Can HelpingAI2.5 5B i1 run on RX 5600 XT 6GB?

YES — Tight Fit

C51Usable

Estimated from fit model

HelpingAI2.5 5B i1 needs ~5.1 GB VRAM. RX 5600 XT 6GB has 6.0 GB. With Q4_K_M quantization, expect ~49 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: 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) — 5.1 GB, 49.1 tok/s, Tight fit

5.1 GB required6.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

49.1 tok/s

TTFT

3943 ms

Safe context

40K

Memory

5.1 GB / 6.0 GB

Memory breakdown

Weights3.1 GB

KV Cache0.6 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsHelpingAI2.5 5B 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: 49.1 tok/s decode · 3.9s TTFT (warm) · 123 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	49.1 tok/s	2151 ms	40K
Coding	C	Tight fit	49.1 tok/s	3943 ms	40K
Agentic Coding	C	Runs with offload	49.1 tok/s	5735 ms	40K
Reasoning	C	Tight fit	49.1 tok/s	4660 ms	40K
RAG	C	Runs with offload	49.1 tok/s	7169 ms	40K

Quantization options

How HelpingAI2.5 5B i1 (5B params) fits at each quantization level on RX 5600 XT 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.0 GB	Low	C54
Q3_K_S	3	2.5 GB	Low	C54
NVFP4	4

Get started

Copy-paste commands to run HelpingAI2.5 5B i1 on your machine.

Run

lms load hf-mradermacher--helpingai2-5-5b-i1-gguf && lms server start

Upgrade options

Hardware that runs HelpingAI2.5 5B i1 well

RX 580 8GBBudget pick

8 GB VRAM (+2)

Adds memory headroom for longer context windows and future model growth.36.1 tok/s decode

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

~$229 MSRP

RX 9060 8GBBest value

8 GB VRAM (+2)

Adds memory headroom for longer context windows and future model growth.59.5 tok/s decode

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

~$249 MSRP

RX 7600 8GBAMD upgrade

8 GB VRAM (+2)

Adds memory headroom for longer context windows and future model growth.54.8 tok/s decode

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

~$269 MSRP

Frequently asked questions

Can RX 5600 XT 6GB run HelpingAI2.5 5B i1?

Yes, RX 5600 XT 6GB can run HelpingAI2.5 5B i1 with a C grade (Tight fit). Expected decode speed: 49.1 tok/s.

How much VRAM does HelpingAI2.5 5B i1 need?

HelpingAI2.5 5B i1 (5B parameters) requires approximately 5.1 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI2.5 5B i1?

The recommended quantization for HelpingAI2.5 5B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will HelpingAI2.5 5B i1 run at on RX 5600 XT 6GB?

On RX 5600 XT 6GB, HelpingAI2.5 5B i1 achieves approximately 49.1 tokens per second decode speed with a time-to-first-token of 3943ms using Q4_K_M quantization.

Can RX 5600 XT 6GB run HelpingAI2.5 5B i1 for coding?

For coding workloads, HelpingAI2.5 5B i1 on RX 5600 XT 6GB receives a C grade with 49.1 tok/s and 40K context.

What context window can HelpingAI2.5 5B i1 use on RX 5600 XT 6GB?

On RX 5600 XT 6GB, HelpingAI2.5 5B i1 can safely use up to 40K tokens of context. 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 HelpingAI2.5 5B i1

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