RTX 3060 12GBBudget pick12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.48.7 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
internlm3 8b instruct abliterated i1 needs ~7.8 GB VRAM. RTX 3060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~62 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 with offload
Decode
62.4 tok/s
TTFT
3101 ms
Safe context
19K
Memory
7.8 GB / 8.0 GB
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 | C | Tight fit | 62.4 tok/s | 1692 ms | 19K |
| Coding | C | Runs with offload | 62.4 tok/s | 3101 ms | 19K |
| Agentic Coding | C | Very compromised (needs ~0.4 GB host RAM) | 38.7 tok/s | 7272 ms | 19K |
| Reasoning | C | Runs with offload | 62.4 tok/s | 3665 ms | 19K |
| RAG | C | Very compromised (needs ~0.4 GB host RAM) | 38.7 tok/s | 9090 ms |
How internlm3 8b instruct abliterated i1 (8B params) fits at each quantization level on RTX 3060 Ti 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C53 |
Q3_K_S | 3 | 3.9 GB | Low | C53 |
NVFP4 | 4 |
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 startUpgrade options
RTX 3060 12GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$329 MSRP
RTX 5070 12GBBest valueRaises estimated decode speed by about 39%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
RTX 4070 Super 12GBNVIDIA upgradeRaises estimated decode speed by about 27%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP
Yes, RTX 3060 Ti 8GB can run internlm3 8b instruct abliterated i1 with a C grade (Runs with offload). Expected decode speed: 62.4 tok/s.
internlm3 8b instruct abliterated i1 (8B parameters) requires approximately 7.8 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 3060 Ti 8GB, internlm3 8b instruct abliterated i1 achieves approximately 62.4 tokens per second decode speed with a time-to-first-token of 3101ms using Q4_K_M quantization.
For coding workloads, internlm3 8b instruct abliterated i1 on RTX 3060 Ti 8GB receives a C grade with 62.4 tok/s and 19K context.
On RTX 3060 Ti 8GB, internlm3 8b instruct abliterated i1 can safely use up to 19K 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-3060-ti-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 19K |
4.5 GB |
| Medium |
| C53 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | C52 |
Q5_K_M | 5 | 5.8 GB | High | F0 |
Q6_K | 6 | 6.6 GB | High | F0 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.