RTX 3060 12GB经济之选12 GB VRAM (+4)360 GB/s (+40)
C
Raises estimated decode speed by about 26%.48.7 tok/s 解码
Raises estimated decode speed by about 26%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can internlm3 8b instruct abliterated i1 run on GTX 1080 8GB?
YES — With Offload
C51Usable
Estimated from fit model
internlm3 8b instruct abliterated i1 needs ~7.8 GB VRAM. GTX 1080 8GB has 8.0 GB. With Q4_K_M quantization, expect ~39 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 7.8 GB, 38.7 tok/s, Runs with offload
7.8 GB required8.0 GB available
98% VRAM used
Fit status
Runs with offload
Decode
38.7 tok/s
TTFT
5004 ms
Safe context
19K
Memory
7.8 GB / 8.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsinternlm3 8b instruct abliterated i1 on GTX 1080 8GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 38.7 tok/s decode · 5.0s TTFT (warm) · 97 tok/s prefill
What limits this setup
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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 38.7 tok/s | 2729 ms | 19K |
| Coding | C | Runs with offload | 38.7 tok/s | 5004 ms | 19K |
| Agentic Coding | D | Very compromised (needs ~0.4 GB host RAM) | 23.1 tok/s | 12164 ms | 19K |
| Reasoning | C | Runs with offload | 38.7 tok/s | 5914 ms | 19K |
| RAG | D | Very compromised (needs ~0.4 GB host RAM) | 23.1 tok/s | 15205 ms | 19K |
Quantization options
How internlm3 8b instruct abliterated i1 (8B params) fits at each quantization level on GTX 1080 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 | 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 |
Get started
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 start升级选项
能流畅运行 internlm3 8b instruct abliterated i1 的硬件
RTX 3060 12GB经济之选RTX 5060 Ti 16GB最佳性价比16 GB VRAM (+8)448 GB/s (+128)
C
Raises estimated decode speed by about 47%.56.9 tok/s 解码
Raises estimated decode speed by about 47%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 5070 12GBNVIDIA 升级12 GB VRAM (+4)672 GB/s (+352)
B
Raises estimated decode speed by about 124%.86.8 tok/s 解码
Raises estimated decode speed by about 124%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
Frequently asked questions
Can GTX 1080 8GB run internlm3 8b instruct abliterated i1?
Yes, GTX 1080 8GB can run internlm3 8b instruct abliterated i1 with a C grade (Runs with offload). Expected decode speed: 38.7 tok/s.
How much VRAM does internlm3 8b instruct abliterated i1 need?
internlm3 8b instruct abliterated i1 (8B parameters) requires approximately 7.8 GB of memory with Q4_K_M quantization.
What is the best quantization for internlm3 8b instruct abliterated i1?
The recommended quantization for internlm3 8b instruct abliterated i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will internlm3 8b instruct abliterated i1 run at on GTX 1080 8GB?
On GTX 1080 8GB, internlm3 8b instruct abliterated i1 achieves approximately 38.7 tokens per second decode speed with a time-to-first-token of 5004ms using Q4_K_M quantization.
Can GTX 1080 8GB run internlm3 8b instruct abliterated i1 for coding?
For coding workloads, internlm3 8b instruct abliterated i1 on GTX 1080 8GB receives a C grade with 38.7 tok/s and 19K context.
What context window can internlm3 8b instruct abliterated i1 use on GTX 1080 8GB?
On GTX 1080 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.
What should I upgrade first if internlm3 8b instruct abliterated i1 feels slow on GTX 1080 8GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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