internlm3 8b instruct abliterated i1 needs ~8.2 GB VRAM. RTX 4080 Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~69 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
69.0 tok/s
TTFT
2804 ms
Safe context
81K
Memory
8.2 GB / 12.0 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 | 69.0 tok/s | 1529 ms | 81K |
| Coding | B | Runs well | 69.0 tok/s | 2804 ms | 81K |
| Agentic Coding | B | Runs well | 69.0 tok/s | 4078 ms | 81K |
| Reasoning | B | Runs well | 69.0 tok/s | 3314 ms | 81K |
| RAG | B | Runs well | 69.0 tok/s | 5098 ms | 81K |
How internlm3 8b instruct abliterated i1 (8B params) fits at each quantization level on RTX 4080 Laptop 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C49 |
Q3_K_S | 3 | 3.9 GB | Low | C50 |
NVFP4 | 4 | 4.5 GB | Medium | C51 |
Q4_K_M | 4 | 4.9 GB | Medium | C51 |
Q5_K_M | 5 | 5.8 GB | High | C52 |
Q6_K | 6 | 6.6 GB | High | C52 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | C51 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Copy-paste commands to run internlm3 8b instruct abliterated i1 on your machine.
Run
lms load hf-mradermacher--internlm3-8b-instruct-abliterated-i1-gguf && lms server startYes, RTX 4080 Laptop 12GB can run internlm3 8b instruct abliterated i1 with a B grade (Runs well). Expected decode speed: 69.0 tok/s.
internlm3 8b instruct abliterated i1 (8B parameters) requires approximately 8.2 GB of memory with Q4_K_M quantization.
The recommended quantization for internlm3 8b instruct abliterated i1 is Q4_K_M, which balances quality and memory efficiency.
On RTX 4080 Laptop 12GB, internlm3 8b instruct abliterated i1 achieves approximately 69.0 tokens per second decode speed with a time-to-first-token of 2804ms using Q4_K_M quantization.
For coding workloads, internlm3 8b instruct abliterated i1 on RTX 4080 Laptop 12GB receives a B grade with 69.0 tok/s and 81K context.
On RTX 4080 Laptop 12GB, internlm3 8b instruct abliterated i1 can safely use up to 81K 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--internlm3-8b-instruct-abliterated-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>
Preview: