Can HelpingAI 15B i1 run on Radeon RX 7900M 16GB?

YES — Tight Fit

C50Usable

Estimated from fit model

HelpingAI 15B i1 needs ~13.4 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q4_K_M quantization, expect ~37 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 13.4 GB, 37.1 tok/s, Tight fit

13.4 GB required16.0 GB available

84% VRAM used

Fit status

Tight fit

Decode

37.1 tok/s

TTFT

5213 ms

Safe context

40K

Memory

13.4 GB / 16.0 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsHelpingAI 15B 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: 37.1 tok/s decode · 5.2s TTFT (warm) · 93 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	37.1 tok/s	2843 ms	40K
Coding	C	Tight fit	37.1 tok/s	5213 ms	40K
Agentic Coding	C	Tight fit	37.1 tok/s	7582 ms	40K
Reasoning	C	Tight fit	37.1 tok/s	6160 ms	40K
RAG	C	Tight fit	37.1 tok/s	9477 ms	40K

Quantization options

How HelpingAI 15B i1 (15B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C49
Q3_K_S	3	7.4 GB	Low	C51
NVFP4	4

Get started

Copy-paste commands to run HelpingAI 15B i1 on your machine.

Run

lms load hf-mradermacher--helpingai-15b-i1-gguf && lms server start

Upgrade options

Hardware that runs HelpingAI 15B i1 well

RX 7900 XT 20GBBudget pick

20 GB VRAM (+4)800 GB/s (+224)

Raises estimated decode speed by about 42%.52.5 tok/s decode

Raises estimated decode speed by about 42%.

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

~$899 MSRP

RX 7900 XTX 24GBBest value

24 GB VRAM (+8)960 GB/s (+384)

Raises estimated decode speed by about 104%.75.5 tok/s decode

Raises estimated decode speed by about 104%.

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

~$999 MSRP

AMD Instinct MI100 32GBAMD upgrade

32 GB VRAM (+16)1228 GB/s (+652)

Raises estimated decode speed by about 135%.87.2 tok/s decode

Raises estimated decode speed by about 135%.

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

~$11,500 MSRP

Frequently asked questions

Can Radeon RX 7900M 16GB run HelpingAI 15B i1?

Yes, Radeon RX 7900M 16GB can run HelpingAI 15B i1 with a C grade (Tight fit). Expected decode speed: 37.1 tok/s.

How much VRAM does HelpingAI 15B i1 need?

HelpingAI 15B i1 (15B parameters) requires approximately 13.4 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI 15B i1?

The recommended quantization for HelpingAI 15B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will HelpingAI 15B i1 run at on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, HelpingAI 15B i1 achieves approximately 37.1 tokens per second decode speed with a time-to-first-token of 5213ms using Q4_K_M quantization.

Can Radeon RX 7900M 16GB run HelpingAI 15B i1 for coding?

For coding workloads, HelpingAI 15B i1 on Radeon RX 7900M 16GB receives a C grade with 37.1 tok/s and 40K context.

What context window can HelpingAI 15B i1 use on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, HelpingAI 15B 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 Radeon RX 7900M 16GB See all hardware for HelpingAI 15B i1

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