Solar Open 69B REAP i1 needs ~54.3 GB but Radeon AI PRO R9700 32GB only has 32.0 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
22.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.3 tok/s
TTFT
82918 ms
Safe context
4K
Memory
54.3 GB / 32.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 54.3 GB, but this setup only exposes 32.0 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 | 2.7 tok/s | 38621 ms | 4K |
| Coding | F | Too heavy | 2.3 tok/s | 82918 ms | 4K |
| Agentic Coding | F | Too heavy | 2.0 tok/s | 140800 ms | 4K |
| Reasoning | F | Too heavy | 2.3 tok/s | 97994 ms | 4K |
| RAG | F | Too heavy | 2.0 tok/s | 176000 ms | 4K |
How Solar Open 69B REAP i1 (69B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 26.9 GB | Low | F0 |
Q3_K_S | 3 | 33.8 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 187%.
~$3,999 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.
~$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.
~$10,000 MSRP
NVIDIA H100 80GBBiggest leapMakes 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.
~$40,000 MSRP
No, Solar Open 69B REAP i1 requires more memory than Radeon AI PRO R9700 32GB provides.
Solar Open 69B REAP i1 (69B parameters) requires approximately 54.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Solar Open 69B REAP i1 is Q4_K_M, which balances quality and memory efficiency.
On Radeon AI PRO R9700 32GB, Solar Open 69B REAP i1 achieves approximately 2.3 tokens per second decode speed with a time-to-first-token of 82918ms using Q4_K_M quantization.
For coding workloads, Solar Open 69B REAP i1 on Radeon AI PRO R9700 32GB receives a F grade with 2.3 tok/s and 4K context.
On Radeon AI PRO R9700 32GB, Solar Open 69B REAP i1 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-mradermacher--solar-open-69b-reap-i1-gguf-on-radeon-ai-pro-r9700-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
38.6 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 42.1 GB | Medium | F0 |
Q5_K_M | 5 | 49.7 GB | High | F0 |
Q6_K | 6 | 56.6 GB | High | F0 |
Q8_0 | 8 | 73.8 GB | Very High | F0 |
F16 | 16 | 141.5 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.
