NVIDIA A100 80GBコスパ重視80 GB VRAM (+16)2039 GB/s (+1439)
C
Raises estimated decode speed by about 109%.149.8 tok/sデコード
Raises estimated decode speed by about 109%.
Adds memory headroom for longer context windows and future model growth.
〜$15,000 MSRP
Can SOLAR 10.7B Instruct v1.0 uncensored run on NVIDIA A16 64GB?
YES — Runs Great
C46Usable
Estimated from fit model
SOLAR 10.7B Instruct v1.0 uncensored needs ~15.4 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~72 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 15.4 GB, 71.7 tok/s, Runs well
15.4 GB required64.0 GB available
24% VRAM used
Fit status
Runs well
Decode
71.7 tok/s
TTFT
2700 ms
Safe context
636K
Memory
15.4 GB / 64.0 GB
Memory breakdown
Weights6.5 GB
KV Cache1.3 GB
Runtime1.2 GB
Headroom6.4 GB
See how fast it feels
See how fast it feelsSOLAR 10.7B Instruct v1.0 uncensored on NVIDIA A16 64GB
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: 71.7 tok/s decode · 2.7s TTFT (warm) · 179 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 | 71.7 tok/s | 1473 ms | 636K |
| Coding | C | Runs well | 71.7 tok/s | 2700 ms | 636K |
| Agentic Coding | C | Runs well | 71.7 tok/s | 3927 ms | 636K |
| Reasoning | C | Runs well | 71.7 tok/s | 3191 ms | 636K |
| RAG | C | Runs well | 71.7 tok/s | 4909 ms | 636K |
Quantization options
How SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | C40 |
Q3_K_S | 3 | 5.2 GB | Low | C40 |
NVFP4 | 4 | 6.0 GB | Medium | C41 |
Q4_K_M | 4 | 6.5 GB | Medium | C41 |
Q5_K_M | 5 | 7.7 GB | High | C41 |
Q6_K | 6 | 8.8 GB | High | C41 |
Q8_0 | 8 | 11.4 GB | Very High | C41 |
F16Best for your GPU | 16 | 21.9 GB | Maximum | C44 |
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
NVIDIA A100 80GBコスパ重視NVIDIA A800 80GBコスパ最良80 GB VRAM (+16)1935 GB/s (+1335)
C
Raises estimated decode speed by about 109%.149.8 tok/sデコード
Raises estimated decode speed by about 109%.
Adds memory headroom for longer context windows and future model growth.
〜$15,000 MSRP
NVIDIA H800 80GBNVIDIAアップグレード80 GB VRAM (+16)3000 GB/s (+2400)
C
Raises estimated decode speed by about 109%.149.8 tok/sデコード
Raises estimated decode speed by about 109%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Frequently asked questions
Can NVIDIA A16 64GB run SOLAR 10.7B Instruct v1.0 uncensored?
Yes, NVIDIA A16 64GB can run SOLAR 10.7B Instruct v1.0 uncensored with a C grade (Runs well). Expected decode speed: 71.7 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 15.4 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 NVIDIA A16 64GB?
On NVIDIA A16 64GB, SOLAR 10.7B Instruct v1.0 uncensored achieves approximately 71.7 tokens per second decode speed with a time-to-first-token of 2700ms using Q4_K_M quantization.
Can NVIDIA A16 64GB run SOLAR 10.7B Instruct v1.0 uncensored for coding?
For coding workloads, SOLAR 10.7B Instruct v1.0 uncensored on NVIDIA A16 64GB receives a C grade with 71.7 tok/s and 636K context.
What context window can SOLAR 10.7B Instruct v1.0 uncensored use on NVIDIA A16 64GB?
On NVIDIA A16 64GB, SOLAR 10.7B Instruct v1.0 uncensored can safely use up to 636K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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