RX 7900 XT 20GBBudget pick
20 GB VRAM (+4)800 GB/s (+224)
C
Raises estimated decode speed by about 41%.56.2 tok/s decode
Raises estimated decode speed by about 41%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
Can NousResearch Hermes 4 14B run on Radeon RX 7900M 16GB?
YES — Runs Great
C54Usable
○Estimated from fit model
NousResearch Hermes 4 14B needs ~12.7 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q4_K_M quantization, expect ~40 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
Share:
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.
Select quantization to explore
Q4_K_M (Medium quality) — 12.7 GB, 39.8 tok/s, Runs well
12.7 GB required16.0 GB available
79% VRAM used
Fit status
Runs well
Decode
39.8 tok/s
TTFT
4865 ms
Safe context
48K
Memory
12.7 GB / 16.0 GB
Memory breakdown
Weights8.5 GB
KV Cache1.6 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsNousResearch Hermes 4 14B 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: 39.8 tok/s decode · 4.9s TTFT (warm) · 100 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 | 39.8 tok/s | 2654 ms | 48K |
| Coding | C | Runs well | 39.8 tok/s | 4865 ms | 48K |
| Agentic Coding | C | Tight fit | 39.8 tok/s | 7076 ms | 48K |
| Reasoning | C | Runs well | 39.8 tok/s | 5750 ms | 48K |
| RAG | C | Tight fit | 39.8 tok/s | 8846 ms | 48K |
Quantization options
How NousResearch Hermes 4 14B (14B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C49 |
Q3_K_S | 3 | 6.9 GB | Low | C51 |
NVFP4 | 4 | 7.8 GB | Medium | C51 |
Q4_K_M | 4 | 8.5 GB | Medium | C51 |
Q5_K_M | 5 | 10.1 GB | High | C51 |
Q6_KBest for your GPU | 6 | 11.5 GB | High | C51 |
Q8_0 | 8 | 15.0 GB | Very High | F0 |
F16 | 16 | 28.7 GB | Maximum | F0 |
Get started
Copy-paste commands to run NousResearch Hermes 4 14B on your machine.
Run
lms load hf-bartowski--nousresearch-hermes-4-14b-gguf && lms server startUpgrade options
