stablelm 2 zephyr 1 6b needs ~7.9 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~53 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
52.8 tok/s
TTFT
3664 ms
Safe context
230K
Memory
7.9 GB / 17.3 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 | 52.8 tok/s | 1998 ms | 230K |
| Coding | C | Runs well | 52.8 tok/s | 3664 ms | 230K |
| Agentic Coding | C | Runs well | 52.8 tok/s | 5329 ms | 230K |
| Reasoning | C | Runs well | 52.8 tok/s | 4330 ms | 230K |
| RAG | C | Runs well | 52.8 tok/s | 6662 ms | 230K |
How stablelm 2 zephyr 1 6b (6B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C46 |
Q3_K_S | 3 | 2.9 GB | Low | C46 |
NVFP4 | 4 |
Copy-paste commands to run stablelm 2 zephyr 1 6b on your machine.
Run
lms load hf-stabilityai--stablelm-2-zephyr-1-6b && lms server startYes, MacBook Pro M4 Pro 24GB can run stablelm 2 zephyr 1 6b with a C grade (Runs well). Expected decode speed: 52.8 tok/s.
stablelm 2 zephyr 1 6b (6B parameters) requires approximately 7.9 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 M4 Pro 24GB, stablelm 2 zephyr 1 6b achieves approximately 52.8 tokens per second decode speed with a time-to-first-token of 3664ms using Q4_K_M quantization.
For coding workloads, stablelm 2 zephyr 1 6b on MacBook Pro M4 Pro 24GB receives a C grade with 52.8 tok/s and 230K context.
On MacBook Pro M4 Pro 24GB, stablelm 2 zephyr 1 6b can safely use up to 230K 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-stabilityai--stablelm-2-zephyr-1-6b-on-m4-pro-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.4 GB |
| Medium |
| C47 |
Q4_K_M | 4 | 3.7 GB | Medium | C47 |
Q5_K_M | 5 | 4.3 GB | High | C47 |
Q6_K | 6 | 4.9 GB | High | C48 |
Q8_0 | 8 | 6.4 GB | Very High | C49 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | C51 |
Not always. MacBook Pro M4 Pro 24GB 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.