stablelm 2 zephyr 1 6b needs ~15.6 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~63 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
63.4 tok/s
TTFT
3054 ms
Safe context
1.2M
Memory
15.6 GB / 69.1 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 | 63.4 tok/s | 1666 ms | 1.2M |
| Coding | C | Runs well | 63.4 tok/s | 3054 ms | 1.2M |
| Agentic Coding | C | Runs well | 63.4 tok/s | 4442 ms | 1.2M |
| Reasoning | C | Runs well | 63.4 tok/s | 3610 ms | 1.2M |
| RAG | C | Runs well | 63.4 tok/s | 5553 ms | 1.2M |
How stablelm 2 zephyr 1 6b (6B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C40 |
Q3_K_S | 3 | 2.9 GB | Low | C40 |
NVFP4 | 4 | 3.4 GB | Medium | C40 |
Q4_K_M | 4 | 3.7 GB | Medium | C40 |
Q5_K_M | 5 | 4.3 GB | High | C40 |
Q6_K | 6 | 4.9 GB | High | C40 |
Q8_0 | 8 | 6.4 GB | Very High | C40 |
F16Best for your GPU | 16 | 12.3 GB | Maximum | C41 |
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 startアップグレードオプション
Raises estimated decode speed by about 32%.
Adds memory headroom for longer context windows and future model growth.
〜$3,999 MSRP
Raises estimated decode speed by about 32%.
Adds memory headroom for longer context windows and future model growth.
〜$3,999 MSRP
Raises estimated decode speed by about 32%.
Adds memory headroom for longer context windows and future model growth.
〜$4,999 MSRP
Yes, MacBook Pro M2 Max 96GB can run stablelm 2 zephyr 1 6b with a C grade (Runs well). Expected decode speed: 63.4 tok/s.
stablelm 2 zephyr 1 6b (6B parameters) requires approximately 15.6 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 Max 96GB, stablelm 2 zephyr 1 6b achieves approximately 63.4 tokens per second decode speed with a time-to-first-token of 3054ms using Q4_K_M quantization.
For coding workloads, stablelm 2 zephyr 1 6b on MacBook Pro M2 Max 96GB receives a C grade with 63.4 tok/s and 1.2M context.
On MacBook Pro M2 Max 96GB, stablelm 2 zephyr 1 6b can safely use up to 1.2M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. MacBook Pro M2 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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<iframe src="https://willitrunai.com/embed/hf-stabilityai--stablelm-2-zephyr-1-6b-on-m2-max-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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