Will It Run AI

Can InternLM 20B run on RTX 3060 12GB?

NO — Won't Fit

F0Won't run
Estimated from fit model

InternLM 20B needs ~34.8 GB but RTX 3060 12GB only has 12.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: 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

Q5_K_M (High quality) — 34.8 GB, exceeds 12.0 GB available
34.8 GB required12.0 GB available
290% VRAM needed

22.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.2 tok/s

TTFT

88217 ms

Safe context

4K

Memory

34.8 GB / 12.0 GB

Offload

70%

Memory breakdown

Weights14.4 GB
KV Cache18.3 GB
Runtime0.9 GB
Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsInternLM 20B on RTX 3060 12GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort: def quicksort(arr): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) # Example usage numbers = [3, 6, 8, 10, 1, 2, 1] print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10] The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]: """Sort a list using the quicksort algorithm. Args: arr: List of integers to sort. Returns: A new sorted list. """ if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 2.2 tok/s decode · 88.2s TTFT (warm) · 6 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 34.8 GB, but this setup only exposes 12.0 GB of usable VRAM.

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 heavy2.2 tok/s47641 ms4K
CodingFToo heavy2.2 tok/s88217 ms4K
Agentic CodingFToo heavy2.2 tok/s128315 ms4K
ReasoningFToo heavy2.2 tok/s104256 ms4K
RAGFToo heavy2.2 tok/s160394 ms4K

Quantization options

How InternLM 20B (20B params) fits at each quantization level on RTX 3060 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_KBest for your GPU
2
7.8 GB
LowB60
Q3_K_S
3
9.8 GB
LowF0
NVFP4
4
11.2 GB
MediumF0
Q4_K_M
4
12.2 GB
MediumF0
Q5_K_M
5
14.4 GB
HighF0
Q6_K
6
16.4 GB
HighF0
Q8_0
8
21.4 GB
Very HighF0
F16
16
41.0 GB
MaximumF0

Opções de upgrade

Hardware que roda bem InternLM 20B

NVIDIARTX 5090 32GBMelhor custo-benefício
32 GB VRAM (+20)1792 GB/s (+1432)
C
Makes the model fit on the accelerator instead of staying completely out of reach.44.5 tok/s decodificação

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

Raises estimated decode speed by about 1923%.

~$1,999 MSRP

NVIDIARTX PRO 4500 Blackwell 32GBUpgrade NVIDIA
32 GB VRAM (+20)896 GB/s (+536)
C
Makes the model fit on the accelerator instead of staying completely out of reach.30.6 tok/s decodificação

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

Raises estimated decode speed by about 1291%.

~$2,499 MSRP

NVIDIARTX A6000 48GBOpção econômica
48 GB VRAM (+36)768 GB/s (+408)
B
Makes the model fit on the accelerator instead of staying completely out of reach.41.3 tok/s decodificação

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.

~$4,650 MSRP

Frequently asked questions

Can RTX 3060 12GB run InternLM 20B?

No, InternLM 20B requires more memory than RTX 3060 12GB provides.

How much VRAM does InternLM 20B need?

InternLM 20B (20B parameters) requires approximately 34.8 GB of memory with Q5_K_M quantization.

What is the best quantization for InternLM 20B?

The recommended quantization for InternLM 20B is Q5_K_M, which balances quality and memory efficiency.

What speed will InternLM 20B run at on RTX 3060 12GB?

On RTX 3060 12GB, InternLM 20B achieves approximately 2.2 tokens per second decode speed with a time-to-first-token of 88217ms using Q5_K_M quantization.

Can RTX 3060 12GB run InternLM 20B for coding?

For coding workloads, InternLM 20B on RTX 3060 12GB receives a F grade with 2.2 tok/s and 4K context.

What context window can InternLM 20B use on RTX 3060 12GB?

On RTX 3060 12GB, InternLM 20B can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if InternLM 20B feels slow on RTX 3060 12GB?

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.

See all results for RTX 3060 12GBSee all hardware for InternLM 20B
Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/internlm-20b-on-rtx-3060-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: