Meta Llama 3 8B Instruct needs ~9.3 GB VRAM. MacBook Air M4 24GB has 17.3 GB. With Q4_K_M quantization, expect ~16 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
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 | 16.3 tok/s | 6483 ms | 152K |
| Coding | C | Runs well | 16.3 tok/s | 11886 ms | 152K |
| Agentic Coding | C | Runs well | 16.3 tok/s | 17288 ms | 152K |
| Reasoning | C | Runs well | 16.3 tok/s | 14047 ms | 152K |
| RAG | C | Runs well | 16.3 tok/s | 21610 ms | 152K |
How Meta Llama 3 8B Instruct (8B params) fits at each quantization level on MacBook Air M4 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 | 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 |
Copy-paste commands to run Meta Llama 3 8B Instruct on your machine.
Run
lms load hf-maziyarpanahi--meta-llama-3-8b-instruct-gguf && lms server startUpgrade options
Raises estimated decode speed by about 191%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 254%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 177%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, MacBook Air M4 24GB can run Meta Llama 3 8B Instruct with a C grade (Runs well). Expected decode speed: 16.3 tok/s.
Meta Llama 3 8B Instruct (8B parameters) requires approximately 9.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Meta Llama 3 8B Instruct is Q4_K_M, which balances quality and memory efficiency.
On MacBook Air M4 24GB, Meta Llama 3 8B Instruct achieves approximately 16.3 tokens per second decode speed with a time-to-first-token of 11886ms using Q4_K_M quantization.
For coding workloads, Meta Llama 3 8B Instruct on MacBook Air M4 24GB receives a C grade with 16.3 tok/s and 152K context.
On MacBook Air M4 24GB, Meta Llama 3 8B Instruct can safely use up to 152K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. MacBook Air M4 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--meta-llama-3-8b-instruct-gguf-on-m4-air-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: