RTX 5060 Ti 16GBOpción económica16 GB VRAM (+4)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.41.3 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$449 MSRP
Can SOLAR 10.7B v1.0 run on RTX 4070 12GB?
YES — Tight Fit
C52Usable
Estimated from fit model
SOLAR 10.7B v1.0 needs ~9.9 GB VRAM. RTX 4070 12GB has 12.0 GB. With Q4_K_M quantization, expect ~61 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: MediumStack: 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, 60.8 tok/s, Tight fit
9.9 GB required12.0 GB available
83% VRAM used
Fit status
Tight fit
Decode
60.8 tok/s
TTFT
3184 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 v1.0 on RTX 4070 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: 60.8 tok/s decode · 3.2s TTFT (warm) · 152 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 | 60.8 tok/s | 1737 ms | 43K |
| Coding | C | Tight fit | 60.8 tok/s | 3184 ms | 43K |
| Agentic Coding | C | Tight fit | 60.8 tok/s | 4631 ms | 43K |
| Reasoning | C | Tight fit | 60.8 tok/s | 3763 ms | 43K |
| RAG | C | Tight fit | 60.8 tok/s | 5789 ms | 43K |
Quantization options
How SOLAR 10.7B v1.0 (10.699999809265137B params) fits at each quantization level on RTX 4070 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | C50 |
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 | C51 |
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 v1.0 on your machine.
Run
lms load hf-mradermacher--solar-10-7b-v1-0-gguf && lms server startOpciones de mejora
Hardware que ejecuta bien SOLAR 10.7B v1.0
RTX 5060 Ti 16GBOpción económicaRTX 5070 Ti 16GBMejor relación calidad-precio16 GB VRAM (+4)896 GB/s (+392)
B
Sube la velocidad estimada de decodificación alrededor de un 40%.85.2 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 40%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$749 MSRP
RTX 4070 Ti Super 16GBMejora NVIDIA16 GB VRAM (+4)672 GB/s (+168)
B
Sube la velocidad estimada de decodificación alrededor de un 42%.86.5 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 42%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$799 MSRP
Frequently asked questions
Can RTX 4070 12GB run SOLAR 10.7B v1.0?
Yes, RTX 4070 12GB can run SOLAR 10.7B v1.0 with a C grade (Tight fit). Expected decode speed: 60.8 tok/s.
How much VRAM does SOLAR 10.7B v1.0 need?
SOLAR 10.7B v1.0 (10.699999809265137B parameters) requires approximately 9.9 GB of memory with Q4_K_M quantization.
What is the best quantization for SOLAR 10.7B v1.0?
The recommended quantization for SOLAR 10.7B v1.0 is Q4_K_M, which balances quality and memory efficiency.
What speed will SOLAR 10.7B v1.0 run at on RTX 4070 12GB?
On RTX 4070 12GB, SOLAR 10.7B v1.0 achieves approximately 60.8 tokens per second decode speed with a time-to-first-token of 3184ms using Q4_K_M quantization.
Can RTX 4070 12GB run SOLAR 10.7B v1.0 for coding?
For coding workloads, SOLAR 10.7B v1.0 on RTX 4070 12GB receives a C grade with 60.8 tok/s and 43K context.
What context window can SOLAR 10.7B v1.0 use on RTX 4070 12GB?
On RTX 4070 12GB, SOLAR 10.7B v1.0 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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