Solar Open 100B i1 needs ~56.4 GB VRAM. Radeon Pro W7900 48GB has 48.0 GB. With Q2_K quantization, expect ~6 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
30.4 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.2 tok/s
TTFT
86806 ms
Safe context
4K
Memory
78.4 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 10% 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 5.8 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 | 2.6 tok/s | 40208 ms | 4K |
| Coding | F | Too heavy | 2.2 tok/s | 86806 ms | 4K |
| Agentic Coding | F | Too heavy | 2.0 tok/s | 140800 ms | 4K |
| Reasoning | F | Too heavy | 2.2 tok/s | 102589 ms | 4K |
| RAG | F | Too heavy | 2.0 tok/s | 176000 ms | 4K |
How Solar Open 100B i1 (100B params) fits at each quantization level on Radeon Pro W7900 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 | 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 |
Copy-paste commands to run Solar Open 100B i1 on your machine.
Run
lms load hf-mradermacher--solar-open-100b-i1-gguf && lms server startアップグレードオプション
Makes 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.
〜$8,000 MSRP
Makes 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
Makes 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.
〜$15,000 MSRP
Yes, Radeon Pro W7900 48GB can run Solar Open 100B i1 at Q2_K quantization (Very compromised (needs ~5.8 GB host RAM)). The recommended Q4_K_M requires 78.4 GB which exceeds available memory, but at Q2_K it needs only 56.4 GB. Expected decode speed: 5.9 tok/s.
Solar Open 100B i1 (100B parameters) requires approximately 78.4 GB at Q4_K_M quantization. On Radeon Pro W7900 48GB, it fits at Q2_K using 56.4 GB.
The recommended quantization is Q4_K_M, but on Radeon Pro W7900 48GB the best fitting quantization is Q2_K, which uses 56.4 GB.
On Radeon Pro W7900 48GB, Solar Open 100B i1 achieves approximately 5.9 tokens per second decode speed with a time-to-first-token of 32648ms using Q2_K quantization.
For coding workloads, Solar Open 100B i1 on Radeon Pro W7900 48GB receives a F grade with 2.2 tok/s and 4K context.
On Radeon Pro W7900 48GB, Solar Open 100B i1 can safely use up to 5K tokens of context at Q2_K quantization. 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.
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