Can InternLM 20B run on NVIDIA A16 64GB?

YES — Runs Great

B60Good

Estimated from fit model

InternLM 20B needs ~40.0 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q5_K_M quantization, expect ~33 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 40.0 GB, 33.2 tok/s, Runs well

40.0 GB required64.0 GB available

63% VRAM used

Fit status

Runs well

Decode

33.2 tok/s

TTFT

5840 ms

Safe context

Memory

40.0 GB / 64.0 GB

Memory breakdown

Weights14.4 GB

KV Cache18.3 GB

Runtime0.9 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsInternLM 20B on NVIDIA A16 64GB
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: 33.2 tok/s decode · 5.8s TTFT (warm) · 83 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	33.2 tok/s	3186 ms	8K
Coding	B	Runs well	33.1 tok/s	5840 ms	8K
Agentic Coding	B	Tight fit	33.2 tok/s	8495 ms	8K
Reasoning	B	Runs well	33.2 tok/s	6902 ms	8K
RAG	B	Tight fit	33.2 tok/s	10618 ms	8K

Quantization options

How InternLM 20B (20B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	C49
Q3_K_S	3	9.8 GB	Low	C49
NVFP4	4

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

Upgrade options

Hardware that runs InternLM 20B well

NVIDIA A100 80GBBudget pick

80 GB VRAM (+16)2039 GB/s (+1439)

Raises estimated decode speed by about 265%.121.3 tok/s decode

Raises estimated decode speed by about 265%.

Adds memory headroom for longer context windows and future model growth.

~$15,000 MSRP

NVIDIA A800 80GBBest value

80 GB VRAM (+16)1935 GB/s (+1335)

Raises estimated decode speed by about 222%.106.9 tok/s decode

Raises estimated decode speed by about 222%.

Adds memory headroom for longer context windows and future model growth.

~$15,000 MSRP

NVIDIA H800 80GBNVIDIA upgrade

80 GB VRAM (+16)3000 GB/s (+2400)

Raises estimated decode speed by about 418%.172.1 tok/s decode

Raises estimated decode speed by about 418%.

Adds memory headroom for longer context windows and future model growth.

~$30,000 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run InternLM 20B?

Yes, NVIDIA A16 64GB can run InternLM 20B with a B grade (Runs well). Expected decode speed: 33.1 tok/s.

How much VRAM does InternLM 20B need?

InternLM 20B (20B parameters) requires approximately 40.0 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 A16 64GB?

On NVIDIA A16 64GB, InternLM 20B achieves approximately 33.1 tokens per second decode speed with a time-to-first-token of 5840ms using Q5_K_M quantization.

Can NVIDIA A16 64GB run InternLM 20B for coding?

For coding workloads, InternLM 20B on NVIDIA A16 64GB receives a B grade with 33.1 tok/s and 8K context.

What context window can InternLM 20B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, 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.

See all results for NVIDIA A16 64GB See all hardware for InternLM 20B

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