Llama 3.2 1B Instruct Q8 0 needs ~8.7 GB VRAM. MacBook Pro M4 Pro 64GB has 46.1 GB. With Q6_K quantization, expect ~14 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
14.0 tok/s
TTFT
13829 ms
Safe context
5.1M
Memory
8.7 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 | C | Runs well | 14.0 tok/s | 7543 ms | 3.0M |
| Coding | C | Runs well | 14.0 tok/s | 13829 ms | 5.1M |
| Agentic Coding | C | Runs well | 14.0 tok/s | 20114 ms | 5.1M |
| Reasoning | C | Runs well | 14.0 tok/s | 16343 ms | 5.1M |
| RAG | C | Runs well | 14.0 tok/s | 25143 ms | 5.1M |
How Llama 3.2 1B Instruct Q8 0 (1B params) fits at each quantization level on MacBook Pro M4 Pro 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.4 GB | Low | C42 |
Q3_K_S | 3 | 0.5 GB | Low | C42 |
NVFP4 | 4 |
Copy-paste commands to run Llama 3.2 1B Instruct Q8 0 on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "hugging-quants/Llama-3.2-1B-Instruct-Q8_0-GGUF" \
--hf-file "Llama-3.2-1B-Instruct-Q8_0-GGUF-Q6_K.gguf" \
-c 4096 -ngl 99Yes, MacBook Pro M4 Pro 64GB can run Llama 3.2 1B Instruct Q8 0 with a C grade (Runs well). Expected decode speed: 14.0 tok/s.
Llama 3.2 1B Instruct Q8 0 (1B parameters) requires approximately 8.7 GB of memory with Q6_K quantization.
The recommended quantization for Llama 3.2 1B Instruct Q8 0 is Q6_K, which balances quality and memory efficiency.
On MacBook Pro M4 Pro 64GB, Llama 3.2 1B Instruct Q8 0 achieves approximately 14.0 tokens per second decode speed with a time-to-first-token of 13829ms using Q6_K quantization.
For coding workloads, Llama 3.2 1B Instruct Q8 0 on MacBook Pro M4 Pro 64GB receives a C grade with 14.0 tok/s and 5.1M context.
On MacBook Pro M4 Pro 64GB, Llama 3.2 1B Instruct Q8 0 can safely use up to 5.1M 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-hugging-quants--llama-3-2-1b-instruct-q8-0-gguf-on-m4-pro-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
0.6 GB |
| Medium |
| C42 |
Q4_K_M | 4 | 0.6 GB | Medium | C42 |
Q5_K_M | 5 | 0.7 GB | High | C42 |
Q6_K | 6 | 0.8 GB | High | C42 |
Q8_0 | 8 | 1.1 GB | Very High | C42 |
F16Best for your GPU | 16 | 2.1 GB | Maximum | C42 |
Not always. MacBook Pro M4 Pro 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.