Can HelpingAI2 9B i1 run on RTX 4000 Ada 20GB?

YES — Runs Great

C50Usable

Estimated from fit model

HelpingAI2 9B i1 needs ~9.7 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~51 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 9.7 GB, 51.1 tok/s, Runs well

9.7 GB required20.0 GB available

49% VRAM used

Fit status

Runs well

Decode

51.1 tok/s

TTFT

3785 ms

Safe context

172K

Memory

9.7 GB / 20.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsHelpingAI2 9B i1 on RTX 4000 Ada 20GB
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: 51.1 tok/s decode · 3.8s TTFT (warm) · 128 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	51.1 tok/s	2065 ms	172K
Coding	C	Runs well	51.1 tok/s	3785 ms	172K
Agentic Coding	C	Runs well	51.1 tok/s	5506 ms	172K
Reasoning	C	Runs well	51.1 tok/s	4473 ms	172K
RAG	C	Runs well	51.1 tok/s	6882 ms	172K

Quantization options

How HelpingAI2 9B i1 (9B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C45
Q3_K_S	3	4.4 GB	Low	C46
NVFP4	4

Get started

Copy-paste commands to run HelpingAI2 9B i1 on your machine.

Run

lms load hf-mradermacher--helpingai2-9b-i1-gguf && lms server start

Upgrade options

Hardware that runs HelpingAI2 9B i1 well

RTX 3090 24GBBudget pick

24 GB VRAM (+4)936 GB/s (+576)

Raises estimated decode speed by about 133%.119.3 tok/s decode

Raises estimated decode speed by about 133%.

~$1,499 MSRP

RTX 4090 24GBBest value

24 GB VRAM (+4)1008 GB/s (+648)

Raises estimated decode speed by about 147%.126 tok/s decode

Raises estimated decode speed by about 147%.

~$1,599 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+4)672 GB/s (+312)

Raises estimated decode speed by about 101%.102.8 tok/s decode

Raises estimated decode speed by about 101%.

~$1,599 MSRP

Frequently asked questions

Can RTX 4000 Ada 20GB run HelpingAI2 9B i1?

Yes, RTX 4000 Ada 20GB can run HelpingAI2 9B i1 with a C grade (Runs well). Expected decode speed: 51.1 tok/s.

How much VRAM does HelpingAI2 9B i1 need?

HelpingAI2 9B i1 (9B parameters) requires approximately 9.7 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.

What speed will HelpingAI2 9B i1 run at on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, HelpingAI2 9B i1 achieves approximately 51.1 tokens per second decode speed with a time-to-first-token of 3785ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run HelpingAI2 9B i1 for coding?

For coding workloads, HelpingAI2 9B i1 on RTX 4000 Ada 20GB receives a C grade with 51.1 tok/s and 172K context.

What context window can HelpingAI2 9B i1 use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, HelpingAI2 9B i1 can safely use up to 172K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4000 Ada 20GB See all hardware for HelpingAI2 9B i1

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-mradermacher--helpingai2-9b-i1-gguf-on-rtx-4000-ada-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: