internlm2 5 1 8b chat i1 needs ~17.1 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~71 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
76.8 tok/s
TTFT
2520 ms
Safe context
904K
Memory
17.1 GB / 69.1 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 | 70.5 tok/s | 1498 ms | 904K |
| Coding | C | Runs well | 70.5 tok/s | 2747 ms | 904K |
| Agentic Coding | C | Runs well | 70.5 tok/s | 3995 ms | 904K |
| Reasoning | C | Runs well | 70.5 tok/s | 3246 ms | 904K |
| RAG | C | Runs well | 70.5 tok/s | 4994 ms | 904K |
How internlm2 5 1 8b chat i1 (8B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | D40 |
Q3_K_S | 3 | 3.9 GB | Low | D40 |
NVFP4 | 4 |
Copy-paste commands to run internlm2 5 1 8b chat i1 on your machine.
Run
lms load hf-mradermacher--internlm2-5-1-8b-chat-i1-gguf && lms server startUpgrade options
Yes, MacBook Pro M4 Max 96GB can run internlm2 5 1 8b chat i1 with a C grade (Runs well). Expected decode speed: 70.5 tok/s.
internlm2 5 1 8b chat i1 (8B parameters) requires approximately 17.1 GB of memory with Q4_K_M quantization.
The recommended quantization for internlm2 5 1 8b chat i1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Max 96GB, internlm2 5 1 8b chat i1 achieves approximately 70.5 tokens per second decode speed with a time-to-first-token of 2747ms using Q4_K_M quantization.
For coding workloads, internlm2 5 1 8b chat i1 on MacBook Pro M4 Max 96GB receives a C grade with 70.5 tok/s and 904K context.
On MacBook Pro M4 Max 96GB, internlm2 5 1 8b chat i1 can safely use up to 904K 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--internlm2-5-1-8b-chat-i1-gguf-on-m4-max-96gb" 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 |
| D40 |
Q4_K_M | 4 | 4.9 GB | Medium | D40 |
Q5_K_M | 5 | 5.8 GB | High | D40 |
Q6_K | 6 | 6.6 GB | High | D40 |
Q8_0 | 8 | 8.6 GB | Very High | C40 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C41 |
Not always. MacBook Pro M4 Max 96GB 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.