RTX 3060 12GBBudget pick12 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
internlm2 5 7b chat i1 needs ~7.1 GB VRAM. RTX 3070 8GB has 8.0 GB. With Q4_K_M quantization, expect ~73 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
Tight fit
Decode
73.4 tok/s
TTFT
2636 ms
Safe context
34K
Memory
7.1 GB / 8.0 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 | Tight fit | 73.4 tok/s | 1438 ms | 34K |
| Coding | C | Tight fit | 73.4 tok/s | 2636 ms | 34K |
| Agentic Coding | C | Runs with offload | 73.4 tok/s | 3834 ms | 34K |
| Reasoning | C | Tight fit | 73.4 tok/s | 3115 ms | 34K |
| RAG | C | Runs with offload | 73.4 tok/s | 4793 ms | 34K |
How internlm2 5 7b chat i1 (7B params) fits at each quantization level on RTX 3070 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 | 3.9 GB | Medium | C53 |
Q4_K_M | 4 | 4.3 GB | Medium | C53 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | C52 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
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
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 34%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
RTX 4070 Super 12GBNVIDIA upgradeAdds memory headroom for longer context windows and future model growth.
~$599 MSRP
Yes, RTX 3070 8GB can run internlm2 5 7b chat i1 with a C grade (Tight fit). Expected decode speed: 73.4 tok/s.
internlm2 5 7b chat i1 (7B parameters) requires approximately 7.1 GB of memory with Q4_K_M quantization.
The recommended quantization for internlm2 5 7b chat i1 is Q4_K_M, which balances quality and memory efficiency.
On RTX 3070 8GB, internlm2 5 7b chat i1 achieves approximately 73.4 tokens per second decode speed with a time-to-first-token of 2636ms using Q4_K_M quantization.
For coding workloads, internlm2 5 7b chat i1 on RTX 3070 8GB receives a C grade with 73.4 tok/s and 34K context.
On RTX 3070 8GB, internlm2 5 7b chat i1 can safely use up to 34K 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--internlm2-5-7b-chat-i1-gguf-on-rtx-3070-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: