Can Helply 10.2b chat i1 run on RTX 2080 Ti 11GB?

YES — Tight Fit

C52Usable

Estimated from fit model

Helply 10.2b chat i1 needs ~9.7 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~64 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, 64.4 tok/s, Tight fit

9.7 GB required11.0 GB available

88% VRAM used

Fit status

Tight fit

Decode

64.4 tok/s

TTFT

3008 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 RTX 2080 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: 64.4 tok/s decode · 3.0s TTFT (warm) · 161 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	64.4 tok/s	1641 ms	33K
Coding	C	Tight fit	64.4 tok/s	3008 ms	33K
Agentic Coding	C	Runs with offload	64.4 tok/s	4376 ms	33K
Reasoning	C	Tight fit	64.4 tok/s	3555 ms	33K
RAG	C	Runs with offload	64.4 tok/s	5470 ms	33K

Quantization options

How Helply 10.2b chat i1 (10.199999809265137B params) fits at each quantization level on RTX 2080 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

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

RTX 3060 12GBBudget pick

12 GB VRAM (+1)

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

~$329 MSRP

RTX 5060 Ti 16GBBest value

16 GB VRAM (+5)

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

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

~$449 MSRP

RTX 5070 12GBNVIDIA upgrade

12 GB VRAM (+1)672 GB/s (+56)

This setup is broadly balanced for this model.68 tok/s decode

~$549 MSRP

Frequently asked questions

Can RTX 2080 Ti 11GB run Helply 10.2b chat i1?

Yes, RTX 2080 Ti 11GB can run Helply 10.2b chat i1 with a C grade (Tight fit). Expected decode speed: 64.4 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 RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, Helply 10.2b chat i1 achieves approximately 64.4 tokens per second decode speed with a time-to-first-token of 3008ms using Q4_K_M quantization.

Can RTX 2080 Ti 11GB run Helply 10.2b chat i1 for coding?

For coding workloads, Helply 10.2b chat i1 on RTX 2080 Ti 11GB receives a C grade with 64.4 tok/s and 33K context.

What context window can Helply 10.2b chat i1 use on RTX 2080 Ti 11GB?

On RTX 2080 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 RTX 2080 Ti 11GB See all hardware for Helply 10.2b chat i1

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