Can Helply 10.2b chat i1 run on NVIDIA L4 24GB?

YES — Runs Great

C48Usable

Estimated from fit model

Helply 10.2b chat i1 needs ~11.0 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~31 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) — 11.0 GB, 31.3 tok/s, Runs well

11.0 GB required24.0 GB available

46% VRAM used

Fit status

Runs well

Decode

31.3 tok/s

TTFT

6177 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 NVIDIA L4 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: 31.3 tok/s decode · 6.2s TTFT (warm) · 78 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	31.3 tok/s	3369 ms	190K
Coding	C	Runs well	31.3 tok/s	6177 ms	190K
Agentic Coding	C	Runs well	31.3 tok/s	8985 ms	190K
Reasoning	C	Runs well	31.3 tok/s	7300 ms	190K
RAG	C	Runs well	31.3 tok/s	11232 ms	190K

Quantization options

How Helply 10.2b chat i1 (10.199999809265137B params) fits at each quantization level on NVIDIA L4 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

Upgrade-Optionen

Hardware, die Helply 10.2b chat i1 gut ausführt

RTX 5090 32GBGünstige Wahl

32 GB VRAM (+8)1792 GB/s (+1492)

Raises estimated decode speed by about 356%.142.8 tok/s Dekodierung

Raises estimated decode speed by about 356%.

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

ca. $1,999 MSRP

RTX PRO 4500 Blackwell 32GBBestes Preis-Leistungs-Verhältnis

32 GB VRAM (+8)896 GB/s (+596)

Raises estimated decode speed by about 287%.121 tok/s Dekodierung

Raises estimated decode speed by about 287%.

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

ca. $2,499 MSRP

RTX 5000 Ada 32GBNVIDIA-Upgrade

32 GB VRAM (+8)576 GB/s (+276)

Raises estimated decode speed by about 137%.74.1 tok/s Dekodierung

Raises estimated decode speed by about 137%.

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

ca. $4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run Helply 10.2b chat i1?

Yes, NVIDIA L4 24GB can run Helply 10.2b chat i1 with a C grade (Runs well). Expected decode speed: 31.3 tok/s.

How much VRAM does Helply 10.2b chat i1 need?

Helply 10.2b chat i1 (10.199999809265137B parameters) requires approximately 11.0 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 NVIDIA L4 24GB?

On NVIDIA L4 24GB, Helply 10.2b chat i1 achieves approximately 31.3 tokens per second decode speed with a time-to-first-token of 6177ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run Helply 10.2b chat i1 for coding?

For coding workloads, Helply 10.2b chat i1 on NVIDIA L4 24GB receives a C grade with 31.3 tok/s and 190K context.

What context window can Helply 10.2b chat i1 use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Helply 10.2b chat i1 can safely use up to 190K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA L4 24GB 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-l4-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: