Can HelpingAI2.5 10B i1 run on NVIDIA V100 32GB?

YES — Runs Great

C49Usable

Estimated from fit model

HelpingAI2.5 10B i1 needs ~11.7 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~99 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 11.7 GB, 98.9 tok/s, Runs well

11.7 GB required32.0 GB available

37% VRAM used

Fit status

Runs well

Decode

98.9 tok/s

TTFT

1958 ms

Safe context

294K

Memory

11.7 GB / 32.0 GB

Memory breakdown

Weights6.1 GB

KV Cache1.2 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsHelpingAI2.5 10B i1 on NVIDIA V100 32GB
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: 98.9 tok/s decode · 2.0s TTFT (warm) · 247 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	98.9 tok/s	1068 ms	294K
Coding	C	Runs well	98.9 tok/s	1958 ms	294K
Agentic Coding	C	Runs well	98.9 tok/s	2849 ms	294K
Reasoning	C	Runs well	98.9 tok/s	2315 ms	294K
RAG	C	Runs well	98.9 tok/s	3561 ms	294K

Quantization options

How HelpingAI2.5 10B i1 (10B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.9 GB	Low	C43
Q3_K_S	3	4.9 GB	Low	C43
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

Frequently asked questions

Can NVIDIA V100 32GB run HelpingAI2.5 10B i1?

Yes, NVIDIA V100 32GB can run HelpingAI2.5 10B i1 with a C grade (Runs well). Expected decode speed: 98.9 tok/s.

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

HelpingAI2.5 10B i1 (10B parameters) requires approximately 11.7 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 V100 32GB?

On NVIDIA V100 32GB, HelpingAI2.5 10B i1 achieves approximately 98.9 tokens per second decode speed with a time-to-first-token of 1958ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run HelpingAI2.5 10B i1 for coding?

For coding workloads, HelpingAI2.5 10B i1 on NVIDIA V100 32GB receives a C grade with 98.9 tok/s and 294K context.

What context window can HelpingAI2.5 10B i1 use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, HelpingAI2.5 10B i1 can safely use up to 294K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See all hardware for HelpingAI2.5 10B i1

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