Can HelpingAI2 9B i1 run on NVIDIA H100 80GB?

YES — Runs Great

C46Usable

Estimated from fit model

HelpingAI2 9B i1 needs ~15.7 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~126 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) — 15.7 GB, 126.0 tok/s, Runs well

15.7 GB required80.0 GB available

20% VRAM used

Fit status

Runs well

Decode

126.0 tok/s

TTFT

1537 ms

Safe context

991K

Memory

15.7 GB / 80.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsHelpingAI2 9B i1 on NVIDIA H100 80GB
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: 126.0 tok/s decode · 1.5s TTFT (warm) · 315 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	126.0 tok/s	838 ms	991K
Coding	C	Runs well	126.0 tok/s	1537 ms	991K
Agentic Coding	C	Runs well	126.0 tok/s	2235 ms	991K
Reasoning	C	Runs well	126.0 tok/s	1816 ms	991K
RAG	C	Runs well	126.0 tok/s	2794 ms	991K

Quantization options

How HelpingAI2 9B i1 (9B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	D39
Q3_K_S	3	4.4 GB	Low	D39
NVFP4	4

Get started

Copy-paste commands to run HelpingAI2 9B i1 on your machine.

Run

lms load hf-mradermacher--helpingai2-9b-i1-gguf && lms server start

Upgrade options

Hardware that runs HelpingAI2 9B i1 well

Mac Studio M2 Ultra 128GBBudget pick

128 GB Unified (+48)

This setup is broadly balanced for this model.84.5 tok/s decode

~$3,999 MSRP

Mac Studio M1 Ultra 128GBBest value

128 GB Unified (+48)

This setup is broadly balanced for this model.80.1 tok/s decode

~$3,999 MSRP

Frequently asked questions

Can NVIDIA H100 80GB run HelpingAI2 9B i1?

Yes, NVIDIA H100 80GB can run HelpingAI2 9B i1 with a C grade (Runs well). Expected decode speed: 126.0 tok/s.

How much VRAM does HelpingAI2 9B i1 need?

HelpingAI2 9B i1 (9B parameters) requires approximately 15.7 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI2 9B i1?

The recommended quantization for HelpingAI2 9B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will HelpingAI2 9B i1 run at on NVIDIA H100 80GB?

On NVIDIA H100 80GB, HelpingAI2 9B i1 achieves approximately 126.0 tokens per second decode speed with a time-to-first-token of 1537ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run HelpingAI2 9B i1 for coding?

For coding workloads, HelpingAI2 9B i1 on NVIDIA H100 80GB receives a C grade with 126.0 tok/s and 991K context.

What context window can HelpingAI2 9B i1 use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, HelpingAI2 9B i1 can safely use up to 991K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA H100 80GB See all hardware for HelpingAI2 9B i1

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