RX 7900 XT 20GBコスパ重視
20 GB VRAM (+4)800 GB/s (+512)
C
Raises estimated decode speed by about 187%.73.5 tok/sデコード
Raises estimated decode speed by about 187%.
Adds memory headroom for longer context windows and future model growth.
〜$899 MSRP
Can Nous Hermes 2 SOLAR 10.7B run on RX 7600 XT 16GB?
YES — Runs Great
C51Usable
Estimated from fit model
Nous Hermes 2 SOLAR 10.7B needs ~10.3 GB VRAM. RX 7600 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~26 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 10.3 GB, 25.6 tok/s, Runs well
10.3 GB required16.0 GB available
64% VRAM used
Fit status
Runs well
Decode
25.6 tok/s
TTFT
7565 ms
Safe context
89K
Memory
10.3 GB / 16.0 GB
Memory breakdown
Weights6.5 GB
KV Cache1.3 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsNous Hermes 2 SOLAR 10.7B on RX 7600 XT 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: 25.6 tok/s decode · 7.6s TTFT (warm) · 64 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 | 25.6 tok/s | 4126 ms | 89K |
| Coding | C | Runs well | 25.6 tok/s | 7565 ms | 89K |
| Agentic Coding | C | Runs well | 25.6 tok/s | 11003 ms | 89K |
| Reasoning | C | Runs well | 25.6 tok/s | 8940 ms | 89K |
| RAG | C | Runs well | 25.6 tok/s | 13754 ms | 89K |
Quantization options
How Nous Hermes 2 SOLAR 10.7B (10.699999809265137B params) fits at each quantization level on RX 7600 XT 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | C48 |
Q3_K_S | 3 | 5.2 GB | Low | C49 |
NVFP4 | 4 | 6.0 GB | Medium | C50 |
Q4_K_M | 4 | 6.5 GB | Medium | C50 |
Q5_K_M | 5 | 7.7 GB | High | C51 |
Q6_K | 6 | 8.8 GB | High | C51 |
Q8_0Best for your GPU | 8 | 11.4 GB | Very High | C51 |
F16 | 16 | 21.9 GB | Maximum | F0 |
Get started
Copy-paste commands to run Nous Hermes 2 SOLAR 10.7B on your machine.
Run
lms load hf-thebloke--nous-hermes-2-solar-10-7b-gguf && lms server startアップグレードオプション
Nous Hermes 2 SOLAR 10.7Bを快適に動かすハードウェア
Frequently asked questions
Can RX 7600 XT 16GB run Nous Hermes 2 SOLAR 10.7B?
Yes, RX 7600 XT 16GB can run Nous Hermes 2 SOLAR 10.7B with a C grade (Runs well). Expected decode speed: 25.6 tok/s.
How much VRAM does Nous Hermes 2 SOLAR 10.7B need?
Nous Hermes 2 SOLAR 10.7B (10.699999809265137B parameters) requires approximately 10.3 GB of memory with Q4_K_M quantization.
What is the best quantization for Nous Hermes 2 SOLAR 10.7B?
The recommended quantization for Nous Hermes 2 SOLAR 10.7B is Q4_K_M, which balances quality and memory efficiency.
What speed will Nous Hermes 2 SOLAR 10.7B run at on RX 7600 XT 16GB?
On RX 7600 XT 16GB, Nous Hermes 2 SOLAR 10.7B achieves approximately 25.6 tokens per second decode speed with a time-to-first-token of 7565ms using Q4_K_M quantization.
Can RX 7600 XT 16GB run Nous Hermes 2 SOLAR 10.7B for coding?
For coding workloads, Nous Hermes 2 SOLAR 10.7B on RX 7600 XT 16GB receives a C grade with 25.6 tok/s and 89K context.
What context window can Nous Hermes 2 SOLAR 10.7B use on RX 7600 XT 16GB?
On RX 7600 XT 16GB, Nous Hermes 2 SOLAR 10.7B can safely use up to 89K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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