SOLAR 10.7B v1.0 needs ~12.2 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~92 tok/s.
Operating mode
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
Fit status
Runs well
Decode
92.4 tok/s
TTFT
2096 ms
Safe context
269K
Memory
12.2 GB / 32.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 92.4 tok/s | 1143 ms | 269K |
| Coding | C | Runs well | 92.4 tok/s | 2096 ms | 269K |
| Agentic Coding | C | Runs well | 92.4 tok/s | 3048 ms | 269K |
| Reasoning | C | Runs well | 92.4 tok/s | 2477 ms | 269K |
| RAG | C | Runs well | 92.4 tok/s | 3810 ms | 269K |
How SOLAR 10.7B v1.0 (10.699999809265137B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | C43 |
Q3_K_S | 3 | 5.2 GB | Low | C43 |
NVFP4 | 4 | 6.0 GB | Medium | C44 |
Q4_K_M | 4 | 6.5 GB | Medium | C44 |
Q5_K_M | 5 | 7.7 GB | High | C44 |
Q6_K | 6 | 8.8 GB | High | C45 |
Q8_0 | 8 | 11.4 GB | Very High | C46 |
F16Best for your GPU | 16 | 21.9 GB | Maximum | C49 |
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 startYes, NVIDIA V100 32GB can run SOLAR 10.7B v1.0 with a C grade (Runs well). Expected decode speed: 92.4 tok/s.
SOLAR 10.7B v1.0 (10.699999809265137B parameters) requires approximately 12.2 GB of memory with Q4_K_M quantization.
The recommended quantization for SOLAR 10.7B v1.0 is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA V100 32GB, SOLAR 10.7B v1.0 achieves approximately 92.4 tokens per second decode speed with a time-to-first-token of 2096ms using Q4_K_M quantization.
For coding workloads, SOLAR 10.7B v1.0 on NVIDIA V100 32GB receives a C grade with 92.4 tok/s and 269K context.
On NVIDIA V100 32GB, SOLAR 10.7B v1.0 can safely use up to 269K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--solar-10-7b-v1-0-gguf-on-v100-32gb" 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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