MacBook Pro M4 32GBコスパ重視
32 GB Unified (+8)120 GB/s (+20)
B
Adds memory headroom for longer context windows and future model growth.18.6 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$799 MSRP
Can LLaVA 1.5 7B run on Mac mini M2 24GB?
YES — Tight Fit
B66Good
Estimated from fit model
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.
Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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, 15.2 tok/s, Tight fit
15.6 GB required17.3 GB available
90% VRAM used
Fit status
Tight fit
Decode
15.2 tok/s
TTFT
12718 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 Mac mini M2 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: 15.2 tok/s decode · 12.7s TTFT (warm) · 38 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 | 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 |
Quantization options
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 | 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 |
Get started
Copy-paste commands to run LLaVA 1.5 7B on your machine.
Run
ollama run llavaアップグレードオプション
LLaVA 1.5 7Bを快適に動かすハードウェア
RX 7900 XT 20GB最大の飛躍
800 GB/s (+700)
A
Raises estimated decode speed by about 545%.98 tok/sデコード
Raises estimated decode speed by about 545%.
〜$899 MSRP
Mac mini M4 32GBコスパ最良
32 GB Unified (+8)120 GB/s (+20)
B
Adds memory headroom for longer context windows and future model growth.18.6 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$1,099 MSRP
Mac Studio M2 Ultra 64GBAppleアップグレード
64 GB Unified (+40)800 GB/s (+700)
B
Raises estimated decode speed by about 545%.98 tok/sデコード
Raises estimated decode speed by about 545%.
Adds memory headroom for longer context windows and future model growth.
〜$3,999 MSRP
Frequently asked questions
Can Mac mini M2 24GB run LLaVA 1.5 7B?
Yes, Mac mini M2 24GB can run LLaVA 1.5 7B with a B grade (Tight fit). Expected decode speed: 15.2 tok/s.
How much VRAM does LLaVA 1.5 7B need?
LLaVA 1.5 7B (7B parameters) requires approximately 15.6 GB of memory with Q4_K_M quantization.
What is the best quantization for LLaVA 1.5 7B?
The recommended quantization for LLaVA 1.5 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will LLaVA 1.5 7B run at on Mac mini M2 24GB?
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.
Can Mac mini M2 24GB run LLaVA 1.5 7B for coding?
For coding workloads, LLaVA 1.5 7B on Mac mini M2 24GB receives a B grade with 15.2 tok/s and 4K context.
What context window can LLaVA 1.5 7B use on Mac mini M2 24GB?
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.
Is unified memory on Mac mini M2 24GB as fast as VRAM for LLaVA 1.5 7B?
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.
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