RX 7600 XT 16GBコスパ重視
16 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.25.6 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$329 MSRP
Can Nous Hermes 2 SOLAR 10.7B run on RX 6750 XT 12GB?
YES — Tight Fit
C51Usable
Estimated from fit model
Nous Hermes 2 SOLAR 10.7B needs ~9.9 GB VRAM. RX 6750 XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~35 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: 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) — 9.9 GB, 35.1 tok/s, Tight fit
9.9 GB required12.0 GB available
83% VRAM used
Fit status
Tight fit
Decode
35.1 tok/s
TTFT
5519 ms
Safe context
43K
Memory
9.9 GB / 12.0 GB
Memory breakdown
Weights6.5 GB
KV Cache1.3 GB
Runtime0.9 GB
Headroom1.2 GB
See how fast it feels
See how fast it feelsNous Hermes 2 SOLAR 10.7B on RX 6750 XT 12GB
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: 35.1 tok/s decode · 5.5s TTFT (warm) · 88 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 | 35.1 tok/s | 3010 ms | 43K |
| Coding | C | Tight fit | 35.1 tok/s | 5519 ms | 43K |
| Agentic Coding | C | Tight fit | 35.1 tok/s | 8028 ms | 43K |
| Reasoning | C | Tight fit | 35.1 tok/s | 6522 ms | 43K |
| RAG | C | Tight fit | 35.1 tok/s | 10035 ms | 43K |
Inference speed
Nous Hermes 2 SOLAR 10.7B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Nous Hermes 2 SOLAR 10.7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~150 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 149.8 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 117.4 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 105.9 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 100.4 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 93.6 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 85.3 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 71.1 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 67.4 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 60.8 | Tight |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 46.4 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 46.4 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 36.8 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 36.4 | Tight |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 33.7 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 28.3 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 16.8 | Heavy offload |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How Nous Hermes 2 SOLAR 10.7B (10.699999809265137B params) fits at each quantization level on RX 6750 XT 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | C51 |
Q3_K_S | 3 | 5.2 GB | Low | C52 |
NVFP4 | 4 | 6.0 GB | Medium | C52 |
Q4_K_M | 4 | 6.5 GB | Medium | C52 |
Q5_K_M | 5 | 7.7 GB | High | C52 |
Q6_KBest for your GPU | 6 | 8.8 GB | High | C51 |
Q8_0 | 8 | 11.4 GB | Very High | F0 |
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
RX 9060 XT 16GBコスパ最良
16 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.30.9 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$349 MSRP
RX 9070 16GBAMDアップグレード
16 GB VRAM (+4)640 GB/s (+208)
C
Raises estimated decode speed by about 73%.60.8 tok/sデコード
Raises estimated decode speed by about 73%.
Adds memory headroom for longer context windows and future model growth.
〜$479 MSRP
Frequently asked questions
Can RX 6750 XT 12GB run Nous Hermes 2 SOLAR 10.7B?
Yes, RX 6750 XT 12GB can run Nous Hermes 2 SOLAR 10.7B with a C grade (Tight fit). Expected decode speed: 35.1 tok/s.
How much VRAM does Nous Hermes 2 SOLAR 10.7B need?
Nous Hermes 2 SOLAR 10.7B (10.699999809265137B parameters) requires approximately 9.9 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 6750 XT 12GB?
On RX 6750 XT 12GB, Nous Hermes 2 SOLAR 10.7B achieves approximately 35.1 tokens per second decode speed with a time-to-first-token of 5519ms using Q4_K_M quantization.
Can RX 6750 XT 12GB run Nous Hermes 2 SOLAR 10.7B for coding?
For coding workloads, Nous Hermes 2 SOLAR 10.7B on RX 6750 XT 12GB receives a C grade with 35.1 tok/s and 43K context.
What context window can Nous Hermes 2 SOLAR 10.7B use on RX 6750 XT 12GB?
On RX 6750 XT 12GB, Nous Hermes 2 SOLAR 10.7B can safely use up to 43K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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