solar finalised finetuned Model 10.7B i1 needs ~9.5 GB VRAM. RTX 2000 Ada Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~15 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
1.5 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~1 GB host RAM)
Decode
15.8 tok/s
TTFT
12276 ms
Safe context
4K
Memory
9.5 GB / 8.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Very compromised | 17.3 tok/s | 6088 ms | 4K |
| Coding | D | Very compromised | 15.0 tok/s | 12890 ms | 4K |
| Agentic Coding | F | Too heavy | 11.6 tok/s | 24352 ms | 4K |
| Reasoning | D | Very compromised | 15.0 tok/s | 15234 ms | 4K |
| RAG | F | Too heavy | 11.6 tok/s | 30440 ms | 4K |
How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on RTX 2000 Ada Laptop 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | C53 |
Q3_K_SBest for your GPU | 3 | 5.2 GB | Low | C52 |
Copy-paste commands to run solar finalised finetuned Model 10.7B i1 on your machine.
Run
lms load hf-mradermacher--solar-finalised-finetuned-model-10-7b-i1-gguf && lms server startUpgrade options
RTX 3060 12GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 126%.
~$329 MSRP
RTX 5060 Ti 16GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 161%.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 114%.
~$499 MSRP
Yes, RTX 2000 Ada Laptop 8GB can run solar finalised finetuned Model 10.7B i1 with a D grade (Very compromised). Expected decode speed: 15.0 tok/s.
solar finalised finetuned Model 10.7B i1 (10.699999809265137B parameters) requires approximately 9.5 GB of memory with Q4_K_M quantization.
The recommended quantization for solar finalised finetuned Model 10.7B i1 is Q4_K_M, which balances quality and memory efficiency.
On RTX 2000 Ada Laptop 8GB, solar finalised finetuned Model 10.7B i1 achieves approximately 15.0 tokens per second decode speed with a time-to-first-token of 12890ms using Q4_K_M quantization.
For coding workloads, solar finalised finetuned Model 10.7B i1 on RTX 2000 Ada Laptop 8GB receives a D grade with 15.0 tok/s and 4K context.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--solar-finalised-finetuned-model-10-7b-i1-gguf-on-rtx-2000-ada-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:
| 4 |
6.0 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 6.5 GB | Medium | F0 |
Q5_K_M | 5 | 7.7 GB | High | F0 |
Q6_K | 6 | 8.8 GB | High | F0 |
Q8_0 | 8 | 11.4 GB | Very High | F0 |
F16 | 16 | 21.9 GB | Maximum | F0 |
On RTX 2000 Ada Laptop 8GB, solar finalised finetuned Model 10.7B i1 can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.