Can Helply 10.2b chat i1 run on RTX 4000 Ada Laptop 12GB?

YES — Runs Great

C54Usable

Estimated from fit model

Helply 10.2b chat i1 needs ~9.5 GB VRAM. RTX 4000 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~51 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 9.5 GB, 53.2 tok/s, Runs well

9.5 GB required12.0 GB available

79% VRAM used

Fit status

Runs well

Decode

53.2 tok/s

TTFT

3638 ms

Safe context

49K

Memory

9.5 GB / 12.0 GB

Memory breakdown

Weights6.2 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsHelply 10.2b chat i1 on RTX 4000 Ada Laptop 12GB
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: 53.2 tok/s decode · 3.6s TTFT (warm) · 133 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	C	Runs well	53.2 tok/s	1984 ms	49K
Coding	C	Runs well	50.7 tok/s	3820 ms	49K
Agentic Coding	C	Tight fit	53.2 tok/s	5291 ms	49K
Reasoning	C	Runs well	53.2 tok/s	4299 ms	49K
RAG	C	Tight fit	53.2 tok/s	6614 ms	49K

Quantization options

How Helply 10.2b chat i1 (10.199999809265137B params) fits at each quantization level on RTX 4000 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.0 GB	Low	C50
Q3_K_S	3	5.0 GB	Low	C51
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 4070 Ti Super 16GBBudget pick

16 GB VRAM (+4)672 GB/s (+240)

Raises estimated decode speed by about 70%.90.7 tok/s decode

Raises estimated decode speed by about 70%.

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

~$799 MSRP

RTX 4080 Super 16GBBest value

16 GB VRAM (+4)736 GB/s (+304)

Raises estimated decode speed by about 94%.103.1 tok/s decode

Raises estimated decode speed by about 94%.

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

~$999 MSRP

RTX 5080 16GBNVIDIA upgrade

16 GB VRAM (+4)960 GB/s (+528)

Raises estimated decode speed by about 83%.97.3 tok/s decode

Raises estimated decode speed by about 83%.

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

~$999 MSRP

Frequently asked questions

Can RTX 4000 Ada Laptop 12GB run Helply 10.2b chat i1?

Yes, RTX 4000 Ada Laptop 12GB can run Helply 10.2b chat i1 with a C grade (Runs well). Expected decode speed: 50.7 tok/s.

How much VRAM does Helply 10.2b chat i1 need?

Helply 10.2b chat i1 (10.199999809265137B parameters) requires approximately 9.5 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 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, Helply 10.2b chat i1 achieves approximately 50.7 tokens per second decode speed with a time-to-first-token of 3820ms using Q4_K_M quantization.

Can RTX 4000 Ada Laptop 12GB run Helply 10.2b chat i1 for coding?

For coding workloads, Helply 10.2b chat i1 on RTX 4000 Ada Laptop 12GB receives a C grade with 50.7 tok/s and 49K context.

What context window can Helply 10.2b chat i1 use on RTX 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, Helply 10.2b chat i1 can safely use up to 49K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4000 Ada Laptop 12GB 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-rtx-4000-ada-laptop-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: