stablelm 2 zephyr 1.6b needs ~5.5 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~22 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
22.4 tok/s
TTFT
8643 ms
Safe context
1.5M
Memory
5.5 GB / 23.0 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 | 22.4 tok/s | 4714 ms | 1.4M |
| Coding | C | Runs well | 22.4 tok/s | 8643 ms | 1.5M |
| Agentic Coding | C | Runs well | 22.4 tok/s | 12571 ms | 1.5M |
| Reasoning | C | Runs well | 22.4 tok/s | 10214 ms | 1.5M |
| RAG | C | Runs well | 22.4 tok/s | 15714 ms | 1.5M |
How stablelm 2 zephyr 1.6b (1.600000023841858B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.6 GB | Low | C43 |
Q3_K_S | 3 | 0.8 GB | Low | C43 |
NVFP4 | 4 |
Copy-paste commands to run stablelm 2 zephyr 1.6b on your machine.
Run
lms load hf-second-state--stablelm-2-zephyr-1-6b-gguf && lms server startYes, MacBook Pro M2 Pro 32GB can run stablelm 2 zephyr 1.6b with a C grade (Runs well). Expected decode speed: 22.4 tok/s.
stablelm 2 zephyr 1.6b (1.600000023841858B parameters) requires approximately 5.5 GB of memory with Q4_K_M quantization.
The recommended quantization for stablelm 2 zephyr 1.6b is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 32GB, stablelm 2 zephyr 1.6b achieves approximately 22.4 tokens per second decode speed with a time-to-first-token of 8643ms using Q4_K_M quantization.
For coding workloads, stablelm 2 zephyr 1.6b on MacBook Pro M2 Pro 32GB receives a C grade with 22.4 tok/s and 1.5M context.
On MacBook Pro M2 Pro 32GB, stablelm 2 zephyr 1.6b can safely use up to 1.5M 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-second-state--stablelm-2-zephyr-1-6b-gguf-on-m2-pro-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
0.9 GB |
| Medium |
| C43 |
Q4_K_M | 4 | 1.0 GB | Medium | C43 |
Q5_K_M | 5 | 1.2 GB | High | C44 |
Q6_K | 6 | 1.3 GB | High | C44 |
Q8_0 | 8 | 1.7 GB | Very High | C44 |
F16Best for your GPU | 16 | 3.3 GB | Maximum | C44 |
Not always. MacBook Pro M2 Pro 32GB 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.