Can HelpingAI 15B i1 run on NVIDIA A800 80GB?

YES — Runs Great

C47Usable

Estimated from fit model

HelpingAI 15B i1 needs ~20.1 GB VRAM. NVIDIA A800 80GB has 80.0 GB. With Q4_K_M quantization, expect ~165 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) — 20.1 GB, 165.0 tok/s, Runs well

20.1 GB required80.0 GB available

25% VRAM used

Fit status

Runs well

Decode

165.0 tok/s

TTFT

1174 ms

Safe context

561K

Memory

20.1 GB / 80.0 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsHelpingAI 15B i1 on NVIDIA A800 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: 165.0 tok/s decode · 1.2s TTFT (warm) · 412 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	165.0 tok/s	640 ms	561K
Coding	C	Runs well	165.0 tok/s	1174 ms	561K
Agentic Coding	C	Runs well	165.0 tok/s	1707 ms	561K
Reasoning	C	Runs well	165.0 tok/s	1387 ms	561K
RAG	C	Runs well	165.0 tok/s	2134 ms	561K

Quantization options

How HelpingAI 15B i1 (15B params) fits at each quantization level on NVIDIA A800 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	D39
Q3_K_S	3	7.4 GB	Low	D39
NVFP4	4

Get started

Copy-paste commands to run HelpingAI 15B i1 on your machine.

Run

lms load hf-mradermacher--helpingai-15b-i1-gguf && lms server start

Frequently asked questions

Can NVIDIA A800 80GB run HelpingAI 15B i1?

Yes, NVIDIA A800 80GB can run HelpingAI 15B i1 with a C grade (Runs well). Expected decode speed: 165.0 tok/s.

How much VRAM does HelpingAI 15B i1 need?

HelpingAI 15B i1 (15B parameters) requires approximately 20.1 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI 15B i1?

The recommended quantization for HelpingAI 15B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will HelpingAI 15B i1 run at on NVIDIA A800 80GB?

On NVIDIA A800 80GB, HelpingAI 15B i1 achieves approximately 165.0 tokens per second decode speed with a time-to-first-token of 1174ms using Q4_K_M quantization.

Can NVIDIA A800 80GB run HelpingAI 15B i1 for coding?

For coding workloads, HelpingAI 15B i1 on NVIDIA A800 80GB receives a C grade with 165.0 tok/s and 561K context.

What context window can HelpingAI 15B i1 use on NVIDIA A800 80GB?

On NVIDIA A800 80GB, HelpingAI 15B i1 can safely use up to 561K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A800 80GB See all hardware for HelpingAI 15B i1

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