internlm2 math plus 20b i1 needs ~14.2 GB VRAM. Radeon RX 7800M 12GB has 12.0 GB. With Q3_K_S quantization, expect ~13 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
4.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
7.9 tok/s
TTFT
24599 ms
Safe context
4K
Memory
16.6 GB / 12.0 GB
Offload
30%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% 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 1.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 9.2 tok/s | 11506 ms | 4K |
| Coding | F | Too heavy | 7.9 tok/s | 24599 ms | 4K |
| Agentic Coding | F | Too heavy | 6.0 tok/s | 47216 ms | 4K |
| Reasoning | F | Too heavy | 7.9 tok/s | 29072 ms | 4K |
| RAG | F | Too heavy | 6.0 tok/s | 59021 ms | 4K |
How internlm2 math plus 20b i1 (20B params) fits at each quantization level on Radeon RX 7800M 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 |
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
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.
~$329 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 37%.
~$349 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 177%.
~$479 MSRP
RTX 5090 Laptop 24GBBiggest leapMakes 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.
Yes, Radeon RX 7800M 12GB can run internlm2 math plus 20b i1 at Q3_K_S quantization (Very compromised (needs ~1.5 GB host RAM)). The recommended Q4_K_M requires 16.6 GB which exceeds available memory, but at Q3_K_S it needs only 14.2 GB. Expected decode speed: 12.6 tok/s.
internlm2 math plus 20b i1 (20B parameters) requires approximately 16.6 GB at Q4_K_M quantization. On Radeon RX 7800M 12GB, it fits at Q3_K_S using 14.2 GB.
The recommended quantization is Q4_K_M, but on Radeon RX 7800M 12GB the best fitting quantization is Q3_K_S, which uses 14.2 GB.
On Radeon RX 7800M 12GB, internlm2 math plus 20b i1 achieves approximately 12.6 tokens per second decode speed with a time-to-first-token of 15309ms using Q3_K_S quantization.
For coding workloads, internlm2 math plus 20b i1 on Radeon RX 7800M 12GB receives a F grade with 7.9 tok/s and 4K context.
On Radeon RX 7800M 12GB, internlm2 math plus 20b i1 can safely use up to 4K tokens of context at Q3_K_S quantization. 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-rx-7800m-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 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 |
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.