Can HelpingAI2 9B run on RTX A6000 48GB?

YES — Runs Great

C47Usable

Estimated from fit model

HelpingAI2 9B needs ~12.5 GB VRAM. RTX A6000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~106 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 12.5 GB, 106.3 tok/s, Runs well

12.5 GB required48.0 GB available

26% VRAM used

Fit status

Runs well

Decode

106.3 tok/s

TTFT

1821 ms

Safe context

554K

Memory

12.5 GB / 48.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsHelpingAI2 9B on RTX A6000 48GB
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: 106.3 tok/s decode · 1.8s TTFT (warm) · 266 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	106.3 tok/s	993 ms	554K
Coding	C	Runs well	106.3 tok/s	1821 ms	554K
Agentic Coding	C	Runs well	106.3 tok/s	2649 ms	554K
Reasoning	C	Runs well	106.3 tok/s	2152 ms	554K
RAG	C	Runs well	106.3 tok/s	3311 ms	554K

Quantization options

How HelpingAI2 9B (9B params) fits at each quantization level on RTX A6000 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C41
Q3_K_S	3	4.4 GB	Low	C41
NVFP4	4

Get started

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

Run

lms load hf-bartowski--helpingai2-9b-gguf && lms server start

Upgrade options

Hardware that runs HelpingAI2 9B well

Mac Studio M3 Ultra 96GBBudget pick

96 GB Unified (+48)819 GB/s (+51)

Adds memory headroom for longer context windows and future model growth.101.4 tok/s decode

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

~$3,999 MSRP

Frequently asked questions

Can RTX A6000 48GB run HelpingAI2 9B?

Yes, RTX A6000 48GB can run HelpingAI2 9B with a C grade (Runs well). Expected decode speed: 106.3 tok/s.

How much VRAM does HelpingAI2 9B need?

HelpingAI2 9B (9B parameters) requires approximately 12.5 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI2 9B?

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

What speed will HelpingAI2 9B run at on RTX A6000 48GB?

On RTX A6000 48GB, HelpingAI2 9B achieves approximately 106.3 tokens per second decode speed with a time-to-first-token of 1821ms using Q4_K_M quantization.

Can RTX A6000 48GB run HelpingAI2 9B for coding?

For coding workloads, HelpingAI2 9B on RTX A6000 48GB receives a C grade with 106.3 tok/s and 554K context.

What context window can HelpingAI2 9B use on RTX A6000 48GB?

On RTX A6000 48GB, HelpingAI2 9B can safely use up to 554K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX A6000 48GB See all hardware for HelpingAI2 9B

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