RTX 5070 Ti 16GBコスパ重視16 GB VRAM (+4)
B
Adds memory headroom for longer context windows and future model growth.87.9 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$749 MSRP
Can SOLAR 10.7B Instruct v1.0 uncensored run on RTX 3080 12GB?
YES — Tight Fit
C54Usable
Estimated from fit model
SOLAR 10.7B Instruct v1.0 uncensored needs ~10.2 GB VRAM. RTX 3080 12GB has 12.0 GB. With Q4_K_M quantization, expect ~106 tok/s.
Runtime: OllamaCapacity: TightBandwidth: HighStack: BasicBottleneck: 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) — 10.2 GB, 106.2 tok/s, Tight fit
10.2 GB required12.0 GB available
85% VRAM used
Fit status
Tight fit
Decode
106.2 tok/s
TTFT
1823 ms
Safe context
39K
Memory
10.2 GB / 12.0 GB
Memory breakdown
Weights6.5 GB
KV Cache1.3 GB
Runtime1.2 GB
Headroom1.2 GB
See how fast it feels
See how fast it feelsSOLAR 10.7B Instruct v1.0 uncensored on RTX 3080 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: 106.2 tok/s decode · 1.8s TTFT (warm) · 266 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 | B | Runs well | 106.2 tok/s | 994 ms | 39K |
| Coding | C | Tight fit | 106.2 tok/s | 1823 ms | 39K |
| Agentic Coding | C | Runs with offload | 106.2 tok/s | 2652 ms | 39K |
| Reasoning | C | Tight fit | 106.2 tok/s | 2155 ms | 39K |
| RAG | C | Runs with offload | 106.2 tok/s | 3315 ms | 39K |
Quantization options
How SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B params) fits at each quantization level on RTX 3080 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アップグレードオプション
SOLAR 10.7B Instruct v1.0 uncensoredを快適に動かすハードウェア
RTX 5070 Ti 16GBコスパ重視RTX 4070 Ti Super 16GBコスパ最良16 GB VRAM (+4)
B
Adds memory headroom for longer context windows and future model growth.82.4 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$799 MSRP
RTX 4080 Super 16GBNVIDIAアップグレード16 GB VRAM (+4)
B
Adds memory headroom for longer context windows and future model growth.93.6 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$999 MSRP
Frequently asked questions
Can RTX 3080 12GB run SOLAR 10.7B Instruct v1.0 uncensored?
Yes, RTX 3080 12GB can run SOLAR 10.7B Instruct v1.0 uncensored with a C grade (Tight fit). Expected decode speed: 106.2 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 10.2 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 3080 12GB?
On RTX 3080 12GB, SOLAR 10.7B Instruct v1.0 uncensored achieves approximately 106.2 tokens per second decode speed with a time-to-first-token of 1823ms using Q4_K_M quantization.
Can RTX 3080 12GB run SOLAR 10.7B Instruct v1.0 uncensored for coding?
For coding workloads, SOLAR 10.7B Instruct v1.0 uncensored on RTX 3080 12GB receives a C grade with 106.2 tok/s and 39K context.
What context window can SOLAR 10.7B Instruct v1.0 uncensored use on RTX 3080 12GB?
On RTX 3080 12GB, SOLAR 10.7B Instruct v1.0 uncensored can safely use up to 39K 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-3080-12gb" 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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