Radeon AI PRO R9700 32GBコスパ最良
640 GB/s (+490)
A
Raises estimated decode speed by about 245%.88.4 tok/sデコード
Raises estimated decode speed by about 245%.
Adds memory headroom for longer context windows and future model growth.
〜$1,899 MSRP
Can LLaVA 1.5 7B run on MacBook Pro M3 Pro 36GB?
YES — Runs Great
B70Good
Estimated from fit model
LLaVA 1.5 7B needs ~16.9 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~26 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 16.9 GB, 25.6 tok/s, Runs well
16.9 GB required25.9 GB available
65% VRAM used
Fit status
Runs well
Decode
25.6 tok/s
TTFT
7550 ms
Safe context
4K
Memory
16.9 GB / 25.9 GB
Memory breakdown
Weights4.3 GB
KV Cache7.8 GB
Runtime0.9 GB
Headroom3.9 GB
See how fast it feels
See how fast it feelsLLaVA 1.5 7B on MacBook Pro M3 Pro 36GB
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: 25.6 tok/s decode · 7.5s TTFT (warm) · 64 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 | 25.6 tok/s | 4118 ms | 4K |
| Coding | B | Runs well | 25.6 tok/s | 7550 ms | 4K |
| Agentic Coding | B | Runs with offload | 25.6 tok/s | 10981 ms | 4K |
| Reasoning | B | Runs well | 25.6 tok/s | 8922 ms | 4K |
| RAG | B | Runs with offload | 25.6 tok/s | 13726 ms | 4K |
Quantization options
How LLaVA 1.5 7B (7B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B62 |
Q3_K_S | 3 | 3.4 GB | Low | B62 |
NVFP4 | 4 | 3.9 GB | Medium | B62 |
Q4_K_M | 4 | 4.3 GB | Medium | B63 |
Q5_K_M | 5 | 5.0 GB | High | B63 |
Q6_K | 6 | 5.7 GB | High | B63 |
Q8_0 | 8 | 7.5 GB | Very High | B64 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | B68 |
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
Frequently asked questions
Can MacBook Pro M3 Pro 36GB run LLaVA 1.5 7B?
Yes, MacBook Pro M3 Pro 36GB can run LLaVA 1.5 7B with a B grade (Runs well). Expected decode speed: 25.6 tok/s.
How much VRAM does LLaVA 1.5 7B need?
LLaVA 1.5 7B (7B parameters) requires approximately 16.9 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 M3 Pro 36GB?
On MacBook Pro M3 Pro 36GB, LLaVA 1.5 7B achieves approximately 25.6 tokens per second decode speed with a time-to-first-token of 7550ms using Q4_K_M quantization.
Can MacBook Pro M3 Pro 36GB run LLaVA 1.5 7B for coding?
For coding workloads, LLaVA 1.5 7B on MacBook Pro M3 Pro 36GB receives a B grade with 25.6 tok/s and 4K context.
What context window can LLaVA 1.5 7B use on MacBook Pro M3 Pro 36GB?
On MacBook Pro M3 Pro 36GB, 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 M3 Pro 36GB as fast as VRAM for LLaVA 1.5 7B?
Not always. MacBook Pro M3 Pro 36GB 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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<iframe src="https://willitrunai.com/embed/llava-1.5-7b-on-m3-pro-36gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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