Solar Open 100B needs ~87.4 GB VRAM. Mac Studio M1 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~7 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
7.2 tok/s
TTFT
26840 ms
Safe context
22K
Memory
87.4 GB / 92.2 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 7.2 tok/s | 14640 ms | 22K |
| Coding | C | Tight fit | 7.2 tok/s | 26840 ms | 22K |
| Agentic Coding | D | Runs with offload (needs ~4.3 GB host RAM) | 6.3 tok/s | 44362 ms | 22K |
| Reasoning | C | Tight fit | 7.2 tok/s | 31720 ms | 22K |
| RAG | D | Runs with offload (needs ~4.3 GB host RAM) | 6.3 tok/s | 55453 ms |
How Solar Open 100B (100B params) fits at each quantization level on Mac Studio M1 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 39.0 GB | Low | C45 |
Q3_K_S | 3 | 49.0 GB | Low | C48 |
NVFP4 | 4 |
Copy-paste commands to run Solar Open 100B on your machine.
Run
lms load hf-aaryank--solar-open-100b-gguf && lms server startUpgrade options
Raises estimated decode speed by about 26%.
Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
Raises estimated decode speed by about 1229%.
Adds memory headroom for longer context windows and future model growth.
~$8,000 MSRP
Raises estimated decode speed by about 744%.
Adds memory headroom for longer context windows and future model growth.
~$12,000 MSRP
Yes, Mac Studio M1 Ultra 128GB can run Solar Open 100B with a C grade (Tight fit). Expected decode speed: 7.2 tok/s.
Solar Open 100B (100B parameters) requires approximately 87.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Solar Open 100B is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M1 Ultra 128GB, Solar Open 100B achieves approximately 7.2 tokens per second decode speed with a time-to-first-token of 26840ms using Q4_K_M quantization.
For coding workloads, Solar Open 100B on Mac Studio M1 Ultra 128GB receives a C grade with 7.2 tok/s and 22K context.
On Mac Studio M1 Ultra 128GB, Solar Open 100B can safely use up to 22K 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-aaryank--solar-open-100b-gguf-on-m1-ultra-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 22K |
56.0 GB |
| Medium |
| C48 |
Q4_K_M | 4 | 61.0 GB | Medium | C48 |
Q5_K_MBest for your GPU | 5 | 72.0 GB | High | C48 |
Q6_K | 6 | 82.0 GB | High | F0 |
Q8_0 | 8 | 107.0 GB | Very High | F0 |
F16 | 16 | 205.0 GB | Maximum | F0 |
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Not always. Mac Studio M1 Ultra 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.