RTX 3060 12GBOpción económica12 GB VRAM (+4)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.55.6 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$329 MSRP
Can internlm2 5 7b chat i1 run on RTX 2070 Super 8GB?
YES — Tight Fit
C52Usable
Estimated from fit model
internlm2 5 7b chat i1 needs ~7.1 GB VRAM. RTX 2070 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~64 tok/s.
Runtime: OllamaCapacity: TightBandwidth: 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) — 7.1 GB, 64.0 tok/s, Tight fit
7.1 GB required8.0 GB available
89% VRAM used
Fit status
Tight fit
Decode
64.0 tok/s
TTFT
3025 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 2070 Super 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: 64.0 tok/s decode · 3.0s TTFT (warm) · 160 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
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
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 64.0 tok/s | 1650 ms | 34K |
| Coding | C | Tight fit | 64.0 tok/s | 3025 ms | 34K |
| Agentic Coding | C | Runs with offload | 64.0 tok/s | 4400 ms | 34K |
| Reasoning | C | Tight fit | 64.0 tok/s | 3575 ms | 34K |
| RAG | C | Runs with offload | 64.0 tok/s | 5500 ms | 34K |
Quantization options
How internlm2 5 7b chat i1 (7B params) fits at each quantization level on RTX 2070 Super 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 |
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 startOpciones de mejora
Hardware que ejecuta bien internlm2 5 7b chat i1
RTX 3060 12GBOpción económicaRTX 5070 12GBMejor relación calidad-precio12 GB VRAM (+4)672 GB/s (+224)
C
Sube la velocidad estimada de decodificación alrededor de un 53%.98 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 53%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$549 MSRP
RTX 4070 Super 12GBMejora NVIDIA12 GB VRAM (+4)504 GB/s (+56)
C
Sube la velocidad estimada de decodificación alrededor de un 42%.90.9 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 42%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$599 MSRP
Frequently asked questions
Can RTX 2070 Super 8GB run internlm2 5 7b chat i1?
Yes, RTX 2070 Super 8GB can run internlm2 5 7b chat i1 with a C grade (Tight fit). Expected decode speed: 64.0 tok/s.
How much VRAM does internlm2 5 7b chat i1 need?
internlm2 5 7b chat i1 (7B parameters) requires approximately 7.1 GB of memory with Q4_K_M quantization.
What is the best quantization for internlm2 5 7b chat i1?
The recommended quantization for internlm2 5 7b chat i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will internlm2 5 7b chat i1 run at on RTX 2070 Super 8GB?
On RTX 2070 Super 8GB, internlm2 5 7b chat i1 achieves approximately 64.0 tokens per second decode speed with a time-to-first-token of 3025ms using Q4_K_M quantization.
Can RTX 2070 Super 8GB run internlm2 5 7b chat i1 for coding?
For coding workloads, internlm2 5 7b chat i1 on RTX 2070 Super 8GB receives a C grade with 64.0 tok/s and 34K context.
What context window can internlm2 5 7b chat i1 use on RTX 2070 Super 8GB?
On RTX 2070 Super 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.
Embed this result▼
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-2070-super-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: