internlm2 5 20b chat needs ~22.4 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~7 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
9.2 tok/s
TTFT
21028 ms
Safe context
178K
Memory
22.4 GB / 46.1 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 7.1 tok/s | 14911 ms | 178K |
| Coding | C | Runs well | 7.1 tok/s | 27337 ms | 178K |
| Agentic Coding | C | Runs well | 7.1 tok/s | 39763 ms | 178K |
| Reasoning | C | Runs well | 7.1 tok/s | 32307 ms | 178K |
| RAG | C | Runs well | 7.1 tok/s | 49704 ms | 178K |
How internlm2 5 20b chat (20B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | C42 |
Q3_K_S | 3 | 9.8 GB | Low | C43 |
NVFP4 | 4 |
Copy-paste commands to run internlm2 5 20b chat on your machine.
Run
lms load hf-bartowski--internlm2-5-20b-chat-gguf && lms server startUpgrade options
Raises estimated decode speed by about 287%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 396%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Raises estimated decode speed by about 313%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, Mac mini M4 64GB can run internlm2 5 20b chat with a C grade (Runs well). Expected decode speed: 7.1 tok/s.
internlm2 5 20b chat (20B parameters) requires approximately 22.4 GB of memory with Q4_K_M quantization.
The recommended quantization for internlm2 5 20b chat is Q4_K_M, which balances quality and memory efficiency.
On Mac mini M4 64GB, internlm2 5 20b chat achieves approximately 7.1 tokens per second decode speed with a time-to-first-token of 27337ms using Q4_K_M quantization.
For coding workloads, internlm2 5 20b chat on Mac mini M4 64GB receives a C grade with 7.1 tok/s and 178K context.
On Mac mini M4 64GB, internlm2 5 20b chat can safely use up to 178K 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-bartowski--internlm2-5-20b-chat-gguf-on-m4-mini-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
11.2 GB |
| Medium |
| C43 |
Q4_K_M | 4 | 12.2 GB | Medium | C43 |
Q5_K_M | 5 | 14.4 GB | High | C44 |
Q6_K | 6 | 16.4 GB | High | C45 |
Q8_0Best for your GPU | 8 | 21.4 GB | Very High | C47 |
F16 | 16 | 41.0 GB | Maximum | F0 |
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Not always. Mac mini M4 64GB 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.