Can dolphin v2 8b abliterated i1 run on Radeon RX 7900M 16GB?

YES — Runs Great

C52Usable

○Estimated from fit model

dolphin v2 8b abliterated i1 needs ~8.3 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) — 8.3 GB, 69.6 tok/s, Runs well

8.3 GB required16.0 GB available

52% VRAM used

Fit status

Runs well

Decode

69.6 tok/s

TTFT

2780 ms

Safe context

147K

Memory

8.3 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsdolphin v2 8b abliterated 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: 69.6 tok/s decode · 2.8s TTFT (warm) · 174 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	69.6 tok/s	1516 ms	147K
Coding	C	Runs well	69.6 tok/s	2780 ms	147K
Agentic Coding	C	Runs well	69.6 tok/s	4044 ms	147K
Reasoning	C	Runs well	69.6 tok/s	3285 ms	147K
RAG	C	Runs well	69.6 tok/s	5055 ms	147K

Quantization options

How dolphin v2 8b abliterated i1 (8B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C47
Q3_K_S	3	3.9 GB	Low	C48
NVFP4	4

Get started

Copy-paste commands to run dolphin v2 8b abliterated i1 on your machine.

Run

lms load hf-mradermacher--dolphin-v2-8b-abliterated-i1-gguf && lms server start

Frequently asked questions

See all results for Radeon RX 7900M 16GB See all hardware for dolphin v2 8b abliterated i1

URL: https://willitrunai.com/can-run/hf-mradermacher--dolphin-v2-8b-abliterated-i1-gguf-on-rx-7900m-16gb

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