Can Helply 10.2b chat i1 run on GTX 1080 Ti 11GB?

YES — Tight Fit

C51Usable

Estimated from fit model

Helply 10.2b chat i1 needs ~9.7 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~46 tok/s.

Runtime: OllamaCapacity: TightBandwidth: MediumStack: 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, 45.9 tok/s, Tight fit

9.7 GB required11.0 GB available

88% VRAM used

Fit status

Tight fit

Decode

45.9 tok/s

TTFT

4218 ms

Safe context

33K

Memory

9.7 GB / 11.0 GB

Memory breakdown

Weights6.2 GB

KV Cache1.2 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsHelply 10.2b chat i1 on GTX 1080 Ti 11GB
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: 45.9 tok/s decode · 4.2s TTFT (warm) · 115 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	Tight fit	45.9 tok/s	2301 ms	33K
Coding	C	Tight fit	45.9 tok/s	4218 ms	33K
Agentic Coding	C	Runs with offload	45.9 tok/s	6136 ms	33K
Reasoning	C	Tight fit	45.9 tok/s	4985 ms	33K
RAG	C	Runs with offload	45.9 tok/s	7670 ms	33K

Quantization options

How Helply 10.2b chat i1 (10.199999809265137B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.0 GB	Low	C51
Q3_K_S	3	5.0 GB	Low	C52
NVFP4	4	5.7 GB	Medium	C52
Q4_K_M	4	6.2 GB	Medium	C52
Q5_K_MBest for your GPU	5	7.3 GB	High	C51
Q6_K	6	8.4 GB	High	F0
Q8_0	8	10.9 GB	Very High	F0
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

Opções de upgrade

Hardware que roda bem Helply 10.2b chat i1

RTX 3060 12GBOpção econômica

12 GB VRAM (+1)

This setup is broadly balanced for this model.38.2 tok/s decodificação

~$329 MSRP

RTX 5060 Ti 16GBMelhor custo-benefício

16 GB VRAM (+5)

Adds memory headroom for longer context windows and future model growth.44.6 tok/s decodificação

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

~$449 MSRP

RTX 4060 Ti 16GBUpgrade NVIDIA

16 GB VRAM (+5)

Adds memory headroom for longer context windows and future model growth.33.8 tok/s decodificação

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

~$499 MSRP

Frequently asked questions

Can GTX 1080 Ti 11GB run Helply 10.2b chat i1?

Yes, GTX 1080 Ti 11GB can run Helply 10.2b chat i1 with a C grade (Tight fit). Expected decode speed: 45.9 tok/s.

How much VRAM does Helply 10.2b chat i1 need?

Helply 10.2b chat i1 (10.199999809265137B parameters) requires approximately 9.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Helply 10.2b chat i1?

The recommended quantization for Helply 10.2b chat i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Helply 10.2b chat i1 run at on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, Helply 10.2b chat i1 achieves approximately 45.9 tokens per second decode speed with a time-to-first-token of 4218ms using Q4_K_M quantization.

Can GTX 1080 Ti 11GB run Helply 10.2b chat i1 for coding?

For coding workloads, Helply 10.2b chat i1 on GTX 1080 Ti 11GB receives a C grade with 45.9 tok/s and 33K context.

What context window can Helply 10.2b chat i1 use on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, Helply 10.2b chat i1 can safely use up to 33K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for GTX 1080 Ti 11GB See all hardware for Helply 10.2b chat i1

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/hf-mradermacher--helply-10-2b-chat-i1-gguf-on-gtx-1080-ti-11gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: