MacBook Pro M3 Max 128GBBudget pick
128 GB Unified (+32)
C
Adds memory headroom for longer context windows and future model growth.5.7 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Solar Open 69B REAP i1 needs ~61.4 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M 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
Fit status
Tight fit
Decode
5.5 tok/s
TTFT
35123 ms
Safe context
31K
Memory
61.4 GB / 69.1 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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 5.5 tok/s | 19158 ms | 31K |
| Coding | C | Tight fit | 5.5 tok/s | 35123 ms | 31K |
| Agentic Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 5.4 tok/s | 51943 ms | 31K |
| Reasoning | C | Tight fit | 5.5 tok/s | 41509 ms | 31K |
| RAG | C | Runs with offload (needs ~0.2 GB host RAM) | 5.4 tok/s | 64929 ms |
How Solar Open 69B REAP i1 (69B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 26.9 GB | Low | C45 |
Q3_K_S | 3 | 33.8 GB | Low | C47 |
NVFP4 | 4 |
Copy-paste commands to run Solar Open 69B REAP i1 on your machine.
Run
lms load hf-mradermacher--solar-open-69b-reap-i1-gguf && lms server startUpgrade options
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 100%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 91%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
NVIDIA H100 80GBBiggest leapRaises estimated decode speed by about 1116%.
Moves the workload away from shared memory into dedicated accelerator memory.
~$40,000 MSRP
Yes, MacBook Pro M2 Max 96GB can run Solar Open 69B REAP i1 with a C grade (Tight fit). Expected decode speed: 5.5 tok/s.
Solar Open 69B REAP i1 (69B parameters) requires approximately 61.4 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 MacBook Pro M2 Max 96GB, Solar Open 69B REAP i1 achieves approximately 5.5 tokens per second decode speed with a time-to-first-token of 35123ms using Q4_K_M quantization.
For coding workloads, Solar Open 69B REAP i1 on MacBook Pro M2 Max 96GB receives a C grade with 5.5 tok/s and 31K context.
On MacBook Pro M2 Max 96GB, Solar Open 69B REAP i1 can safely use up to 31K 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-m2-max-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 31K |
38.6 GB |
| Medium |
| C48 |
Q4_K_M | 4 | 42.1 GB | Medium | C48 |
Q5_K_M | 5 | 49.7 GB | High | C48 |
Q6_KBest for your GPU | 6 | 56.6 GB | High | C48 |
Q8_0 | 8 | 73.8 GB | Very High | F0 |
F16 | 16 | 141.5 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. MacBook Pro M2 Max 96GB 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.