internlm3 8b instruct abliterated i1 needs ~9.3 GB VRAM. MacBook Air M4 24GB has 17.3 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
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 | 17.7 tok/s | 10935 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 internlm3 8b instruct abliterated i1 (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 | C46 |
Q3_K_S | 3 | 3.9 GB | Low | C47 |
NVFP4 | 4 |
Copy-paste commands to run internlm3 8b instruct abliterated i1 on your machine.
Run
lms load hf-mradermacher--internlm3-8b-instruct-abliterated-i1-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 internlm3 8b instruct abliterated i1 with a C grade (Runs well). Expected decode speed: 17.7 tok/s.
internlm3 8b instruct abliterated i1 (8B parameters) requires approximately 9.3 GB of memory with Q4_K_M quantization.
The recommended quantization for internlm3 8b instruct abliterated i1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Air M4 24GB, internlm3 8b instruct abliterated i1 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, internlm3 8b instruct abliterated i1 on MacBook Air M4 24GB receives a C grade with 17.7 tok/s and 152K context.
On MacBook Air M4 24GB, internlm3 8b instruct abliterated i1 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-mradermacher--internlm3-8b-instruct-abliterated-i1-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:
4.5 GB |
| Medium |
| C47 |
Q4_K_M | 4 | 4.9 GB | Medium | C47 |
Q5_K_M | 5 | 5.8 GB | High | C48 |
Q6_K | 6 | 6.6 GB | High | C49 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | C51 |
F16 | 16 | 16.4 GB | Maximum | F0 |
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.