Can Helply 10.2b chat i1 run on RTX PRO 4000 Blackwell 24GB?

YES — Runs Great

C51Usable

○Estimated from fit model

Helply 10.2b chat i1 needs ~11.0 GB VRAM. RTX PRO 4000 Blackwell 24GB has 24.0 GB. With Q4_K_M quantization, expect ~91 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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) — 11.0 GB, 90.7 tok/s, Runs well

11.0 GB required24.0 GB available

46% VRAM used

Fit status

Runs well

Decode

90.7 tok/s

TTFT

2134 ms

Safe context

190K

Memory

11.0 GB / 24.0 GB

Memory breakdown

Weights6.2 GB

KV Cache1.2 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsHelply 10.2b chat i1 on RTX PRO 4000 Blackwell 24GB
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: 90.7 tok/s decode · 2.1s TTFT (warm) · 227 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	90.7 tok/s	1164 ms	190K
Coding	C	Runs well	90.7 tok/s	2134 ms	190K
Agentic Coding	C	Runs well	90.7 tok/s	3104 ms	190K
Reasoning	C	Runs well	90.7 tok/s	2522 ms	190K
RAG	C	Runs well	90.7 tok/s	3880 ms	190K

Quantization options

How Helply 10.2b chat i1 (10.199999809265137B params) fits at each quantization level on RTX PRO 4000 Blackwell 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.0 GB	Low	C44
Q3_K_S	3	5.0 GB	Low	C45
NVFP4	4	5.7 GB	Medium	C45
Q4_K_M	4	6.2 GB	Medium	C46
Q5_K_M	5	7.3 GB	High	C46
Q6_K	6	8.4 GB	High	C47
Q8_0Best for your GPU	8	10.9 GB	Very High	C49
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

Frequently asked questions

See all results for RTX PRO 4000 Blackwell 24GB See all hardware for Helply 10.2b chat i1

URL: https://willitrunai.com/can-run/hf-mradermacher--helply-10-2b-chat-i1-gguf-on-rtx-pro-4000-blackwell-24gb

⇱ Helply 10.2b chat i1 on RTX PRO 4000 Blackwell 24GB? YES