InternLM 7B needs ~14.9 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q4_K_M quantization, expect ~21 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
1.9 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.6 GB host RAM)
Decode
20.6 tok/s
TTFT
9409 ms
Safe context
8K
Memory
14.9 GB / 13.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 25.6 tok/s | 4118 ms | 8K |
| Coding | B | Very compromised | 20.6 tok/s | 9409 ms | 8K |
| Agentic Coding | F | Too heavy | 12.6 tok/s | 22370 ms | 8K |
| Reasoning | B | Very compromised (needs ~0.6 GB host RAM) | 20.6 tok/s | 11119 ms | 8K |
| RAG | F | Too heavy | 12.6 tok/s | 27963 ms | 8K |
How InternLM 7B (7B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B69 |
Q3_K_S | 3 | 3.4 GB | Low | B70 |
NVFP4 | 4 | 3.9 GB | Medium | A70 |
Q4_K_M | 4 | 4.3 GB | Medium | A71 |
Q5_K_M | 5 | 5.0 GB | High | A72 |
Q6_K | 6 | 5.7 GB | High | A73 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | A72 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Copy-paste commands to run InternLM 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "InternLM/InternLM-7B" \
--hf-file "InternLM-7B-Q4_K_M.gguf" \
-c 4096 -ngl 99升级选项
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 376%.
~$899 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Yes, MacBook Pro M3 Pro 18GB can run InternLM 7B with a B grade (Very compromised). Expected decode speed: 20.6 tok/s.
InternLM 7B (7B parameters) requires approximately 14.9 GB of memory with Q4_K_M quantization.
The recommended quantization for InternLM 7B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 18GB, InternLM 7B achieves approximately 20.6 tokens per second decode speed with a time-to-first-token of 9409ms using Q4_K_M quantization.
For coding workloads, InternLM 7B on MacBook Pro M3 Pro 18GB receives a B grade with 20.6 tok/s and 8K context.
On MacBook Pro M3 Pro 18GB, InternLM 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Not always. MacBook Pro M3 Pro 18GB 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/internlm-7b-on-m3-pro-18gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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