060 XT 16GB?

YES — Runs Great

C51Usable

○Estimated from fit model

HelpingAI2.5 10B i1 needs ~9.8 GB VRAM. RX 9060 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~33 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 9.8 GB, 33.0 tok/s, Runs well

9.8 GB required16.0 GB available

61% VRAM used

Fit status

Runs well

Decode

33.0 tok/s

TTFT

5858 ms

Safe context

101K

Memory

9.8 GB / 16.0 GB

Memory breakdown

Weights6.1 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsHelpingAI2.5 10B i1 on RX 9060 XT 16GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
 - Full privacy: your prompts never leave your machine
 - No per-token cost or rate limits
 - Works offline once the model is downloaded
 - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

 Model size Q4 (4-bit) Q8 (8-bit) FP16
 7B params ~4.3 GB ~7.5 GB ~14 GB
 13B params ~7.9 GB ~13.9 GB ~26 GB
 70B params ~42.7 GB ~74.9 GB ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 33.0 tok/s decode · 5.9s TTFT (warm) · 83 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	33.0 tok/s	3195 ms	101K
Coding	C	Runs well	33.0 tok/s	5858 ms	101K
Agentic Coding	C	Runs well	33.0 tok/s	8521 ms	101K
Reasoning	C	Runs well	33.0 tok/s	6923 ms	101K
RAG	C	Runs well	33.0 tok/s	10651 ms	101K

Quantization options

How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on RX 9060 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.9 GB	Low	C47
Q3_K_S	3	4.9 GB	Low	C48
NVFP4	4	5.6 GB	Medium	C49
Q4_K_M	4	6.1 GB	Medium	C49
Q5_K_M	5	7.2 GB	High	C50
Q6_K	6	8.2 GB	High	C51
Q8_0Best for your GPU	8	10.7 GB	Very High	C50
F16	16	20.5 GB	Maximum	F0

Get started

Copy-paste commands to run HelpingAI2.5 10B i1 on your machine.

Run

lms load hf-mradermacher--helpingai2-5-10b-i1-gguf && lms server start

Upgrade options

Hardware that runs HelpingAI2.5 10B i1 well

RX 7900 XT 20GBBudget pick

20 GB VRAM (+4)800 GB/s (+480)

Raises estimated decode speed by about 138%.78.7 tok/s decode

Raises estimated decode speed by about 138%.

Adds memory headroom for longer context windows and future model growth.

~$899 MSRP

👁 NVIDIA
RTX A4500 20GBBest value

20 GB VRAM (+4)640 GB/s (+320)

Raises estimated decode speed by about 148%.81.8 tok/s decode

Raises estimated decode speed by about 148%.