RX 7700 XT 12GBBudget pick
432 GB/s (+232)
C
Raises estimated decode speed by about 85%.60.7 tok/s decode
Raises estimated decode speed by about 85%.
~$449 MSRP
internlm2 5 7b chat i1 needs ~7.7 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~33 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
32.8 tok/s
TTFT
5905 ms
Safe context
90K
Memory
7.7 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 | 32.8 tok/s | 3221 ms | 90K |
| Coding | C | Runs well | 32.8 tok/s | 5905 ms | 90K |
| Agentic Coding | C | Runs well | 32.8 tok/s | 8589 ms | 90K |
| Reasoning | C | Runs well | 32.8 tok/s | 6978 ms | 90K |
| RAG | C | Runs well | 32.8 tok/s | 10736 ms | 90K |
How internlm2 5 7b chat i1 (7B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C49 |
Q3_K_S | 3 | 3.4 GB | Low | C50 |
NVFP4 | 4 |
Copy-paste commands to run internlm2 5 7b chat i1 on your machine.
Run
lms load hf-mradermacher--internlm2-5-7b-chat-i1-gguf && lms server startUpgrade options
Raises estimated decode speed by about 85%.
~$449 MSRP
RTX 5070 12GBBest valueRaises estimated decode speed by about 202%.
~$549 MSRP
Yes, MacBook Pro M2 Pro 16GB can run internlm2 5 7b chat i1 with a C grade (Runs well). Expected decode speed: 32.8 tok/s.
internlm2 5 7b chat i1 (7B parameters) requires approximately 7.7 GB of memory with Q4_K_M quantization.
The recommended quantization for internlm2 5 7b chat i1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 16GB, internlm2 5 7b chat i1 achieves approximately 32.8 tokens per second decode speed with a time-to-first-token of 5905ms using Q4_K_M quantization.
For coding workloads, internlm2 5 7b chat i1 on MacBook Pro M2 Pro 16GB receives a C grade with 32.8 tok/s and 90K context.
On MacBook Pro M2 Pro 16GB, internlm2 5 7b chat i1 can safely use up to 90K 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--internlm2-5-7b-chat-i1-gguf-on-m2-pro-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| C50 |
Q4_K_M | 4 | 4.3 GB | Medium | C51 |
Q5_K_M | 5 | 5.0 GB | High | C52 |
Q6_K | 6 | 5.7 GB | High | C52 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | C51 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Not always. MacBook Pro M2 Pro 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.