Can HelpingAI 15B i1 run on NVIDIA A100 80GB?

YES — Runs Great

C47Usable

Estimated from fit model

HelpingAI 15B i1 needs ~20.1 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~187 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, 187.2 tok/s, Runs well

20.1 GB required80.0 GB available

25% VRAM used

Fit status

Runs well

Decode

187.2 tok/s

TTFT

1034 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 A100 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: 187.2 tok/s decode · 1.0s TTFT (warm) · 468 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	187.2 tok/s	564 ms	561K
Coding	C	Runs well	187.2 tok/s	1034 ms	561K
Agentic Coding	C	Runs well	187.2 tok/s	1504 ms	561K
Reasoning	C	Runs well	187.2 tok/s	1222 ms	561K
RAG	C	Runs well	187.2 tok/s	1880 ms	561K

Inference speed

HelpingAI 15B i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for HelpingAI 15B i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~131 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	131.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	83.7	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	75.5	Fits
RTX 3090 24GB	24 GB	Q4_K_M	71.6	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	68.3	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	60.9	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	50.7	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	48.1	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	34.7	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	34.7	Fits
RTX 4070 12GB	12 GB	Q4_K_M	26.7	Heavy offload
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	26.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	24.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	21.2	Fits
RTX 3060 12GB	12 GB	Q4_K_M	15.7	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	5.9	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How HelpingAI 15B i1 (15B params) fits at each quantization level on NVIDIA A100 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	8.4 GB	Medium	D40
Q4_K_M	4	9.2 GB	Medium	D40
Q5_K_M	5	10.8 GB	High	D40
Q6_K	6	12.3 GB	High	D40
Q8_0	8	16.1 GB	Very High	C41
F16Best for your GPU	16	30.7 GB	Maximum	C43

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 A100 80GB run HelpingAI 15B i1?

Yes, NVIDIA A100 80GB can run HelpingAI 15B i1 with a C grade (Runs well). Expected decode speed: 187.2 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 A100 80GB?

On NVIDIA A100 80GB, HelpingAI 15B i1 achieves approximately 187.2 tokens per second decode speed with a time-to-first-token of 1034ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run HelpingAI 15B i1 for coding?

For coding workloads, HelpingAI 15B i1 on NVIDIA A100 80GB receives a C grade with 187.2 tok/s and 561K context.

What context window can HelpingAI 15B i1 use on NVIDIA A100 80GB?

On NVIDIA A100 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 A100 80GB See all hardware for HelpingAI 15B i1

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