internlm2 math plus 20b i1 needs ~18.1 GB VRAM. RTX PRO 4000 Blackwell 24GB has 24.0 GB. With Q4_K_M quantization, expect ~46 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
46.3 tok/s
TTFT
4184 ms
Safe context
56K
Memory
18.1 GB / 24.0 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 | 46.3 tok/s | 2282 ms | 56K |
| Coding | C | Runs well | 46.3 tok/s | 4184 ms | 56K |
| Agentic Coding | C | Tight fit | 46.3 tok/s | 6086 ms | 56K |
| Reasoning | C | Runs well | 46.3 tok/s | 4945 ms | 56K |
| RAG | C | Tight fit | 46.3 tok/s | 7608 ms | 56K |
How internlm2 math plus 20b i1 (20B params) fits at each quantization level on RTX PRO 4000 Blackwell 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | C47 |
Q3_K_S | 3 | 9.8 GB | Low | C48 |
NVFP4 | 4 | 11.2 GB | Medium | C49 |
Q4_K_M | 4 | 12.2 GB | Medium | C50 |
Q5_K_M | 5 | 14.4 GB | High | C49 |
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 |
Copy-paste commands to run internlm2 math plus 20b i1 on your machine.
Run
lms load hf-mradermacher--internlm2-math-plus-20b-i1-gguf && lms server startYes, RTX PRO 4000 Blackwell 24GB can run internlm2 math plus 20b i1 with a C grade (Runs well). Expected decode speed: 46.3 tok/s.
internlm2 math plus 20b i1 (20B parameters) requires approximately 18.1 GB of memory with Q4_K_M quantization.
The recommended quantization for internlm2 math plus 20b i1 is Q4_K_M, which balances quality and memory efficiency.
On RTX PRO 4000 Blackwell 24GB, internlm2 math plus 20b i1 achieves approximately 46.3 tokens per second decode speed with a time-to-first-token of 4184ms using Q4_K_M quantization.
For coding workloads, internlm2 math plus 20b i1 on RTX PRO 4000 Blackwell 24GB receives a C grade with 46.3 tok/s and 56K context.
On RTX PRO 4000 Blackwell 24GB, internlm2 math plus 20b i1 can safely use up to 56K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-mradermacher--internlm2-math-plus-20b-i1-gguf-on-rtx-pro-4000-blackwell-24gb" 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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