UI-Venus 2 9B on AMD G-Series GX-420MC NAS (8 GB)
Can the AMD G-Series GX-420MC NAS (8 GB) run UI-Venus 2 9B?
Barely: it runs very slowly (tight fit)
Very slow (tight fit). At Q2_K it needs about 5.0 GB of 5.2 GB usable, with an estimated speed of 0.6 tok/s.
- Best quant
Q2_K- Generation speed
- 0.6 tok/s
- Prompt processing
- 0.5 tok/s
- First token (1K prompt)
- 2,141.3 s
- Largest safe context
- 12K
Best app to run it on the AMD G-Series GX-420MC NAS (8 GB)
On Linux
- Usually the first to support new model architectures.
llama-server -hf bartowski/UI-Venus-2-9B-GGUF:Q2_KDownload UI-Venus 2 9B
Recommended for the AMD G-Series GX-420MC NAS (8 GB): Q2_K. Every other quantization has its own download link in the table below.
Quantized files · GGUF
Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.
Download the Q2_K file (opens in a new tab)
huggingface.co/bartowski/UI-Venus-2-9B-GGUF (opens in a new tab)
ollama run hf.co/bartowski/UI-Venus-2-9B-GGUF:Q2_KOriginal weights · Hugging Face
Full-precision files from Ant Group (inclusionAI), for fine-tuning, vLLM, Transformers.
huggingface.co/inclusionAI/UI-Venus-2-9B (opens in a new tab)
How it’s rated
Match score
To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.
| Model quality | 22.1 / 45 | |
|---|---|---|
| Memory fit | 24.0 / 25 | |
| Speed | 1.8 / 15 | |
| Memory use | 15.0 / 15 | |
| Match score | 62.8 / 100 |
Every quantization on the AMD G-Series GX-420MC NAS (8 GB)
| Quant | Memory needed | Available | Speed | Runs | Quality | Download |
|---|---|---|---|---|---|---|
Q2_K | 5.0 GB | 5.2 GB | 0.6 tok/s | Very slowtight fit | 49Fair quality (C) · 49 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking. | GGUF (opens in a new tab) |
Q3_K_M | 5.9 GB | 5.2 GB | 0.4 tok/s | Very slowfar over memory limit | 58Good quality (B) · 58 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking. | GGUF (opens in a new tab) |
Q4_K_M | 7.0 GB | 5.2 GB | — | Won't run | 66Good quality (B) · 66 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 8/100 (quality, fit, speed and memory use) sets the ranking. | GGUF (opens in a new tab) |
Q5_K_M | 8.0 GB | 5.2 GB | — | Won't run | 67Good quality (B) · 67 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. | GGUF (opens in a new tab) |
Q6_K | 9.0 GB | 5.2 GB | — | Won't run | 69Good quality (B) · 69 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. | GGUF (opens in a new tab) |
Q8_0 | 11.3 GB | 5.2 GB | — | Won't run | 70Great quality (A) · 70 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. | GGUF (opens in a new tab) |
F16 | 20.1 GB | 5.2 GB | — | Won't run | 70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. | GGUF (opens in a new tab) Original weights (opens in a new tab) |
Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.
Other hardware that runs it
-
70Great quality (A) · 70 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.
NVIDIA GeForce RTX 4070 Ti SUPER
-
70Great quality (A) · 70 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.
NVIDIA GeForce RTX 4080 SUPER
-
70Great quality (A) · 70 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.
NVIDIA GeForce RTX 5070 Ti
-
70Great quality (A) · 70 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.
NVIDIA GeForce RTX 5080
-
70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.
NVIDIA GeForce RTX 5090
-
69Good quality (B) · 69 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.
NVIDIA GeForce RTX 4070 SUPER
Quick answers
How much memory does UI-Venus 2 9B need on the AMD G-Series GX-420MC NAS 8GB?
At Q2_K it needs about 5.0 GB. The AMD G-Series GX-420MC NAS 8GB has 5.2 GB usable.
How fast is UI-Venus 2 9B on the AMD G-Series GX-420MC NAS 8GB?
About 0.6 tok/s at Q2_K, an estimate from the hardware’s memory bandwidth and the size of the model.
Which quantization of UI-Venus 2 9B should I use on the AMD G-Series GX-420MC NAS 8GB?
Q2_K. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.
Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.