vntl llama3 8b v2 needs ~10.6 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
22.4 tok/s
TTFT
8628 ms
Safe context
277K
Memory
10.6 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 | C | Runs well | 22.4 tok/s | 4706 ms | 277K |
| Coding | C | Runs well | 22.4 tok/s | 8628 ms | 277K |
| Agentic Coding | C | Runs well | 22.4 tok/s | 12550 ms | 277K |
| Reasoning | C | Runs well | 22.4 tok/s | 10197 ms | 277K |
| RAG | C | Runs well | 22.4 tok/s | 15687 ms | 277K |
How vntl llama3 8b v2 (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 | C44 |
Q3_K_S | 3 | 3.9 GB | Low | C45 |
NVFP4 | 4 | 4.5 GB | Medium | C45 |
Q4_K_M | 4 | 4.9 GB | Medium | C45 |
Q5_K_M | 5 | 5.8 GB | High | C45 |
Q6_K | 6 | 6.6 GB | High | C46 |
Q8_0 | 8 | 8.6 GB | Very High | C47 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C50 |
Copy-paste commands to run vntl llama3 8b v2 on your machine.
Run
lms load hf-lmg-anon--vntl-llama3-8b-v2-gguf && lms server startOpções de upgrade
Raises estimated decode speed by about 243%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 120%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 243%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, MacBook Pro M3 Pro 36GB can run vntl llama3 8b v2 with a C grade (Runs well). Expected decode speed: 22.4 tok/s.
vntl llama3 8b v2 (8B parameters) requires approximately 10.6 GB of memory with Q4_K_M quantization.
The recommended quantization for vntl llama3 8b v2 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 36GB, vntl llama3 8b v2 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, vntl llama3 8b v2 on MacBook Pro M3 Pro 36GB receives a C grade with 22.4 tok/s and 277K context.
On MacBook Pro M3 Pro 36GB, vntl llama3 8b v2 can safely use up to 277K tokens of context. The model's official context limit is —, 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/hf-lmg-anon--vntl-llama3-8b-v2-gguf-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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