Vicuna 13B needs ~27.9 GB VRAM. Mac Studio M1 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~56 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
55.5 tok/s
TTFT
3489 ms
Safe context
4K
Memory
27.9 GB / 46.1 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 | 55.5 tok/s | 1903 ms | 4K |
| Coding | A | Runs well | 55.5 tok/s | 3489 ms | 4K |
| Agentic Coding | A | Tight fit | 55.5 tok/s | 5075 ms | 4K |
| Reasoning | A | Runs well | 55.5 tok/s | 4124 ms | 4K |
| RAG | A | Tight fit | 55.5 tok/s | 6344 ms | 4K |
How Vicuna 13B (13B params) fits at each quantization level on Mac Studio M1 Ultra 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B63 |
Q3_K_S | 3 | 6.4 GB | Low | B63 |
NVFP4 | 4 |
Copy-paste commands to run Vicuna 13B on your machine.
Run
ollama run vicuna:13bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 66.5 tok/s | |
| Qwen 3.5 27B | 27B | S | 28.9 tok/s |
Yes, Mac Studio M1 Ultra 64GB can run Vicuna 13B with a A grade (Runs well). Expected decode speed: 55.5 tok/s.
Vicuna 13B (13B parameters) requires approximately 27.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Vicuna 13B is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M1 Ultra 64GB, Vicuna 13B achieves approximately 55.5 tokens per second decode speed with a time-to-first-token of 3489ms using Q4_K_M quantization.
For coding workloads, Vicuna 13B on Mac Studio M1 Ultra 64GB receives a A grade with 55.5 tok/s and 4K context.
On Mac Studio M1 Ultra 64GB, Vicuna 13B 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-13b-on-m1-ultra-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
7.3 GB |
| Medium |
| B63 |
Q4_K_M | 4 | 7.9 GB | Medium | B63 |
Q5_K_M | 5 | 9.4 GB | High | B64 |
Q6_K | 6 | 10.7 GB | High | B64 |
Q8_0 | 8 | 13.9 GB | Very High | B65 |
F16Best for your GPU | 16 | 26.7 GB | Maximum | B69 |
| Qwen 3.6 27B | 27B | S | 21.9 tok/s |
| Qwen 3.6 35B A3B | 35B | S | 55.9 tok/s |
| Qwen3-VL 30B A3B Instruct | 30B | S | 68.8 tok/s |
Not always. Mac Studio M1 Ultra 64GB 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.