Intel Arc B580 12GBBudget pick456 GB/s (+336)
C
Raises estimated decode speed by about 176%.51.3 tok/s decode
Raises estimated decode speed by about 176%.
~$249 MSRP
zephyr 7B alpha needs ~7.7 GB VRAM. MacBook Pro M4 16GB has 11.5 GB. With Q4_K_M quantization, expect ~19 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
18.6 tok/s
TTFT
10400 ms
Safe context
90K
Memory
7.7 GB / 11.5 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 | 18.6 tok/s | 5673 ms | 90K |
| Coding | C | Runs well | 18.6 tok/s | 10400 ms | 90K |
| Agentic Coding | C | Runs well | 18.6 tok/s | 15127 ms | 90K |
| Reasoning | C | Runs well | 18.6 tok/s | 12291 ms | 90K |
| RAG | C | Runs well | 18.6 tok/s | 18909 ms | 90K |
How zephyr 7B alpha (7B params) fits at each quantization level on MacBook Pro M4 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C49 |
Q3_K_S | 3 | 3.4 GB | Low | C50 |
NVFP4 | 4 |
Copy-paste commands to run zephyr 7B alpha on your machine.
Run
lms load hf-thebloke--zephyr-7b-alpha-gguf && lms server startUpgrade options
Intel Arc B580 12GBBudget pickRaises estimated decode speed by about 176%.
~$249 MSRP
RTX 3060 12GBBest valueRaises estimated decode speed by about 151%.
~$329 MSRP
Yes, MacBook Pro M4 16GB can run zephyr 7B alpha with a C grade (Runs well). Expected decode speed: 18.6 tok/s.
zephyr 7B alpha (7B parameters) requires approximately 7.7 GB of memory with Q4_K_M quantization.
The recommended quantization for zephyr 7B alpha is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 16GB, zephyr 7B alpha achieves approximately 18.6 tokens per second decode speed with a time-to-first-token of 10400ms using Q4_K_M quantization.
For coding workloads, zephyr 7B alpha on MacBook Pro M4 16GB receives a C grade with 18.6 tok/s and 90K context.
On MacBook Pro M4 16GB, zephyr 7B alpha can safely use up to 90K 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-thebloke--zephyr-7b-alpha-gguf-on-m4-16gb" 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 |
| C51 |
Q4_K_M | 4 | 4.3 GB | Medium | C51 |
Q5_K_M | 5 | 5.0 GB | High | C52 |
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 M4 16GB 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.