internlm3 8b instruct abliterated i1 needs ~6.9 GB VRAM. RX 5600 XT 6GB has 6.0 GB. With NVFP4 quantization, expect ~20 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
1.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
15.2 tok/s
TTFT
12774 ms
Safe context
4K
Memory
7.3 GB / 6.0 GB
Offload
20%
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 0.6 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Very compromised (needs ~0.6 GB host RAM) | 17.4 tok/s | 6061 ms | 4K |
| Coding | F | Too heavy | 15.2 tok/s | 12774 ms | 4K |
| Agentic Coding | F | Too heavy | 11.8 tok/s | 23948 ms | 4K |
| Reasoning | F | Too heavy | 15.2 tok/s | 15097 ms | 4K |
| RAG | F | Too heavy | 11.8 tok/s | 29935 ms | 4K |
How internlm3 8b instruct abliterated i1 (8B params) fits at each quantization level on RX 5600 XT 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 3.1 GB | Low | C53 |
Q3_K_S | 3 | 3.9 GB | Low | 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 startUpgrade options
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.
~$229 MSRP
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.
~$249 MSRP
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.
~$269 MSRP
RTX 3080 10GBBiggest 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.
~$699 MSRP
Yes, RX 5600 XT 6GB can run internlm3 8b instruct abliterated i1 at NVFP4 quantization (Very compromised (needs ~0.6 GB host RAM)). The recommended Q4_K_M requires 7.3 GB which exceeds available memory, but at NVFP4 it needs only 6.9 GB. Expected decode speed: 19.5 tok/s.
internlm3 8b instruct abliterated i1 (8B parameters) requires approximately 7.3 GB at Q4_K_M quantization. On RX 5600 XT 6GB, it fits at NVFP4 using 6.9 GB.
The recommended quantization is Q4_K_M, but on RX 5600 XT 6GB the best fitting quantization is NVFP4, which uses 6.9 GB.
On RX 5600 XT 6GB, internlm3 8b instruct abliterated i1 achieves approximately 19.5 tokens per second decode speed with a time-to-first-token of 9923ms using NVFP4 quantization.
For coding workloads, internlm3 8b instruct abliterated i1 on RX 5600 XT 6GB receives a F grade with 15.2 tok/s and 4K context.
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-rx-5600-xt-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4 |
4.5 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 4.9 GB | Medium | F0 |
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 |
On RX 5600 XT 6GB, internlm3 8b instruct abliterated i1 can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is —, but available memory constrains the safe maximum.
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.