Can HelpingAI 15B i1 run on NVIDIA T4 16GB?

YES — Tight Fit

C49Usable

Estimated from fit model

HelpingAI 15B i1 needs ~13.7 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~23 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 13.7 GB, 22.7 tok/s, Tight fit

13.7 GB required16.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

22.7 tok/s

TTFT

8517 ms

Safe context

37K

Memory

13.7 GB / 16.0 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsHelpingAI 15B i1 on NVIDIA T4 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: 22.7 tok/s decode · 8.5s TTFT (warm) · 57 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	22.7 tok/s	4645 ms	37K
Coding	C	Tight fit	22.7 tok/s	8517 ms	37K
Agentic Coding	C	Runs with offload	22.7 tok/s	12388 ms	37K
Reasoning	C	Tight fit	22.7 tok/s	10065 ms	37K
RAG	C	Runs with offload	22.7 tok/s	15485 ms	37K

Quantization options

How HelpingAI 15B i1 (15B params) fits at each quantization level on NVIDIA T4 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)360 GB/s (+40)

Raises estimated decode speed by about 35%.30.7 tok/s decode

Raises estimated decode speed by about 35%.

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 (+616)

Raises estimated decode speed by about 215%.71.6 tok/s decode

Raises estimated decode speed by about 215%.

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

~$1,499 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+8)672 GB/s (+352)

Raises estimated decode speed by about 172%.61.7 tok/s decode

Raises estimated decode speed by about 172%.

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

~$1,599 MSRP

Frequently asked questions

Can NVIDIA T4 16GB run HelpingAI 15B i1?

Yes, NVIDIA T4 16GB can run HelpingAI 15B i1 with a C grade (Tight fit). Expected decode speed: 22.7 tok/s.

How much VRAM does HelpingAI 15B i1 need?

HelpingAI 15B i1 (15B parameters) requires approximately 13.7 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 NVIDIA T4 16GB?

On NVIDIA T4 16GB, HelpingAI 15B i1 achieves approximately 22.7 tokens per second decode speed with a time-to-first-token of 8517ms using Q4_K_M quantization.

Can NVIDIA T4 16GB run HelpingAI 15B i1 for coding?

For coding workloads, HelpingAI 15B i1 on NVIDIA T4 16GB receives a C grade with 22.7 tok/s and 37K context.

What context window can HelpingAI 15B i1 use on NVIDIA T4 16GB?

On NVIDIA T4 16GB, HelpingAI 15B i1 can safely use up to 37K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA T4 16GB See all hardware for HelpingAI 15B i1

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/hf-mradermacher--helpingai-15b-i1-gguf-on-t4-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: