Intel Arc B580 12GBBest value456 GB/s (+336)
C
Raises estimated decode speed by about 175%.44.9 tok/s decode
Raises estimated decode speed by about 175%.
~$249 MSRP
Llama 3 8B Instruct 32k v0.1 needs ~8.4 GB VRAM. MacBook Pro M4 16GB has 11.5 GB. With Q4_K_M quantization, expect ~18 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
16.3 tok/s
TTFT
11886 ms
Safe context
68K
Memory
8.4 GB / 11.5 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 | 16.3 tok/s | 6483 ms | 68K |
| Coding | C | Runs well | 17.7 tok/s | 10935 ms | 68K |
| Agentic Coding | C | Runs well | 16.3 tok/s | 17288 ms | 68K |
| Reasoning | C | Runs well | 16.3 tok/s | 14047 ms | 68K |
| RAG | C | Runs well | 16.3 tok/s | 21610 ms | 68K |
How Llama 3 8B Instruct 32k v0.1 (8B params) fits at each quantization level on MacBook Pro M4 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C50 |
Q3_K_S | 3 | 3.9 GB | Low | C51 |
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
Intel Arc B580 12GBBest valueRaises estimated decode speed by about 175%.
~$249 MSRP
Raises estimated decode speed by about 37%.
~$1,999 MSRP
Yes, MacBook Pro M4 16GB can run Llama 3 8B Instruct 32k v0.1 with a C grade (Runs well). Expected decode speed: 17.7 tok/s.
Llama 3 8B Instruct 32k v0.1 (8B parameters) requires approximately 8.4 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 M4 16GB, Llama 3 8B Instruct 32k v0.1 achieves approximately 17.7 tokens per second decode speed with a time-to-first-token of 10935ms using Q4_K_M quantization.
For coding workloads, Llama 3 8B Instruct 32k v0.1 on MacBook Pro M4 16GB receives a C grade with 17.7 tok/s and 68K context.
On MacBook Pro M4 16GB, Llama 3 8B Instruct 32k v0.1 can safely use up to 68K 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-m4-16gb" 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 |
| C52 |
Q4_K_M | 4 | 4.9 GB | Medium | C53 |
Q5_K_M | 5 | 5.8 GB | High | C53 |
Q6_KBest for your GPU | 6 | 6.6 GB | High | C52 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Not always. MacBook Pro M4 16GB 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.