SOLAR 10.7B v1.0 needs ~11.4 GB VRAM. NVIDIA A30 24GB has 24.0 GB. With Q4_K_M quantization, expect ~112 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
111.5 tok/s
TTFT
1736 ms
Safe context
177K
Memory
11.4 GB / 24.0 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 | 111.5 tok/s | 947 ms | 177K |
| Coding | C | Runs well | 111.5 tok/s | 1736 ms | 177K |
| Agentic Coding | C | Runs well | 111.5 tok/s | 2526 ms | 177K |
| Reasoning | C | Runs well | 111.5 tok/s | 2052 ms | 177K |
| RAG | C | Runs well | 111.5 tok/s | 3157 ms | 177K |
How SOLAR 10.7B v1.0 (10.699999809265137B params) fits at each quantization level on NVIDIA A30 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | C45 |
Q3_K_S | 3 | 5.2 GB | Low | C45 |
NVFP4 | 4 | 6.0 GB | Medium | C46 |
Q4_K_M | 4 | 6.5 GB | Medium | C46 |
Q5_K_M | 5 | 7.7 GB | High | C47 |
Q6_K | 6 | 8.8 GB | High | C47 |
Q8_0Best for your GPU | 8 | 11.4 GB | Very High | C49 |
F16 | 16 | 21.9 GB | Maximum | F0 |
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 A30 24GB can run SOLAR 10.7B v1.0 with a C grade (Runs well). Expected decode speed: 111.5 tok/s.
SOLAR 10.7B v1.0 (10.699999809265137B parameters) requires approximately 11.4 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 A30 24GB, SOLAR 10.7B v1.0 achieves approximately 111.5 tokens per second decode speed with a time-to-first-token of 1736ms using Q4_K_M quantization.
For coding workloads, SOLAR 10.7B v1.0 on NVIDIA A30 24GB receives a C grade with 111.5 tok/s and 177K context.
On NVIDIA A30 24GB, SOLAR 10.7B v1.0 can safely use up to 177K 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-a30-24gb" 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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