Can HelpingAI2.5 5B i1 run on Radeon RX 7900M 16GB?

YES — Runs Great

C49Usable

Estimated from fit model

HelpingAI2.5 5B i1 needs ~6.1 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q4_K_M quantization, expect ~70 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 6.1 GB, 70.0 tok/s, Runs well

6.1 GB required16.0 GB available

38% VRAM used

Fit status

Runs well

Decode

70.0 tok/s

TTFT

2766 ms

Safe context

285K

Memory

6.1 GB / 16.0 GB

Memory breakdown

Weights3.1 GB

KV Cache0.6 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsHelpingAI2.5 5B i1 on Radeon RX 7900M 16GB
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: 70.0 tok/s decode · 2.8s TTFT (warm) · 175 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	70.0 tok/s	1509 ms	285K
Coding	C	Runs well	70.0 tok/s	2766 ms	285K
Agentic Coding	C	Runs well	70.0 tok/s	4023 ms	285K
Reasoning	C	Runs well	70.0 tok/s	3269 ms	285K
RAG	C	Runs well	70.0 tok/s	5029 ms	285K

Quantization options

How HelpingAI2.5 5B i1 (5B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.0 GB	Low	C46
Q3_K_S	3	2.5 GB	Low	C46
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

MacBook Pro M4 Pro 24GBBudget pick

24 GB Unified (+8)

This setup is broadly balanced for this model.63.4 tok/s decode

~$1,999 MSRP

Frequently asked questions

Can Radeon RX 7900M 16GB run HelpingAI2.5 5B i1?

Yes, Radeon RX 7900M 16GB can run HelpingAI2.5 5B i1 with a C grade (Runs well). Expected decode speed: 70.0 tok/s.

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

HelpingAI2.5 5B i1 (5B parameters) requires approximately 6.1 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 Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, HelpingAI2.5 5B i1 achieves approximately 70.0 tokens per second decode speed with a time-to-first-token of 2766ms using Q4_K_M quantization.

Can Radeon RX 7900M 16GB run HelpingAI2.5 5B i1 for coding?

For coding workloads, HelpingAI2.5 5B i1 on Radeon RX 7900M 16GB receives a C grade with 70.0 tok/s and 285K context.

What context window can HelpingAI2.5 5B i1 use on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, HelpingAI2.5 5B i1 can safely use up to 285K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon RX 7900M 16GB See all hardware for HelpingAI2.5 5B i1

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