Solar Open 100B i1 needs ~56.7 GB VRAM. RTX PRO 5000 Blackwell 48GB has 48.0 GB. With Q2_K quantization, expect ~13 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
30.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.2 tok/s
TTFT
37526 ms
Safe context
4K
Memory
78.7 GB / 48.0 GB
Offload
40%
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 6.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 6.0 tok/s | 17481 ms | 4K |
| Coding | F | Too heavy | 5.2 tok/s | 37526 ms | 4K |
| Agentic Coding | F | Too heavy | 3.9 tok/s | 72445 ms | 4K |
| Reasoning | F | Too heavy | 5.2 tok/s | 44349 ms | 4K |
| RAG | F | Too heavy | 3.9 tok/s | 90556 ms | 4K |
How Solar Open 100B i1 (100B params) fits at each quantization level on RTX PRO 5000 Blackwell 48GB (48.0 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 |
Copy-paste commands to run Solar Open 100B i1 on your machine.
Run
lms load hf-mradermacher--solar-open-100b-i1-gguf && lms server startUpgrade 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
Yes, RTX PRO 5000 Blackwell 48GB can run Solar Open 100B i1 at Q2_K quantization (Very compromised (needs ~6 GB host RAM)). The recommended Q4_K_M requires 78.7 GB which exceeds available memory, but at Q2_K it needs only 56.7 GB. Expected decode speed: 13.4 tok/s.
Solar Open 100B i1 (100B parameters) requires approximately 78.7 GB at Q4_K_M quantization. On RTX PRO 5000 Blackwell 48GB, it fits at Q2_K using 56.7 GB.
The recommended quantization is Q4_K_M, but on RTX PRO 5000 Blackwell 48GB the best fitting quantization is Q2_K, which uses 56.7 GB.
On RTX PRO 5000 Blackwell 48GB, Solar Open 100B i1 achieves approximately 13.4 tokens per second decode speed with a time-to-first-token of 14463ms using Q2_K quantization.
For coding workloads, Solar Open 100B i1 on RTX PRO 5000 Blackwell 48GB receives a F grade with 5.2 tok/s and 4K context.
On RTX PRO 5000 Blackwell 48GB, Solar Open 100B i1 can safely use up to 4K tokens of context at Q2_K quantization. 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-open-100b-i1-gguf-on-rtx-pro-5000-blackwell-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
56.0 GB |
| 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 |
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.