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URL: https://willitrunai.com/can-run/internlm-7b-on-rtx-2060-super-8gb

⇱ InternLM 7B on RTX 2060 Super 8GB? No — Alternatives

Can InternLM 7B run on RTX 2060 Super 8GB?

NO — Won't Fit

F0Won't run
○Estimated from fit model

InternLM 7B needs ~14.1 GB but RTX 2060 Super 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity
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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 14.1 GB, exceeds 8.0 GB available
14.1 GB required8.0 GB available
176% VRAM needed

6.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

12.8 tok/s

TTFT

15100 ms

Safe context

4K

Memory

14.1 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights4.3 GB
KV Cache7.8 GB
Runtime1.2 GB
Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsInternLM 7B on RTX 2060 Super 8GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step. Given information: - Speed = 60 mph (miles per hour) - Time = 2.5 hours Formula: Distance = Speed x Time Calculation: Distance = 60 mph x 2.5 h = 150 miles The train travels 150 miles. Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time. Breakdown: - First half: 1.25 hours at 60 mph = 75 miles - Stop: 20 minutes (no distance) - Second half: 1.25 hours at 60 mph = 75 miles Total distance: 150 miles (same) Total time: 2.5 h + 0.33 h = 2.83 hours Average speed: 150 / 2.83 = 53 mph The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 12.8 tok/s decode · 15.1s TTFT (warm) · 32 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 14.1 GB, but this setup only exposes 8.0 GB of usable VRAM.

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

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy26.2 tok/s4035 ms4K
CodingFToo heavy12.8 tok/s15100 ms4K
Agentic CodingFToo heavy9.1 tok/s30851 ms4K
ReasoningFToo heavy12.8 tok/s17845 ms4K
RAGFToo heavy9.1 tok/s38563 ms4K

Quantization options

How InternLM 7B (7B params) fits at each quantization level on RTX 2060 Super 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowA74
Q3_K_S
3
3.4 GB
LowA74
NVFP4
4
3.9 GB
MediumA74
Q4_K_M
4
4.3 GB
MediumA74
Q5_K_MBest for your GPU
5
5.0 GB
HighA73
Q6_K
6
5.7 GB
HighF0
Q8_0
8
7.5 GB
Very HighF0
F16
16
14.3 GB
MaximumF0

Upgrade options

Hardware that runs InternLM 7B well

👁 NVIDIA
RTX 5060 Ti 16GBBudget pick
16 GB VRAM (+8)
A
Makes the model fit on the accelerator instead of staying completely out of reach.65 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$449 MSRP

👁 NVIDIA
RTX 4060 Ti 16GBBest value
16 GB VRAM (+8)
A
Makes the model fit on the accelerator instead of staying completely out of reach.49.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$499 MSRP

👁 NVIDIA
RTX 2000 Ada 16GBNVIDIA upgrade
16 GB VRAM (+8)
A
Makes the model fit on the accelerator instead of staying completely out of reach.51.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$625 MSRP

Frequently asked questions

See all results for RTX 2060 Super 8GBSee all hardware for InternLM 7B