Can InternLM 20B run on RTX A6000 48GB?

YES — Runs Great

B62Good

Estimated from fit model

InternLM 20B needs ~38.4 GB VRAM. RTX A6000 48GB has 48.0 GB. With Q5_K_M quantization, expect ~41 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) — 38.4 GB, 41.3 tok/s, Runs well

38.4 GB required48.0 GB available

80% VRAM used

Fit status

Runs well

Decode

41.3 tok/s

TTFT

4683 ms

Safe context

Memory

38.4 GB / 48.0 GB

Memory breakdown

Weights14.4 GB

KV Cache18.3 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsInternLM 20B on RTX A6000 48GB
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: 41.3 tok/s decode · 4.7s TTFT (warm) · 103 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	41.3 tok/s	2554 ms	8K
Coding	B	Runs well	41.3 tok/s	4683 ms	8K
Agentic Coding	C	Very compromised (needs ~2.2 GB host RAM)	21.8 tok/s	12906 ms	8K
Reasoning	B	Runs well	41.3 tok/s	5534 ms	8K
RAG	C	Very compromised (needs ~2.2 GB host RAM)	21.8 tok/s	16132 ms	8K

Quantization options

How InternLM 20B (20B params) fits at each quantization level on RTX A6000 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	C50
Q3_K_S	3	9.8 GB	Low	C51
NVFP4	4	11.2 GB	Medium	C51
Q4_K_M	4	12.2 GB	Medium	C51
Q5_K_M	5	14.4 GB	High	C52
Q6_K	6	16.4 GB	High	C53
Q8_0	8	21.4 GB	Very High	C54
F16Best for your GPU	16	41.0 GB	Maximum	B56

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-Optionen

Hardware, die InternLM 20B gut ausführt

AMD Instinct MI210 64GBGünstige Wahl

64 GB VRAM (+16)1638 GB/s (+870)

Raises estimated decode speed by about 91%.78.9 tok/s Dekodierung

Raises estimated decode speed by about 91%.

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

ca. $10,000 MSRP

Frequently asked questions

Can RTX A6000 48GB run InternLM 20B?

Yes, RTX A6000 48GB can run InternLM 20B with a B grade (Runs well). Expected decode speed: 41.3 tok/s.

How much VRAM does InternLM 20B need?

InternLM 20B (20B parameters) requires approximately 38.4 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 A6000 48GB?

On RTX A6000 48GB, InternLM 20B achieves approximately 41.3 tokens per second decode speed with a time-to-first-token of 4683ms using Q5_K_M quantization.

Can RTX A6000 48GB run InternLM 20B for coding?

For coding workloads, InternLM 20B on RTX A6000 48GB receives a B grade with 41.3 tok/s and 8K context.

What context window can InternLM 20B use on RTX A6000 48GB?

On RTX A6000 48GB, 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 RTX A6000 48GB See all hardware for InternLM 20B

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