Solar Open 100B needs ~74.7 GB but RTX 4060 Laptop 8GB only has 8.0 GB. Try a smaller quantization or lighter model.
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
66.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.0 tok/s
TTFT
96800 ms
Safe context
4K
Memory
74.7 GB / 8.0 GB
Offload
90%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 74.7 GB, but this setup only exposes 8.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.0 tok/s | 52800 ms | 4K |
| Coding | F | Too heavy | 2.0 tok/s | 96800 ms | 4K |
| Agentic Coding | F | Too heavy | 2.0 tok/s | 140800 ms | 4K |
| Reasoning | F | Too heavy | 2.0 tok/s | 114400 ms | 4K |
| RAG | F | Too heavy | 2.0 tok/s | 176000 ms | 4K |
How Solar Open 100B (100B params) fits at each quantization level on RTX 4060 Laptop 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 39.0 GB | Low | F0 |
Q3_K_S | 3 | 49.0 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$9,999 MSRP
NVIDIA H20 96GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$12,000 MSRP
No, Solar Open 100B requires more memory than RTX 4060 Laptop 8GB provides.
Solar Open 100B (100B parameters) requires approximately 74.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Solar Open 100B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4060 Laptop 8GB, Solar Open 100B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.
For coding workloads, Solar Open 100B on RTX 4060 Laptop 8GB receives a F grade with 2.0 tok/s and 4K context.
On RTX 4060 Laptop 8GB, Solar Open 100B can safely use up to 4K 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-aaryank--solar-open-100b-gguf-on-rtx-4060-laptop-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| F0 |
Q4_K_M | 4 | 61.0 GB | Medium | F0 |
Q5_K_M | 5 | 72.0 GB | High | F0 |
Q6_K | 6 | 82.0 GB | High | F0 |
Q8_0 | 8 | 107.0 GB | Very High | F0 |
F16 | 16 | 205.0 GB | Maximum | F0 |
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
