RTX 5060 Ti 16GB经济之选16 GB VRAM (+4)448 GB/s (+16)
C
Adds memory headroom for longer context windows and future model growth.41.3 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Can SOLAR 10.7B Instruct v1.0 uncensored run on RTX 4000 Ada Laptop 12GB?
YES — Tight Fit
C52Usable
Estimated from fit model
SOLAR 10.7B Instruct v1.0 uncensored needs ~9.9 GB VRAM. RTX 4000 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~51 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, 50.7 tok/s, Tight fit
9.9 GB required12.0 GB available
83% VRAM used
Fit status
Tight fit
Decode
50.7 tok/s
TTFT
3816 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 feelsSOLAR 10.7B Instruct v1.0 uncensored on RTX 4000 Ada Laptop 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: 50.7 tok/s decode · 3.8s TTFT (warm) · 127 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 | 50.7 tok/s | 2082 ms | 43K |
| Coding | C | Tight fit | 50.7 tok/s | 3816 ms | 43K |
| Agentic Coding | C | Tight fit | 50.7 tok/s | 5551 ms | 43K |
| Reasoning | C | Tight fit | 50.7 tok/s | 4510 ms | 43K |
| RAG | C | Tight fit | 50.7 tok/s | 6938 ms | 43K |
Quantization options
How SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B params) fits at each quantization level on RTX 4000 Ada Laptop 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 SOLAR 10.7B Instruct v1.0 uncensored on your machine.
Run
lms load hf-thebloke--solar-10-7b-instruct-v1-0-uncensored-gguf && lms server start升级选项
能流畅运行 SOLAR 10.7B Instruct v1.0 uncensored 的硬件
RTX 5060 Ti 16GB经济之选RTX 4060 Ti 16GB最佳性价比16 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.33.8 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
RTX 5070 Ti 16GBNVIDIA 升级16 GB VRAM (+4)896 GB/s (+464)
B
Raises estimated decode speed by about 68%.85.2 tok/s 解码
Raises estimated decode speed by about 68%.
Adds memory headroom for longer context windows and future model growth.
~$749 MSRP
Frequently asked questions
Can RTX 4000 Ada Laptop 12GB run SOLAR 10.7B Instruct v1.0 uncensored?
Yes, RTX 4000 Ada Laptop 12GB can run SOLAR 10.7B Instruct v1.0 uncensored with a C grade (Tight fit). Expected decode speed: 50.7 tok/s.
How much VRAM does SOLAR 10.7B Instruct v1.0 uncensored need?
SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B parameters) requires approximately 9.9 GB of memory with Q4_K_M quantization.
What is the best quantization for SOLAR 10.7B Instruct v1.0 uncensored?
The recommended quantization for SOLAR 10.7B Instruct v1.0 uncensored is Q4_K_M, which balances quality and memory efficiency.
What speed will SOLAR 10.7B Instruct v1.0 uncensored run at on RTX 4000 Ada Laptop 12GB?
On RTX 4000 Ada Laptop 12GB, SOLAR 10.7B Instruct v1.0 uncensored achieves approximately 50.7 tokens per second decode speed with a time-to-first-token of 3816ms using Q4_K_M quantization.
Can RTX 4000 Ada Laptop 12GB run SOLAR 10.7B Instruct v1.0 uncensored for coding?
For coding workloads, SOLAR 10.7B Instruct v1.0 uncensored on RTX 4000 Ada Laptop 12GB receives a C grade with 50.7 tok/s and 43K context.
What context window can SOLAR 10.7B Instruct v1.0 uncensored use on RTX 4000 Ada Laptop 12GB?
On RTX 4000 Ada Laptop 12GB, SOLAR 10.7B Instruct v1.0 uncensored 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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<iframe src="https://willitrunai.com/embed/hf-thebloke--solar-10-7b-instruct-v1-0-uncensored-gguf-on-rtx-4000-ada-laptop-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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