12 GB VRAM (+4)
B
Adds memory headroom for longer context windows and future model growth.50.2 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can Starling LM 7B run on RTX 2070 Super 8GB?
YES — With Offload
C53Usable
○Estimated from fit model
Starling LM 7B needs ~7.9 GB VRAM. RTX 2070 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~69 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: 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) — 7.9 GB, 68.8 tok/s, Runs with offload
7.9 GB required8.0 GB available
99% VRAM used
Fit status
Runs with offload
Decode
68.8 tok/s
TTFT
2814 ms
Safe context
8K
Memory
7.9 GB / 8.0 GB
Memory breakdown
Weights4.3 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsStarling LM 7B 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: 68.8 tok/s decode · 2.8s TTFT (warm) · 172 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 | 68.8 tok/s | 1535 ms | 8K |
| Coding | C | Runs with offload | 68.8 tok/s | 2814 ms | 8K |
| Agentic Coding | F | Too heavy | 31.6 tok/s | 8913 ms | 8K |
| Reasoning | C | Runs with offload | 68.8 tok/s | 3326 ms | 8K |
| RAG | F | Too heavy | 31.6 tok/s | 11141 ms | 8K |
Quantization options
How Starling LM 7B (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 | C54 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Starling LM 7B on your machine.
Run
ollama run starling-lmUpgrade options
Hardware that runs Starling LM 7B well
12 GB VRAM (+4)672 GB/s (+224)
B
Raises estimated decode speed by about 55%.106.6 tok/s decode
Raises estimated decode speed by about 55%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
12 GB VRAM (+4)504 GB/s (+56)
B
Raises estimated decode speed by about 31%.89.9 tok/s decode
Raises estimated decode speed by about 31%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP