Can InternLM 20B run on NVIDIA T4 16GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

InternLM 20B needs ~35.2 GB but NVIDIA T4 16GB only has 16.0 GB. Try a smaller quantization or lighter model.

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

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) — 35.2 GB, exceeds 16.0 GB available

35.2 GB required16.0 GB available

220% VRAM needed

19.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.2 tok/s

TTFT

87602 ms

Safe context

Memory

35.2 GB / 16.0 GB

Offload

50%

Memory breakdown

Weights14.4 GB

KV Cache18.3 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsInternLM 20B on NVIDIA T4 16GB
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 · 87.6s 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 35.2 GB, but this setup only exposes 16.0 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	3.7 tok/s	28671 ms	4K
Coding	F	Too heavy	2.2 tok/s	87602 ms	4K
Agentic Coding	F	Too heavy	2.2 tok/s	127421 ms	4K
Reasoning	F	Too heavy	2.2 tok/s	103529 ms	4K
RAG	F	Too heavy	2.2 tok/s	159276 ms	4K

Quantization options

How InternLM 20B (20B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	B59
Q3_K_S	3	9.8 GB	Low	B59
NVFP4	4

Upgrade options

Hardware that runs InternLM 20B well

RTX 5090 32GBBest value

32 GB VRAM (+16)1792 GB/s (+1472)

Makes the model fit on the accelerator instead of staying completely out of reach.44.5 tok/s decode

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

Raises estimated decode speed by about 1923%.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBNVIDIA upgrade

32 GB VRAM (+16)896 GB/s (+576)

Makes the model fit on the accelerator instead of staying completely out of reach.30.6 tok/s decode

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

Raises estimated decode speed by about 1291%.

~$2,499 MSRP

RTX A6000 48GBBudget pick

48 GB VRAM (+32)768 GB/s (+448)

Makes the model fit on the accelerator instead of staying completely out of reach.41.3 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.

~$4,650 MSRP

Frequently asked questions

Can NVIDIA T4 16GB run InternLM 20B?

No, InternLM 20B requires more memory than NVIDIA T4 16GB provides.

How much VRAM does InternLM 20B need?

InternLM 20B (20B parameters) requires approximately 35.2 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 NVIDIA T4 16GB?

On NVIDIA T4 16GB, InternLM 20B achieves approximately 2.2 tokens per second decode speed with a time-to-first-token of 87602ms using Q5_K_M quantization.

Can NVIDIA T4 16GB run InternLM 20B for coding?

For coding workloads, InternLM 20B on NVIDIA T4 16GB receives a F grade with 2.2 tok/s and 4K context.

What context window can InternLM 20B use on NVIDIA T4 16GB?

On NVIDIA T4 16GB, 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 NVIDIA T4 16GB?

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 NVIDIA T4 16GB See all hardware for InternLM 20B

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