internlm2 math plus 20b i1 needs ~17.0 GB VRAM. RTX A4000 16GB has 16.0 GB. With Q4_K_M quantization, expect ~17 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
1.0 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.7 GB host RAM)
Decode
16.9 tok/s
TTFT
11471 ms
Safe context
9K
Memory
17.0 GB / 16.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 25.7 tok/s | 4108 ms | 9K |
| Coding | D | Runs with offload | 16.9 tok/s | 11471 ms | 9K |
| Agentic Coding | F | Too heavy | 12.9 tok/s | 21883 ms | 9K |
| Reasoning | D | Runs with offload (needs ~0.7 GB host RAM) | 16.9 tok/s | 13557 ms | 9K |
| RAG | F | Too heavy | 12.9 tok/s | 27354 ms | 9K |
How internlm2 math plus 20b i1 (20B params) fits at each quantization level on RTX A4000 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | C51 |
Q3_K_S | 3 | 9.8 GB | Low | C51 |
NVFP4 | 4 |
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
RTX 4000 Ada 20GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 36%.
~$1,250 MSRP
RTX 3090 24GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 176%.
~$1,499 MSRP
RTX 4090 24GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 201%.
~$1,599 MSRP
Yes, RTX A4000 16GB can run internlm2 math plus 20b i1 with a D grade (Runs with offload). Expected decode speed: 16.9 tok/s.
internlm2 math plus 20b i1 (20B parameters) requires approximately 17.0 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 A4000 16GB, internlm2 math plus 20b i1 achieves approximately 16.9 tokens per second decode speed with a time-to-first-token of 11471ms using Q4_K_M quantization.
For coding workloads, internlm2 math plus 20b i1 on RTX A4000 16GB receives a D grade with 16.9 tok/s and 9K context.
On RTX A4000 16GB, internlm2 math plus 20b i1 can safely use up to 9K 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-a4000-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| C50 |
Q4_K_MBest for your GPU | 4 | 12.2 GB | Medium | C50 |
Q5_K_M | 5 | 14.4 GB | High | F0 |
Q6_K | 6 | 16.4 GB | High | F0 |
Q8_0 | 8 | 21.4 GB | Very High | F0 |
F16 | 16 | 41.0 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.