internlm2 math plus 20b i1 needs ~18.1 GB VRAM. NVIDIA A10 24GB has 24.0 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
38.4 tok/s
TTFT
5047 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 | 38.4 tok/s | 2753 ms | 56K |
| Coding | C | Runs well | 38.4 tok/s | 5047 ms | 56K |
| Agentic Coding | C | Tight fit | 38.4 tok/s | 7341 ms | 56K |
| Reasoning | C | Runs well | 38.4 tok/s | 5964 ms | 56K |
| RAG | C | Tight fit | 38.4 tok/s | 9176 ms | 56K |
How internlm2 math plus 20b i1 (20B params) fits at each quantization level on NVIDIA A10 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 startUpgrade options
Yes, NVIDIA A10 24GB can run internlm2 math plus 20b i1 with a C grade (Runs well). Expected decode speed: 38.4 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 NVIDIA A10 24GB, internlm2 math plus 20b i1 achieves approximately 38.4 tokens per second decode speed with a time-to-first-token of 5047ms using Q4_K_M quantization.
For coding workloads, internlm2 math plus 20b i1 on NVIDIA A10 24GB receives a C grade with 38.4 tok/s and 56K context.
On NVIDIA A10 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--internlm2-math-plus-20b-i1-gguf-on-a10-24gb" 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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