MacBook Pro M4 32GB经济之选
32 GB Unified (+8)
B
Adds memory headroom for longer context windows and future model growth.18.6 tok/s 解码
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
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 |
Inference speed
LLaVA 1.5 7B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for LLaVA 1.5 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 98.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 98.0 | Tight |
| RTX 3090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 87.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 87.8 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 56.2 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 51.5 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 45.3 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 43.0 | Heavy offload |
| RTX 3060 12GB | 12 GB | Q4_K_M | 24.7 | Heavy offload |
| RTX 4060 8GB | 8 GB | Q4_K_M | 10.6 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
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 | 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升级选项
能流畅运行 LLaVA 1.5 7B 的硬件
Mac mini M4 32GB最佳性价比
32 GB Unified (+8)
B
Adds memory headroom for longer context windows and future model growth.18.6 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
MacBook Pro M2 Max 32GBApple 升级
32 GB Unified (+8)400 GB/s (+127)
A
Adds memory headroom for longer context windows and future model growth.54.3 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Frequently asked questions
Can MacBook Pro M4 Pro 24GB run LLaVA 1.5 7B?
Yes, MacBook Pro M4 Pro 24GB can run LLaVA 1.5 7B with a B grade (Tight fit). Expected decode speed: 45.3 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 MacBook Pro M4 Pro 24GB?
On MacBook Pro M4 Pro 24GB, LLaVA 1.5 7B achieves approximately 45.3 tokens per second decode speed with a time-to-first-token of 4275ms using Q4_K_M quantization.
Can MacBook Pro M4 Pro 24GB run LLaVA 1.5 7B for coding?
For coding workloads, LLaVA 1.5 7B on MacBook Pro M4 Pro 24GB receives a B grade with 45.3 tok/s and 4K context.
What context window can LLaVA 1.5 7B use on MacBook Pro M4 Pro 24GB?
On MacBook Pro M4 Pro 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 MacBook Pro M4 Pro 24GB as fast as VRAM for LLaVA 1.5 7B?
Not always. MacBook Pro M4 Pro 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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