Can HelpingAI2.5 10B i1 run on NVIDIA L4 24GB?

YES — Runs Great

C48Usable

Estimated from fit model

HelpingAI2.5 10B i1 needs ~10.9 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~32 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) — 10.9 GB, 32.0 tok/s, Runs well

10.9 GB required24.0 GB available

45% VRAM used

Fit status

Runs well

Decode

32.0 tok/s

TTFT

6056 ms

Safe context

195K

Memory

10.9 GB / 24.0 GB

Memory breakdown

Weights6.1 GB

KV Cache1.2 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsHelpingAI2.5 10B 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: 32.0 tok/s decode · 6.1s TTFT (warm) · 80 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	32.0 tok/s	3303 ms	195K
Coding	C	Runs well	32.0 tok/s	6056 ms	195K
Agentic Coding	C	Runs well	32.0 tok/s	8809 ms	195K
Reasoning	C	Runs well	32.0 tok/s	7157 ms	195K
RAG	C	Runs well	32.0 tok/s	11011 ms	195K

Quantization options

How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.9 GB	Low	C44
Q3_K_S	3	4.9 GB	Low	C45
NVFP4	4

Get started

Copy-paste commands to run HelpingAI2.5 10B i1 on your machine.

Run

lms load hf-mradermacher--helpingai2-5-10b-i1-gguf && lms server start

Upgrade options

Hardware that runs HelpingAI2.5 10B i1 well

RTX 5090 32GBBudget pick

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

Raises estimated decode speed by about 338%.140 tok/s decode

Raises estimated decode speed by about 338%.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

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

Raises estimated decode speed by about 286%.123.4 tok/s decode

Raises estimated decode speed by about 286%.

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

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

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

Raises estimated decode speed by about 136%.75.5 tok/s decode

Raises estimated decode speed by about 136%.

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

~$4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run HelpingAI2.5 10B i1?

Yes, NVIDIA L4 24GB can run HelpingAI2.5 10B i1 with a C grade (Runs well). Expected decode speed: 32.0 tok/s.

How much VRAM does HelpingAI2.5 10B i1 need?

HelpingAI2.5 10B i1 (10B parameters) requires approximately 10.9 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI2.5 10B i1?

The recommended quantization for HelpingAI2.5 10B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will HelpingAI2.5 10B i1 run at on NVIDIA L4 24GB?

On NVIDIA L4 24GB, HelpingAI2.5 10B i1 achieves approximately 32.0 tokens per second decode speed with a time-to-first-token of 6056ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run HelpingAI2.5 10B i1 for coding?

For coding workloads, HelpingAI2.5 10B i1 on NVIDIA L4 24GB receives a C grade with 32.0 tok/s and 195K context.

What context window can HelpingAI2.5 10B i1 use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, HelpingAI2.5 10B i1 can safely use up to 195K 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 HelpingAI2.5 10B i1

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