MacBook Pro M4 Max 48GBBudget pick
48 GB Unified (+16)
C
This setup is broadly balanced for this model.35.6 tok/s decode
~$2,499 MSRP
internlm2 5 20b chat needs ~18.6 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~31 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
31.0 tok/s
TTFT
6255 ms
Safe context
107K
Memory
18.6 GB / 32.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 31.0 tok/s | 3412 ms | 107K |
| Coding | C | Runs well | 31.0 tok/s | 6255 ms | 107K |
| Agentic Coding | C | Runs well | 31.0 tok/s | 9098 ms | 107K |
| Reasoning | C | Runs well | 31.0 tok/s | 7392 ms | 107K |
| RAG | C | Runs well | 31.0 tok/s | 11373 ms | 107K |
How internlm2 5 20b chat (20B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | C45 |
Q3_K_S | 3 | 9.8 GB | Low | C45 |
NVFP4 | 4 | 11.2 GB | Medium | C46 |
Q4_K_M | 4 | 12.2 GB | Medium | C47 |
Q5_K_M | 5 | 14.4 GB | High | C48 |
Q6_K | 6 | 16.4 GB | High | C49 |
Q8_0Best for your GPU | 8 | 21.4 GB | Very High | C49 |
F16 | 16 | 41.0 GB | Maximum | F0 |
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
~$2,499 MSRP
RTX PRO 5000 Blackwell 48GBBest valueRaises estimated decode speed by about 198%.
Adds memory headroom for longer context windows and future model growth.
~$4,999 MSRP
Yes, Radeon AI PRO R9700 32GB can run internlm2 5 20b chat with a C grade (Runs well). Expected decode speed: 31.0 tok/s.
internlm2 5 20b chat (20B parameters) requires approximately 18.6 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 Radeon AI PRO R9700 32GB, internlm2 5 20b chat achieves approximately 31.0 tokens per second decode speed with a time-to-first-token of 6255ms using Q4_K_M quantization.
For coding workloads, internlm2 5 20b chat on Radeon AI PRO R9700 32GB receives a C grade with 31.0 tok/s and 107K context.
On Radeon AI PRO R9700 32GB, internlm2 5 20b chat can safely use up to 107K 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-radeon-ai-pro-r9700-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: