RTX 5070 Ti 16GBBudget pick16 GB VRAM (+4)
B
Adds memory headroom for longer context windows and future model growth.87.9 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$749 MSRP
solar finalised finetuned Model 10.7B i1 needs ~10.2 GB VRAM. RTX 3080 Ti 12GB has 12.0 GB. With Q4_K_M quantization, expect ~103 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
Tight fit
Decode
103.4 tok/s
TTFT
1872 ms
Safe context
39K
Memory
10.2 GB / 12.0 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 | B | Runs well | 103.4 tok/s | 1021 ms | 39K |
| Coding | C | Tight fit | 103.4 tok/s | 1872 ms | 39K |
| Agentic Coding | C | Runs with offload | 103.4 tok/s | 2723 ms | 39K |
| Reasoning | C | Tight fit | 103.4 tok/s | 2213 ms | 39K |
| RAG | C | Runs with offload | 103.4 tok/s | 3404 ms | 39K |
Inference speed
Estimated decode speed (tokens/sec) for solar finalised finetuned Model 10.7B i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~150 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
| RTX 5090 32GB | 32 GB | Q4_K_M | 149.8 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 117.4 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 105.9 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 100.4 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 93.6 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 85.3 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 71.1 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 67.4 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 60.8 | Tight |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 46.4 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 46.4 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 36.8 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 36.4 | Tight |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 33.7 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 28.3 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 16.8 | Heavy offload |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on RTX 3080 Ti 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | C51 |
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 |
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 5070 Ti 16GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$749 MSRP
RTX 4070 Ti Super 16GBBest valueAdds memory headroom for longer context windows and future model growth.
~$799 MSRP
RTX 4080 Super 16GBNVIDIA upgradeAdds memory headroom for longer context windows and future model growth.
~$999 MSRP
Yes, RTX 3080 Ti 12GB can run solar finalised finetuned Model 10.7B i1 with a C grade (Tight fit). Expected decode speed: 103.4 tok/s.
solar finalised finetuned Model 10.7B i1 (10.699999809265137B parameters) requires approximately 10.2 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 3080 Ti 12GB, solar finalised finetuned Model 10.7B i1 achieves approximately 103.4 tokens per second decode speed with a time-to-first-token of 1872ms using Q4_K_M quantization.
For coding workloads, solar finalised finetuned Model 10.7B i1 on RTX 3080 Ti 12GB receives a C grade with 103.4 tok/s and 39K context.
On RTX 3080 Ti 12GB, solar finalised finetuned Model 10.7B i1 can safely use up to 39K 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-finalised-finetuned-model-10-7b-i1-gguf-on-rtx-3080-ti-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: