Llama 3.2 3B Instruct needs ~8.6 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q5_K_M quantization, expect ~42 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
42.0 tok/s
TTFT
4610 ms
Safe context
1.2M
Memory
8.6 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 | 42.0 tok/s | 2514 ms | 1.2M |
| Coding | C | Runs well | 42.0 tok/s | 4610 ms | 1.2M |
| Agentic Coding | C | Runs well | 42.0 tok/s | 6705 ms | 1.2M |
| Reasoning | C | Runs well | 42.0 tok/s | 5448 ms | 1.2M |
| RAG | C | Runs well | 42.0 tok/s | 8381 ms | 1.2M |
How Llama 3.2 3B Instruct (3B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | C43 |
Q3_K_S | 3 | 1.5 GB | Low | C43 |
NVFP4 | 4 |
Copy-paste commands to run Llama 3.2 3B Instruct on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "bartowski/Llama-3.2-3B-Instruct-GGUF" \
--hf-file "Llama-3.2-3B-Instruct-GGUF-Q5_K_M.gguf" \
-c 4096 -ngl 99Yes, MacBook Pro M4 Pro 48GB can run Llama 3.2 3B Instruct with a C grade (Runs well). Expected decode speed: 42.0 tok/s.
Llama 3.2 3B Instruct (3B parameters) requires approximately 8.6 GB of memory with Q5_K_M quantization.
The recommended quantization for Llama 3.2 3B Instruct is Q5_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Pro 48GB, Llama 3.2 3B Instruct achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q5_K_M quantization.
For coding workloads, Llama 3.2 3B Instruct on MacBook Pro M4 Pro 48GB receives a C grade with 42.0 tok/s and 1.2M context.
On MacBook Pro M4 Pro 48GB, Llama 3.2 3B Instruct can safely use up to 1.2M tokens of context. The model's official context limit is —, 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/hf-bartowski--llama-3-2-3b-instruct-gguf-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:
1.7 GB |
| Medium |
| C43 |
Q4_K_M | 4 | 1.8 GB | Medium | C43 |
Q5_K_M | 5 | 2.2 GB | High | C43 |
Q6_K | 6 | 2.5 GB | High | C43 |
Q8_0 | 8 | 3.2 GB | Very High | C43 |
F16Best for your GPU | 16 | 6.1 GB | Maximum | C44 |
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.