Llama 3 8B Instruct 32k v0.1 needs ~13.6 GB VRAM. MacBook Pro M1 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~45 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
45.1 tok/s
TTFT
4294 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 | 45.1 tok/s | 2342 ms | 570K |
| Coding | C | Runs well | 45.1 tok/s | 4294 ms | 570K |
| Agentic Coding | C | Runs well | 45.1 tok/s | 6246 ms | 570K |
| Reasoning | C | Runs well | 45.1 tok/s | 5075 ms | 570K |
| RAG | C | Runs well | 45.1 tok/s | 7808 ms | 570K |
How Llama 3 8B Instruct 32k v0.1 (8B params) fits at each quantization level on MacBook Pro M1 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 Llama 3 8B Instruct 32k v0.1 on your machine.
Run
lms load hf-maziyarpanahi--llama-3-8b-instruct-32k-v0-1-gguf && lms server startUpgrade options
Raises estimated decode speed by about 70%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 148%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 111%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M1 Max 64GB can run Llama 3 8B Instruct 32k v0.1 with a C grade (Runs well). Expected decode speed: 45.1 tok/s.
Llama 3 8B Instruct 32k v0.1 (8B parameters) requires approximately 13.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 3 8B Instruct 32k v0.1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M1 Max 64GB, Llama 3 8B Instruct 32k v0.1 achieves approximately 45.1 tokens per second decode speed with a time-to-first-token of 4294ms using Q4_K_M quantization.
For coding workloads, Llama 3 8B Instruct 32k v0.1 on MacBook Pro M1 Max 64GB receives a C grade with 45.1 tok/s and 570K context.
On MacBook Pro M1 Max 64GB, Llama 3 8B Instruct 32k v0.1 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-maziyarpanahi--llama-3-8b-instruct-32k-v0-1-gguf-on-m1-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 M1 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.