Can HelpingAI2 9B run on RTX 3050 8GB?

Q: What should I upgrade first if HelpingAI2 9B feels slow on RTX 3050 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — With Offload

C48Usable

Estimated from fit model

HelpingAI2 9B needs ~8.2 GB VRAM. RTX 3050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~16 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 8.2 GB, 15.9 tok/s, Runs with offload (needs ~0.2 GB host RAM)

8.2 GB required8.0 GB available

102% VRAM needed

0.2 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.2 GB host RAM)

Decode

15.9 tok/s

TTFT

12159 ms

Safe context

12K

Memory

8.2 GB / 8.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsHelpingAI2 9B on RTX 3050 8GB
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: 15.9 tok/s decode · 12.2s TTFT (warm) · 40 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload	22.6 tok/s	4668 ms	12K
Coding	C	Runs with offload (needs ~0.2 GB host RAM)	15.9 tok/s	12159 ms	12K
Agentic Coding	D	Very compromised (needs ~0.8 GB host RAM)	12.4 tok/s	22786 ms	12K
Reasoning	C	Runs with offload (needs ~0.2 GB host RAM)	15.9 tok/s	14369 ms	12K
RAG	D	Very compromised (needs ~0.8 GB host RAM)	12.4 tok/s

Quantization options

How HelpingAI2 9B (9B params) fits at each quantization level on RTX 3050 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C53
Q3_K_S	3	4.4 GB	Low	C53
NVFP4Best for your GPU

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 (+4)360 GB/s (+136)

Raises estimated decode speed by about 129%.36.4 tok/s decode

Raises estimated decode speed by about 129%.

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

~$329 MSRP

RTX 5060 Ti 16GBBest value

16 GB VRAM (+8)448 GB/s (+224)

Raises estimated decode speed by about 218%.50.6 tok/s decode

Raises estimated decode speed by about 218%.

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

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+8)288 GB/s (+64)

Raises estimated decode speed by about 121%.35.2 tok/s decode

Raises estimated decode speed by about 121%.

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

~$499 MSRP

Frequently asked questions

Can RTX 3050 8GB run HelpingAI2 9B?

Yes, RTX 3050 8GB can run HelpingAI2 9B with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 15.9 tok/s.

How much VRAM does HelpingAI2 9B need?

HelpingAI2 9B (9B parameters) requires approximately 8.2 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 3050 8GB?

On RTX 3050 8GB, HelpingAI2 9B achieves approximately 15.9 tokens per second decode speed with a time-to-first-token of 12159ms using Q4_K_M quantization.

Can RTX 3050 8GB run HelpingAI2 9B for coding?

For coding workloads, HelpingAI2 9B on RTX 3050 8GB receives a C grade with 15.9 tok/s and 12K context.

What context window can HelpingAI2 9B use on RTX 3050 8GB?

On RTX 3050 8GB, HelpingAI2 9B can safely use up to 12K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if HelpingAI2 9B feels slow on RTX 3050 8GB?

See all results for RTX 3050 8GB See all hardware for HelpingAI2 9B

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