Vicuna 7B needs ~18.2 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q4_K_M quantization, expect ~49 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
Runs well
Decode
45.3 tok/s
TTFT
4275 ms
Safe context
4K
Memory
18.2 GB / 34.6 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 | C | Runs well | 45.3 tok/s | 2332 ms | 4K |
| Coding | C | Runs well | 49.2 tok/s | 3933 ms | 4K |
| Agentic Coding | B | Runs well | 45.3 tok/s | 6218 ms | 4K |
| Reasoning | C | Runs well | 45.3 tok/s | 5052 ms | 4K |
| RAG | B | Runs well | 45.3 tok/s | 7772 ms | 4K |
How Vicuna 7B (7B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C43 |
Q3_K_S | 3 | 3.4 GB | Low | C43 |
NVFP4 | 4 |
Copy-paste commands to run Vicuna 7B on your machine.
Run
ollama run vicunaUpgrade options
Raises estimated decode speed by about 116%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 116%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 94%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M4 Pro 48GB can run Vicuna 7B with a C grade (Runs well). Expected decode speed: 49.2 tok/s.
Vicuna 7B (7B parameters) requires approximately 18.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Vicuna 7B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Pro 48GB, Vicuna 7B achieves approximately 49.2 tokens per second decode speed with a time-to-first-token of 3933ms using Q4_K_M quantization.
For coding workloads, Vicuna 7B on MacBook Pro M4 Pro 48GB receives a C grade with 49.2 tok/s and 4K context.
On MacBook Pro M4 Pro 48GB, Vicuna 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/vicuna-7b-on-m4-pro-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| C43 |
Q4_K_M | 4 | 4.3 GB | Medium | C44 |
Q5_K_M | 5 | 5.0 GB | High | C44 |
Q6_K | 6 | 5.7 GB | High | C44 |
Q8_0 | 8 | 7.5 GB | Very High | C45 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C48 |
Not always. MacBook Pro M4 Pro 48GB 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.