RTX 5090 32GBBudget pick1792 GB/s (+1382)
C
Raises estimated decode speed by about 217%.89.7 tok/s decode
Raises estimated decode speed by about 217%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
internlm2 5 20b chat needs ~19.3 GB VRAM. MacBook Pro M4 Max 36GB has 25.9 GB. With Q4_K_M quantization, expect ~28 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
28.3 tok/s
TTFT
6829 ms
Safe context
61K
Memory
19.3 GB / 25.9 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 | 28.3 tok/s | 3725 ms | 61K |
| Coding | C | Runs well | 28.3 tok/s | 6829 ms | 61K |
| Agentic Coding | C | Tight fit | 28.3 tok/s | 9933 ms | 61K |
| Reasoning | C | Runs well | 21.2 tok/s | 10807 ms | 61K |
| RAG | C | Tight fit | 28.3 tok/s | 12416 ms | 61K |
How internlm2 5 20b chat (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 | C46 |
Q3_K_S | 3 | 9.8 GB | Low | C47 |
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
RTX 5090 32GBBudget pickRaises estimated decode speed by about 217%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 131%.
Adds memory headroom for longer context windows and future model growth.
~$11,500 MSRP
Yes, MacBook Pro M4 Max 36GB can run internlm2 5 20b chat with a C grade (Runs well). Expected decode speed: 28.3 tok/s.
internlm2 5 20b chat (20B parameters) requires approximately 19.3 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 MacBook Pro M4 Max 36GB, internlm2 5 20b chat achieves approximately 28.3 tokens per second decode speed with a time-to-first-token of 6829ms using Q4_K_M quantization.
For coding workloads, internlm2 5 20b chat on MacBook Pro M4 Max 36GB receives a C grade with 28.3 tok/s and 61K context.
On MacBook Pro M4 Max 36GB, internlm2 5 20b chat can safely use up to 61K 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-max-36gb" 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 |
| C48 |
Q4_K_M | 4 | 12.2 GB | Medium | C49 |
Q5_K_M | 5 | 14.4 GB | High | C50 |
Q6_KBest for your GPU | 6 | 16.4 GB | High | C49 |
Q8_0 | 8 | 21.4 GB | Very High | F0 |
F16 | 16 | 41.0 GB | Maximum | F0 |
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.