Llama 3 8B Instruct 32k v0.1 needs ~9.3 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~13 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
13.3 tok/s
TTFT
14535 ms
Safe context
152K
Memory
9.3 GB / 17.3 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 | 13.3 tok/s | 7928 ms | 152K |
| Coding | C | Runs well | 13.3 tok/s | 14535 ms | 152K |
| Agentic Coding | C | Runs well | 13.3 tok/s | 21142 ms | 152K |
| Reasoning | C | Runs well | 13.3 tok/s | 17178 ms | 152K |
| RAG | C | Runs well | 13.3 tok/s | 26427 ms | 152K |
How Llama 3 8B Instruct 32k v0.1 (8B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C47 |
Q3_K_S | 3 | 3.9 GB | Low | C47 |
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 257%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 116%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 615%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, Mac mini M2 24GB can run Llama 3 8B Instruct 32k v0.1 with a C grade (Runs well). Expected decode speed: 13.3 tok/s.
Llama 3 8B Instruct 32k v0.1 (8B parameters) requires approximately 9.3 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 Mac mini M2 24GB, Llama 3 8B Instruct 32k v0.1 achieves approximately 13.3 tokens per second decode speed with a time-to-first-token of 14535ms using Q4_K_M quantization.
For coding workloads, Llama 3 8B Instruct 32k v0.1 on Mac mini M2 24GB receives a C grade with 13.3 tok/s and 152K context.
On Mac mini M2 24GB, Llama 3 8B Instruct 32k v0.1 can safely use up to 152K 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-m2-24gb" 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 |
| C48 |
Q4_K_M | 4 | 4.9 GB | Medium | C48 |
Q5_K_M | 5 | 5.8 GB | High | C49 |
Q6_K | 6 | 6.6 GB | High | C50 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | C52 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Not always. Mac mini M2 24GB 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.