InternLM 20B needs ~30.9 GB VRAM. MacBook Pro M4 Max 36GB has 25.9 GB. With Q2_K quantization, expect ~29 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
11.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
14.9 tok/s
TTFT
12967 ms
Safe context
6K
Memory
37.5 GB / 25.9 GB
Offload
30%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% 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 1.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Very compromised (needs ~1.2 GB host RAM) | 21.1 tok/s | 5011 ms | 6K |
| Coding | F | Too heavy | 14.9 tok/s | 12967 ms | 6K |
| Agentic Coding | F | Too heavy | 11.0 tok/s | 25543 ms | 6K |
| Reasoning | F | Too heavy | 14.9 tok/s | 15325 ms | 6K |
| RAG | F | Too heavy | 11.0 tok/s | 31929 ms | 6K |
How InternLM 20B (20B params) fits at each quantization level on MacBook Pro M4 Max 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | C55 |
Q3_K_S | 3 | 9.8 GB | Low | B56 |
NVFP4 | 4 | 11.2 GB | Medium | B57 |
Q4_K_M | 4 | 12.2 GB | Medium | B57 |
Q5_K_M | 5 | 14.4 GB | High | B58 |
Q6_KBest for your GPU | 6 | 16.4 GB | High | B58 |
Q8_0 | 8 | 21.4 GB | Very High | F0 |
F16 | 16 | 41.0 GB | Maximum | F0 |
Copy-paste commands to run InternLM 20B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "internlm/internlm2_5-20b-chat" \
--hf-file "internlm2_5-20b-chat-Q5_K_M.gguf" \
-c 4096 -ngl 99升级选项
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,099 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,599 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$2,499 MSRP
Yes, MacBook Pro M4 Max 36GB can run InternLM 20B at Q2_K quantization (Very compromised (needs ~1.3 GB host RAM)). The recommended Q5_K_M requires 37.5 GB which exceeds available memory, but at Q2_K it needs only 30.9 GB. Expected decode speed: 28.9 tok/s.
InternLM 20B (20B parameters) requires approximately 37.5 GB at Q5_K_M quantization. On MacBook Pro M4 Max 36GB, it fits at Q2_K using 30.9 GB.
The recommended quantization is Q5_K_M, but on MacBook Pro M4 Max 36GB the best fitting quantization is Q2_K, which uses 30.9 GB.
On MacBook Pro M4 Max 36GB, InternLM 20B achieves approximately 28.9 tokens per second decode speed with a time-to-first-token of 6689ms using Q2_K quantization.
For coding workloads, InternLM 20B on MacBook Pro M4 Max 36GB receives a F grade with 14.9 tok/s and 6K context.
On MacBook Pro M4 Max 36GB, InternLM 20B can safely use up to 8K tokens of context at Q2_K quantization. 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 M4 Max 36GB 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-20b-on-m4-max-36gb" 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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