Can HelpingAI2 9B i1 run on RX 5600 XT 6GB?

Q: What should I upgrade first if HelpingAI2 9B i1 feels slow on RX 5600 XT 6GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q3_K_S

D38Poor

Estimated from fit model

HelpingAI2 9B i1 needs ~7.0 GB VRAM. RX 5600 XT 6GB has 6.0 GB. With Q3_K_S quantization, expect ~17 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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.

HelpingAI2 9B i1 at Q4_K_M needs 8.0 GB — too much for RX 5600 XT 6GB (6.0 GB). Runs at Q3_K_S (7.0 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 8.0 GB, exceeds 6.0 GB available

8.0 GB required6.0 GB available

133% VRAM needed

2.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

11.0 tok/s

TTFT

17543 ms

Safe context

Memory

8.0 GB / 6.0 GB

Offload

30%

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsHelpingAI2 9B i1 on RX 5600 XT 6GB
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: 11.0 tok/s decode · 17.5s TTFT (warm) · 28 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 0.6 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	12.7 tok/s	8296 ms	4K
Coding	F	Too heavy	11.0 tok/s	17543 ms	4K
Agentic Coding	F	Too heavy	8.5 tok/s	33072 ms	4K
Reasoning	F	Too heavy	11.0 tok/s	20733 ms	4K
RAG	F	Too heavy	8.5 tok/s	41340 ms	4K

Quantization options

How HelpingAI2 9B i1 (9B params) fits at each quantization level on RX 5600 XT 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	3.5 GB	Low	C53
Q3_K_S	3	4.4 GB	Low	F0

Get started

Copy-paste commands to run HelpingAI2 9B i1 on your machine.

Run

lms load hf-mradermacher--helpingai2-9b-i1-gguf && lms server start

Upgrade options

Hardware that runs HelpingAI2 9B i1 well

RX 580 8GBBudget pick

8 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.13.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$229 MSRP

RX 9060 8GBBest value

8 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.23.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$249 MSRP

RX 7600 8GBAMD upgrade

8 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.21.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$269 MSRP

RTX 3080 Ti 12GBBiggest leap

12 GB VRAM (+6)912 GB/s (+624)

Makes the model fit on the accelerator instead of staying completely out of reach.103.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,199 MSRP

Frequently asked questions

Can RX 5600 XT 6GB run HelpingAI2 9B i1?

Yes, RX 5600 XT 6GB can run HelpingAI2 9B i1 at Q3_K_S quantization (Very compromised (needs ~0.6 GB host RAM)). The recommended Q4_K_M requires 8.0 GB which exceeds available memory, but at Q3_K_S it needs only 7.0 GB. Expected decode speed: 17.3 tok/s.

How much VRAM does HelpingAI2 9B i1 need?

HelpingAI2 9B i1 (9B parameters) requires approximately 8.0 GB at Q4_K_M quantization. On RX 5600 XT 6GB, it fits at Q3_K_S using 7.0 GB.

What is the best quantization for HelpingAI2 9B i1?

The recommended quantization is Q4_K_M, but on RX 5600 XT 6GB the best fitting quantization is Q3_K_S, which uses 7.0 GB.

What speed will HelpingAI2 9B i1 run at on RX 5600 XT 6GB?

On RX 5600 XT 6GB, HelpingAI2 9B i1 achieves approximately 17.3 tokens per second decode speed with a time-to-first-token of 11187ms using Q3_K_S quantization.

Can RX 5600 XT 6GB run HelpingAI2 9B i1 for coding?

For coding workloads, HelpingAI2 9B i1 on RX 5600 XT 6GB receives a F grade with 11.0 tok/s and 4K context.

What context window can HelpingAI2 9B i1 use on RX 5600 XT 6GB?

On RX 5600 XT 6GB, HelpingAI2 9B i1 can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 5600 XT 6GB See all hardware for HelpingAI2 9B i1

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