Can InternLM 20B run on NVIDIA A100 40GB?

YES — Tight Fit

B62Good

Estimated from fit model

InternLM 20B needs ~37.6 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q5_K_M quantization, expect ~93 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: HighStack: StandardBottleneck: 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.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 37.6 GB, 92.5 tok/s, Tight fit

37.6 GB required40.0 GB available

94% VRAM used

Fit status

Tight fit

Decode

92.5 tok/s

TTFT

2092 ms

Safe context

Memory

37.6 GB / 40.0 GB

Memory breakdown

Weights14.4 GB

KV Cache18.3 GB

Runtime0.9 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsInternLM 20B on NVIDIA A100 40GB
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: 92.5 tok/s decode · 2.1s TTFT (warm) · 231 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.

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	B	Runs well	92.5 tok/s	1141 ms	8K
Coding	B	Tight fit	92.5 tok/s	2092 ms	8K
Agentic Coding	F	Too heavy	34.3 tok/s	8216 ms	8K
Reasoning	B	Tight fit	92.5 tok/s	2473 ms	8K
RAG	F	Too heavy	34.3 tok/s	10269 ms	8K

Quantization options

How InternLM 20B (20B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	C51
Q3_K_S	3	9.8 GB	Low	C52
NVFP4	4	11.2 GB	Medium	C52
Q4_K_M	4	12.2 GB	Medium	C53
Q5_K_M	5	14.4 GB	High	C54
Q6_K	6	16.4 GB	High	C54
Q8_0Best for your GPU	8	21.4 GB	Very High	B57
F16	16	41.0 GB	Maximum	F0

Get started

Copy-paste commands to run InternLM 20B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "internlm/internlm2_5-20b-chat" \
  --hf-file "internlm2_5-20b-chat-Q5_K_M.gguf" \
  -c 4096 -ngl 99

Opções de upgrade

Hardware que roda bem InternLM 20B

RTX A6000 48GBOpção econômica

48 GB VRAM (+8)

This setup is broadly balanced for this model.41.3 tok/s decodificação

~$4,650 MSRP

RTX PRO 5000 Blackwell 48GBMelhor custo-benefício

48 GB VRAM (+8)

This setup is broadly balanced for this model.80 tok/s decodificação

~$4,999 MSRP

NVIDIA L40 48GBUpgrade NVIDIA

48 GB VRAM (+8)

This setup is broadly balanced for this model.38.7 tok/s decodificação

~$5,500 MSRP

Frequently asked questions

Can NVIDIA A100 40GB run InternLM 20B?

Yes, NVIDIA A100 40GB can run InternLM 20B with a B grade (Tight fit). Expected decode speed: 92.5 tok/s.

How much VRAM does InternLM 20B need?

InternLM 20B (20B parameters) requires approximately 37.6 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 A100 40GB?

On NVIDIA A100 40GB, InternLM 20B achieves approximately 92.5 tokens per second decode speed with a time-to-first-token of 2092ms using Q5_K_M quantization.

Can NVIDIA A100 40GB run InternLM 20B for coding?

For coding workloads, InternLM 20B on NVIDIA A100 40GB receives a B grade with 92.5 tok/s and 8K context.

What context window can InternLM 20B use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, InternLM 20B can safely use up to 8K 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 A100 40GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for NVIDIA A100 40GB See all hardware for InternLM 20B

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