MacBook Pro M3 Max 128GBGünstige Wahl
128 GB Unified (+32)
C
Adds memory headroom for longer context windows and future model growth.5.7 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $2,499 MSRP
Can Solar Open 69B REAP i1 run on MacBook Pro M4 Max 96GB?
YES — Tight Fit
C46Usable
Estimated from fit model
Solar Open 69B REAP i1 needs ~61.4 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~8 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 61.4 GB, 14.3 tok/s, Tight fit
61.4 GB required69.1 GB available
89% VRAM used
Fit status
Tight fit
Decode
14.3 tok/s
TTFT
13568 ms
Safe context
31K
Memory
61.4 GB / 69.1 GB
Memory breakdown
Weights42.1 GB
KV Cache8.1 GB
Runtime0.9 GB
Headroom10.4 GB
See how fast it feels
See how fast it feelsSolar Open 69B REAP i1 on MacBook Pro M4 Max 96GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 14.3 tok/s decode · 13.6s TTFT (warm) · 36 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 14.3 tok/s | 7401 ms | 31K |
| Coding | C | Tight fit | 8.2 tok/s | 23689 ms | 31K |
| Agentic Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 14.0 tok/s | 20065 ms | 31K |
| Reasoning | C | Tight fit | 14.3 tok/s | 16035 ms | 31K |
| RAG | C | Runs with offload (needs ~0.2 GB host RAM) | 14.0 tok/s | 25081 ms | 31K |
Quantization options
How Solar Open 69B REAP i1 (69B params) fits at each quantization level on MacBook Pro M4 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 | 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 |
Get started
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-Optionen
Hardware, die Solar Open 69B REAP i1 gut ausführt
Mac Studio M2 Ultra 128GBBestes Preis-Leistungs-Verhältnis
128 GB Unified (+32)800 GB/s (+254)
C
Adds memory headroom for longer context windows and future model growth.11 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $3,999 MSRP
Mac Studio M1 Ultra 128GBApple-Upgrade
128 GB Unified (+32)800 GB/s (+254)
C
Adds memory headroom for longer context windows and future model growth.10.5 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $3,999 MSRP
NVIDIA H100 80GBGrößter Sprung3350 GB/s (+2804)
B
Raises estimated decode speed by about 368%.66.9 tok/s Dekodierung
Raises estimated decode speed by about 368%.
ca. $40,000 MSRP
Frequently asked questions
Can MacBook Pro M4 Max 96GB run Solar Open 69B REAP i1?
Yes, MacBook Pro M4 Max 96GB can run Solar Open 69B REAP i1 with a C grade (Tight fit). Expected decode speed: 8.2 tok/s.
How much VRAM does Solar Open 69B REAP i1 need?
Solar Open 69B REAP i1 (69B parameters) requires approximately 61.4 GB of memory with Q4_K_M quantization.
What is the best quantization for Solar Open 69B REAP i1?
The recommended quantization for Solar Open 69B REAP i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will Solar Open 69B REAP i1 run at on MacBook Pro M4 Max 96GB?
On MacBook Pro M4 Max 96GB, Solar Open 69B REAP i1 achieves approximately 8.2 tokens per second decode speed with a time-to-first-token of 23689ms using Q4_K_M quantization.
Can MacBook Pro M4 Max 96GB run Solar Open 69B REAP i1 for coding?
For coding workloads, Solar Open 69B REAP i1 on MacBook Pro M4 Max 96GB receives a C grade with 8.2 tok/s and 31K context.
What context window can Solar Open 69B REAP i1 use on MacBook Pro M4 Max 96GB?
On MacBook Pro M4 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.
Is unified memory on MacBook Pro M4 Max 96GB as fast as VRAM for Solar Open 69B REAP i1?
Not always. MacBook Pro M4 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.
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