MacBook Pro M4 32GBBudget pick
32 GB Unified (+8)
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
Can LLaVA 1.5 7B run on MacBook Pro M4 Pro 24GB?
YES — Tight Fit
B70Good
▲Estimated — low-sample bucket· few comparable runs
LLaVA 1.5 7B needs ~15.6 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~45 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: Balanced
Share:
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 15.6 GB, 45.3 tok/s, Tight fit
15.6 GB required17.3 GB available
90% VRAM used
Fit status
Tight fit
Decode
45.3 tok/s
TTFT
4275 ms
Safe context
4K
Memory
15.6 GB / 17.3 GB
Memory breakdown
Weights4.3 GB
KV Cache7.8 GB
Runtime0.9 GB
Headroom2.6 GB
See how fast it feels
See how fast it feelsLLaVA 1.5 7B on MacBook Pro M4 Pro 24GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 45.3 tok/s decode · 4.3s TTFT (warm) · 113 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 45.3 tok/s | 2332 ms | 4K |
| Coding | B | Tight fit | 45.3 tok/s | 4275 ms | 4K |
| Agentic Coding | F | Too heavy | 29.8 tok/s | 9443 ms | 4K |
| Reasoning | B | Tight fit | 45.3 tok/s | 5052 ms | 4K |
| RAG | F | Too heavy | 29.8 tok/s | 11804 ms | 4K |
Quantization options
How LLaVA 1.5 7B (7B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B64 |
Q3_K_S | 3 | 3.4 GB | Low | B65 |
NVFP4 | 4 |
Get started
Copy-paste commands to run LLaVA 1.5 7B on your machine.
Run
ollama run llavaUpgrade options
Hardware that runs LLaVA 1.5 7B well
Mac mini M4 32GBBest value
32 GB Unified (+8)
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.
~$1,099 MSRP
MacBook Pro M2 Max 32GBApple upgrade
32 GB Unified (+8)400 GB/s (+127)
A
Adds memory headroom for longer context windows and future model growth.54.3 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP