Solar Open 100B needs ~87.4 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~4 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
3.9 tok/s
TTFT
49207 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 | 3.9 tok/s | 26840 ms | 22K |
| Coding | C | Tight fit | 3.9 tok/s | 49207 ms | 22K |
| Agentic Coding | D | Runs with offload (needs ~4.3 GB host RAM) | 3.5 tok/s | 81331 ms | 22K |
| Reasoning | C | Tight fit | 3.9 tok/s | 58153 ms | 22K |
| RAG | D | Runs with offload (needs ~4.3 GB host RAM) | 3.5 tok/s | 101663 ms | 22K |
Inference speed
Estimated decode speed (tokens/sec) for Solar Open 100B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~10 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 9.8 | Tight |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 9.1 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 7.6 | Tight |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 7.2 | Tight |
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 5.1 | Too big |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 4.9 | Too big |
 RTX 5090 32GB | 32 GB | Q4_K_M | 3.0 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 2.8 | Too big |
| 2× RTX 4090 24GB | 48 GB | Q4_K_M | 2.7 | Too big |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 2.5 | Too big |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 2.2 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 2.0 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 2.0 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Solar Open 100B (100B params) fits at each quantization level on MacBook Pro M3 Max 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 | 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 |
Copy-paste commands to run Solar Open 100B on your machine.
Run
lms load hf-aaryank--solar-open-100b-gguf && lms server startOpciones de mejora
Sube la velocidad estimada de decodificación alrededor de un 133%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$6,999 MSRP
Sube la velocidad estimada de decodificación alrededor de un 2354%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$8,000 MSRP
Sube la velocidad estimada de decodificación alrededor de un 1459%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$12,000 MSRP
Yes, MacBook Pro M3 Max 128GB can run Solar Open 100B with a C grade (Tight fit). Expected decode speed: 3.9 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 MacBook Pro M3 Max 128GB, Solar Open 100B achieves approximately 3.9 tokens per second decode speed with a time-to-first-token of 49207ms using Q4_K_M quantization.
For coding workloads, Solar Open 100B on MacBook Pro M3 Max 128GB receives a C grade with 3.9 tok/s and 22K context.
On MacBook Pro M3 Max 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.
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 M3 Max 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.
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