vntl llama3 8b v2 needs ~13.6 GB VRAM. MacBook Pro M3 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~49 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
49.2 tok/s
TTFT
3937 ms
Safe context
570K
Memory
13.6 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 | 49.2 tok/s | 2147 ms | 570K |
| Coding | C | Runs well | 49.2 tok/s | 3937 ms | 570K |
| Agentic Coding | C | Runs well | 49.2 tok/s | 5726 ms | 570K |
| Reasoning | C | Runs well | 49.2 tok/s | 4652 ms | 570K |
| RAG | C | Runs well | 49.2 tok/s | 7157 ms | 570K |
How vntl llama3 8b v2 (8B params) fits at each quantization level on MacBook Pro M3 Max 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C42 |
Q3_K_S | 3 | 3.9 GB | Low | C42 |
NVFP4 | 4 |
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 startUpgrade options
Raises estimated decode speed by about 56%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 128%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 93%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M3 Max 64GB can run vntl llama3 8b v2 with a C grade (Runs well). Expected decode speed: 49.2 tok/s.
vntl llama3 8b v2 (8B parameters) requires approximately 13.6 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 Max 64GB, vntl llama3 8b v2 achieves approximately 49.2 tokens per second decode speed with a time-to-first-token of 3937ms using Q4_K_M quantization.
For coding workloads, vntl llama3 8b v2 on MacBook Pro M3 Max 64GB receives a C grade with 49.2 tok/s and 570K context.
On MacBook Pro M3 Max 64GB, vntl llama3 8b v2 can safely use up to 570K 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-lmg-anon--vntl-llama3-8b-v2-gguf-on-m3-max-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
4.5 GB |
| Medium |
| C42 |
Q4_K_M | 4 | 4.9 GB | Medium | C42 |
Q5_K_M | 5 | 5.8 GB | High | C42 |
Q6_K | 6 | 6.6 GB | High | C42 |
Q8_0 | 8 | 8.6 GB | Very High | C43 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C45 |
Not always. MacBook Pro M3 Max 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.