12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.55.6 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can internlm2 5 7b chat i1 run on RTX 3060 Ti 8GB?
YES — Tight Fit
C53Usable
○Estimated from fit model
internlm2 5 7b chat i1 needs ~7.1 GB VRAM. RTX 3060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~71 tok/s.
Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: Balanced
Share:
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) — 7.1 GB, 71.3 tok/s, Tight fit
7.1 GB required8.0 GB available
89% VRAM used
Fit status
Tight fit
Decode
71.3 tok/s
TTFT
2714 ms
Safe context
34K
Memory
7.1 GB / 8.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime1.2 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsinternlm2 5 7b chat i1 on RTX 3060 Ti 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: 71.3 tok/s decode · 2.7s TTFT (warm) · 178 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 71.3 tok/s | 1480 ms | 34K |
| Coding | C | Tight fit | 71.3 tok/s | 2714 ms | 34K |
| Agentic Coding | C | Runs with offload | 71.3 tok/s | 3947 ms | 34K |
| Reasoning | C | Tight fit | 71.3 tok/s | 3207 ms | 34K |
| RAG | C | Runs with offload | 71.3 tok/s | 4934 ms | 34K |
Quantization options
How internlm2 5 7b chat i1 (7B params) fits at each quantization level on RTX 3060 Ti 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C53 |
Q3_K_S | 3 | 3.4 GB | Low | C53 |
NVFP4 | 4 |
Get started
Copy-paste commands to run internlm2 5 7b chat i1 on your machine.
Run
lms load hf-mradermacher--internlm2-5-7b-chat-i1-gguf && lms server startUpgrade options
Hardware that runs internlm2 5 7b chat i1 well
12 GB VRAM (+4)672 GB/s (+224)
C
Raises estimated decode speed by about 37%.98 tok/s decode
Raises estimated decode speed by about 37%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
12 GB VRAM (+4)504 GB/s (+56)
C
Raises estimated decode speed by about 27%.90.9 tok/s decode
Raises estimated decode speed by about 27%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP