MacBook Pro M4 32GBBudget pick
32 GB Unified (+8)120 GB/s (+20)
B
Adds memory headroom for longer context windows and future model growth.18.6 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
LLaVA 1.5 7B needs ~15.6 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~15 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
Tight fit
Decode
15.2 tok/s
TTFT
12718 ms
Safe context
4K
Memory
15.6 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 | B | Runs well | 15.2 tok/s | 6937 ms | 4K |
| Coding | B | Tight fit | 15.2 tok/s | 12718 ms | 4K |
| Agentic Coding | F | Too heavy | 10.0 tok/s | 28096 ms | 4K |
| Reasoning | B | Tight fit | 15.2 tok/s | 15030 ms | 4K |
| RAG | F | Too heavy | 10.0 tok/s | 35120 ms | 4K |
How LLaVA 1.5 7B (7B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B64 |
Q3_K_S | 3 | 3.4 GB | Low | B65 |
NVFP4 | 4 |
Copy-paste commands to run LLaVA 1.5 7B on your machine.
Run
ollama run llavaUpgrade options
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
Raises estimated decode speed by about 545%.
~$899 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Raises estimated decode speed by about 545%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, Mac mini M2 24GB can run LLaVA 1.5 7B with a B grade (Tight fit). Expected decode speed: 15.2 tok/s.
LLaVA 1.5 7B (7B parameters) requires approximately 15.6 GB of memory with Q4_K_M quantization.
The recommended quantization for LLaVA 1.5 7B is Q4_K_M, which balances quality and memory efficiency.
On Mac mini M2 24GB, LLaVA 1.5 7B achieves approximately 15.2 tokens per second decode speed with a time-to-first-token of 12718ms using Q4_K_M quantization.
For coding workloads, LLaVA 1.5 7B on Mac mini M2 24GB receives a B grade with 15.2 tok/s and 4K context.
On Mac mini M2 24GB, LLaVA 1.5 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, 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/llava-1.5-7b-on-m2-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| B65 |
Q4_K_M | 4 | 4.3 GB | Medium | B65 |
Q5_K_M | 5 | 5.0 GB | High | B66 |
Q6_K | 6 | 5.7 GB | High | B67 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | B68 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Not always. Mac mini M2 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.