RX 9070 16GBBudget pick
640 GB/s (+490)
C
Raises estimated decode speed by about 255%.98 tok/s decode
Raises estimated decode speed by about 255%.
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP
Starling LM 7B needs ~9.1 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q4_K_M quantization, expect ~28 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
Runs well
Decode
27.6 tok/s
TTFT
7023 ms
Safe context
8K
Memory
9.1 GB / 13.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 27.6 tok/s | 3831 ms | 8K |
| Coding | C | Runs well | 27.6 tok/s | 7023 ms | 8K |
| Agentic Coding | C | Tight fit | 27.6 tok/s | 10215 ms | 8K |
| Reasoning | C | Runs well | 27.6 tok/s | 8300 ms | 8K |
| RAG | C | Tight fit | 27.6 tok/s | 12769 ms | 8K |
How Starling LM 7B (7B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C49 |
Q3_K_S | 3 | 3.4 GB | Low | C49 |
NVFP4 | 4 |
Copy-paste commands to run Starling LM 7B on your machine.
Run
ollama run starling-lmUpgrade options
Raises estimated decode speed by about 255%.
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP
Raises estimated decode speed by about 253%.
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
Yes, MacBook Pro M3 Pro 18GB can run Starling LM 7B with a C grade (Runs well). Expected decode speed: 27.6 tok/s.
Starling LM 7B (7B parameters) requires approximately 9.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Starling LM 7B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 18GB, Starling LM 7B achieves approximately 27.6 tokens per second decode speed with a time-to-first-token of 7023ms using Q4_K_M quantization.
For coding workloads, Starling LM 7B on MacBook Pro M3 Pro 18GB receives a C grade with 27.6 tok/s and 8K context.
On MacBook Pro M3 Pro 18GB, Starling LM 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, 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/starling-7b-on-m3-pro-18gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| C50 |
Q4_K_M | 4 | 4.3 GB | Medium | C50 |
Q5_K_M | 5 | 5.0 GB | High | C51 |
Q6_K | 6 | 5.7 GB | High | C52 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | C52 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Not always. MacBook Pro M3 Pro 18GB 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.