Can HelpingAI 15B i1 run on RTX 4070 Ti Super 16GB?

YES — Tight Fit

C52Usable

Estimated from fit model

HelpingAI 15B i1 needs ~13.4 GB VRAM. RTX 4070 Ti Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~60 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: 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) — 13.4 GB, 60.1 tok/s, Tight fit

13.4 GB required16.0 GB available

84% VRAM used

Fit status

Tight fit

Decode

60.1 tok/s

TTFT

3222 ms

Safe context

40K

Memory

13.4 GB / 16.0 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsHelpingAI 15B i1 on RTX 4070 Ti Super 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: 60.1 tok/s decode · 3.2s TTFT (warm) · 150 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	B	Runs well	60.1 tok/s	1757 ms	40K
Coding	C	Tight fit	60.1 tok/s	3222 ms	40K
Agentic Coding	C	Tight fit	60.1 tok/s	4686 ms	40K
Reasoning	C	Tight fit	60.1 tok/s	3808 ms	40K
RAG	C	Tight fit	60.1 tok/s	5858 ms	40K

Quantization options

How HelpingAI 15B i1 (15B params) fits at each quantization level on RTX 4070 Ti Super 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C49
Q3_K_S	3	7.4 GB	Low	C51
NVFP4	4

Get started

Copy-paste commands to run HelpingAI 15B i1 on your machine.

Run

lms load hf-mradermacher--helpingai-15b-i1-gguf && lms server start

Upgrade options

Hardware that runs HelpingAI 15B i1 well

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.30.7 tok/s decode

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

~$1,250 MSRP

RTX 3090 24GBBest value

24 GB VRAM (+8)936 GB/s (+264)

Adds memory headroom for longer context windows and future model growth.68.3 tok/s decode

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

~$1,499 MSRP

RTX 4090 24GBNVIDIA upgrade

24 GB VRAM (+8)1008 GB/s (+336)

Raises estimated decode speed by about 42%.85.6 tok/s decode

Raises estimated decode speed by about 42%.

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

~$1,599 MSRP

Frequently asked questions

Can RTX 4070 Ti Super 16GB run HelpingAI 15B i1?

Yes, RTX 4070 Ti Super 16GB can run HelpingAI 15B i1 with a C grade (Tight fit). Expected decode speed: 60.1 tok/s.

How much VRAM does HelpingAI 15B i1 need?

HelpingAI 15B i1 (15B parameters) requires approximately 13.4 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI 15B i1?

The recommended quantization for HelpingAI 15B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will HelpingAI 15B i1 run at on RTX 4070 Ti Super 16GB?

On RTX 4070 Ti Super 16GB, HelpingAI 15B i1 achieves approximately 60.1 tokens per second decode speed with a time-to-first-token of 3222ms using Q4_K_M quantization.

Can RTX 4070 Ti Super 16GB run HelpingAI 15B i1 for coding?

For coding workloads, HelpingAI 15B i1 on RTX 4070 Ti Super 16GB receives a C grade with 60.1 tok/s and 40K context.

What context window can HelpingAI 15B i1 use on RTX 4070 Ti Super 16GB?

On RTX 4070 Ti Super 16GB, HelpingAI 15B i1 can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4070 Ti Super 16GB See all hardware for HelpingAI 15B i1

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