internlm2 math plus 20b i1 needs ~12.5 GB VRAM. RTX 4080 Laptop 12GB has 12.0 GB. With Q2_K quantization, expect ~25 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
4.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
10.0 tok/s
TTFT
19320 ms
Safe context
4K
Memory
16.9 GB / 12.0 GB
Offload
30%
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 11.7 tok/s | 9063 ms | 4K |
| Coding | F | Too heavy | 10.0 tok/s | 19320 ms | 4K |
| Agentic Coding | F | Too heavy | 7.6 tok/s | 36913 ms | 4K |
| Reasoning | F | Too heavy | 10.0 tok/s | 22833 ms | 4K |
| RAG | F | Too heavy | 7.6 tok/s | 46142 ms | 4K |
How internlm2 math plus 20b i1 (20B params) fits at each quantization level on RTX 4080 Laptop 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 7.8 GB | Low | C51 |
Q3_K_S | 3 | 9.8 GB | Low | F0 |
NVFP4 | 4 | 11.2 GB | Medium | F0 |
Q4_K_M | 4 | 12.2 GB | Medium | F0 |
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 |
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 startアップグレードオプション
RTX 5060 Ti 16GBコスパ最良Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 48%.
〜$449 MSRP
RTX 4060 Ti 16GBNVIDIAアップグレードMakes the model fit on the accelerator instead of staying completely out of reach.
Adds memory headroom for longer context windows and future model growth.
〜$499 MSRP
RTX 4000 Ada 20GBコスパ重視Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$1,250 MSRP
RTX 4090 24GB最大の飛躍Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$1,599 MSRP
Yes, RTX 4080 Laptop 12GB can run internlm2 math plus 20b i1 at Q2_K quantization (Runs with offload (needs ~0.3 GB host RAM)). The recommended Q4_K_M requires 16.9 GB which exceeds available memory, but at Q2_K it needs only 12.5 GB. Expected decode speed: 25.1 tok/s.
internlm2 math plus 20b i1 (20B parameters) requires approximately 16.9 GB at Q4_K_M quantization. On RTX 4080 Laptop 12GB, it fits at Q2_K using 12.5 GB.
The recommended quantization is Q4_K_M, but on RTX 4080 Laptop 12GB the best fitting quantization is Q2_K, which uses 12.5 GB.
On RTX 4080 Laptop 12GB, internlm2 math plus 20b i1 achieves approximately 25.1 tokens per second decode speed with a time-to-first-token of 7717ms using Q2_K quantization.
For coding workloads, internlm2 math plus 20b i1 on RTX 4080 Laptop 12GB receives a F grade with 10.0 tok/s and 4K context.
On RTX 4080 Laptop 12GB, internlm2 math plus 20b i1 can safely use up to 12K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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-rtx-4080-laptop-12gb" 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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