Llama 3.1 8B needs ~11.6 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~22 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
24.1 tok/s
TTFT
8026 ms
Safe context
128K
Memory
11.6 GB / 25.9 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 | B | Runs well | 22.4 tok/s | 4706 ms | 128K |
| Coding | B | Runs well | 22.4 tok/s | 8628 ms | 128K |
| Agentic Coding | B | Runs well | 22.4 tok/s | 12550 ms | 128K |
| Reasoning | B | Runs well | 22.4 tok/s | 10197 ms | 128K |
| RAG | B | Runs well | 22.4 tok/s | 15687 ms | 128K |
How Llama 3.1 8B (8B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | B65 |
Q3_K_S | 3 | 3.9 GB | Low | B65 |
NVFP4 | 4 | 4.5 GB | Medium | B66 |
Q4_K_M | 4 | 4.9 GB | Medium | B66 |
Q5_K_M | 5 | 5.8 GB | High | B66 |
Q6_K | 6 | 6.6 GB | High | B67 |
Q8_0 | 8 | 8.6 GB | Very High | B68 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | A71 |
Copy-paste commands to run Llama 3.1 8B on your machine.
Run
ollama run llama3.1Opciones de mejora
Sube la velocidad estimada de decodificación alrededor de un 243%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,499 MSRP
Sube la velocidad estimada de decodificación alrededor de un 120%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,499 MSRP
Sube la velocidad estimada de decodificación alrededor de un 243%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,499 MSRP
Yes, MacBook Pro M3 Pro 36GB can run Llama 3.1 8B with a B grade (Runs well). Expected decode speed: 22.4 tok/s.
Llama 3.1 8B (8B parameters) requires approximately 11.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 3.1 8B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 36GB, Llama 3.1 8B achieves approximately 22.4 tokens per second decode speed with a time-to-first-token of 8628ms using Q4_K_M quantization.
For coding workloads, Llama 3.1 8B on MacBook Pro M3 Pro 36GB receives a B grade with 22.4 tok/s and 128K context.
On MacBook Pro M3 Pro 36GB, Llama 3.1 8B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Not always. MacBook Pro M3 Pro 36GB 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/llama-3.1-8b-on-m3-pro-36gb" 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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