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
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.
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
45.3 tok/s
TTFT
4275 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 | 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
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.
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 |
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
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
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.
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 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.
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.
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.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/llava-1.5-7b-on-m4-pro-24gb" 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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