RTX PRO 5000 Blackwell 48GBBudget pick1344 GB/s (+544)
C
Raises estimated decode speed by about 143%.92.5 tok/s decode
Raises estimated decode speed by about 143%.
~$4,999 MSRP
internlm2 5 20b chat needs ~22.4 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~38 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
38.0 tok/s
TTFT
5090 ms
Safe context
178K
Memory
22.4 GB / 46.1 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 | 38.0 tok/s | 2777 ms | 178K |
| Coding | C | Runs well | 38.0 tok/s | 5090 ms | 178K |
| Agentic Coding | C | Runs well | 38.0 tok/s | 7404 ms | 178K |
| Reasoning | C | Runs well | 38.0 tok/s | 6016 ms | 178K |
| RAG | C | Runs well | 38.0 tok/s | 9255 ms | 178K |
How internlm2 5 20b chat (20B params) fits at each quantization level on Mac Studio M2 Ultra 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
RTX PRO 5000 Blackwell 48GBBudget pickRaises estimated decode speed by about 143%.
~$4,999 MSRP
RTX 6000 Ada 48GBBest valueRaises estimated decode speed by about 70%.
~$6,800 MSRP
Yes, Mac Studio M2 Ultra 64GB can run internlm2 5 20b chat with a C grade (Runs well). Expected decode speed: 38.0 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 Studio M2 Ultra 64GB, internlm2 5 20b chat achieves approximately 38.0 tokens per second decode speed with a time-to-first-token of 5090ms using Q4_K_M quantization.
For coding workloads, internlm2 5 20b chat on Mac Studio M2 Ultra 64GB receives a C grade with 38.0 tok/s and 178K context.
On Mac Studio M2 Ultra 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-m2-ultra-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 |
Not always. Mac Studio M2 Ultra 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.