Can Helply 10.2b chat i1 run on NVIDIA T4 16GB?

YES — Runs Great

C52Usable

○Estimated from fit model

Helply 10.2b chat i1 needs ~10.2 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~33 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) — 10.2 GB, 33.4 tok/s, Runs well

10.2 GB required16.0 GB available

64% VRAM used

Fit status

Runs well

Decode

33.4 tok/s

TTFT

5791 ms

Safe context

93K

Memory

10.2 GB / 16.0 GB

Memory breakdown

Weights6.2 GB

KV Cache1.2 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsHelply 10.2b chat 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: 33.4 tok/s decode · 5.8s TTFT (warm) · 84 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	33.4 tok/s	3159 ms	93K
Coding	C	Runs well	33.4 tok/s	5791 ms	93K
Agentic Coding	C	Runs well	33.4 tok/s	8424 ms	93K
Reasoning	C	Runs well	33.4 tok/s	6844 ms	93K
RAG	C	Runs well	33.4 tok/s	10530 ms	93K

Quantization options

How Helply 10.2b chat i1 (10.199999809265137B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.0 GB	Low	C47
Q3_K_S	3	5.0 GB	Low	C48
NVFP4	4	5.7 GB	Medium	C49
Q4_K_M	4	6.2 GB	Medium	C49
Q5_K_M	5	7.3 GB	High	C51
Q6_K	6	8.4 GB	High	C51
Q8_0Best for your GPU	8	10.9 GB	Very High	C50
F16	16	20.9 GB	Maximum	F0

Get started

Copy-paste commands to run Helply 10.2b chat i1 on your machine.

Run

lms load hf-mradermacher--helply-10-2b-chat-i1-gguf && lms server start

Upgrade options

Hardware that runs Helply 10.2b chat i1 well

RX 7900 XT 20GBBest value

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

Raises estimated decode speed by about 131%.77.1 tok/s decode

Raises estimated decode speed by about 131%.

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

~$899 MSRP

👁 NVIDIA
RTX A4500 20GBBudget pick

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

Raises estimated decode speed by about 140%.80.2 tok/s decode

Raises estimated decode speed by about 140%.