Can HelpingAI2 9B run on RTX 3080 10GB?

YES — Tight Fit

C53Usable

Estimated from fit model

HelpingAI2 9B needs ~8.7 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~105 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 8.7 GB, 105.2 tok/s, Tight fit

8.7 GB required10.0 GB available

87% VRAM used

Fit status

Tight fit

Decode

105.2 tok/s

TTFT

1840 ms

Safe context

35K

Memory

8.7 GB / 10.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime1.2 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsHelpingAI2 9B on RTX 3080 10GB
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: 105.2 tok/s decode · 1.8s TTFT (warm) · 263 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	Tight fit	105.2 tok/s	1004 ms	35K
Coding	C	Tight fit	105.2 tok/s	1840 ms	35K
Agentic Coding	C	Runs with offload	105.2 tok/s	2677 ms	35K
Reasoning	C	Tight fit	105.2 tok/s	2175 ms	35K
RAG	C	Runs with offload	105.2 tok/s	3346 ms	35K

Quantization options

How HelpingAI2 9B (9B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C52
Q3_K_S	3	4.4 GB	Low	C53
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

RTX 3060 12GBBudget pick

12 GB VRAM (+2)

This setup is broadly balanced for this model.43.3 tok/s decode

~$329 MSRP

RTX 5070 12GBBest value

12 GB VRAM (+2)

This setup is broadly balanced for this model.77.1 tok/s decode

~$549 MSRP

RTX 4070 Super 12GBNVIDIA upgrade

12 GB VRAM (+2)

This setup is broadly balanced for this model.70.7 tok/s decode

~$599 MSRP

Frequently asked questions

Can RTX 3080 10GB run HelpingAI2 9B?

Yes, RTX 3080 10GB can run HelpingAI2 9B with a C grade (Tight fit). Expected decode speed: 105.2 tok/s.

How much VRAM does HelpingAI2 9B need?

HelpingAI2 9B (9B parameters) requires approximately 8.7 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 3080 10GB?

On RTX 3080 10GB, HelpingAI2 9B achieves approximately 105.2 tokens per second decode speed with a time-to-first-token of 1840ms using Q4_K_M quantization.

Can RTX 3080 10GB run HelpingAI2 9B for coding?

For coding workloads, HelpingAI2 9B on RTX 3080 10GB receives a C grade with 105.2 tok/s and 35K context.

What context window can HelpingAI2 9B use on RTX 3080 10GB?

On RTX 3080 10GB, HelpingAI2 9B can safely use up to 35K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 3080 10GB See all hardware for HelpingAI2 9B

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