Can InternLM 20B run on RTX A5000 24GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

InternLM 20B needs ~36.0 GB but RTX A5000 24GB only has 24.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: MediumStack: 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) — 36.0 GB, exceeds 24.0 GB available

36.0 GB required24.0 GB available

150% VRAM needed

12.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

12.2 tok/s

TTFT

15925 ms

Safe context

Memory

36.0 GB / 24.0 GB

Offload

30%

Memory breakdown

Weights14.4 GB

KV Cache18.3 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsInternLM 20B on RTX A5000 24GB
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: 12.2 tok/s decode · 15.9s TTFT (warm) · 30 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 36.0 GB, but this setup only exposes 24.0 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Very compromised (needs ~1.5 GB host RAM)	22.5 tok/s	4685 ms	6K
Coding	F	Too heavy	12.2 tok/s	15925 ms	6K
Agentic Coding	F	Too heavy	5.7 tok/s	49300 ms	6K
Reasoning	F	Too heavy	12.2 tok/s	18821 ms	6K
RAG	F	Too heavy	5.7 tok/s	61625 ms	6K

Inference speed

InternLM 20B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for InternLM 20B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~45 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q5_K_M	44.5	Heavy offload
Mac Studio M3 Ultra 256GB	256 GB	Q5_K_M	39.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q5_K_M	32.9	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q5_K_M	31.2	Fits
MacBook Pro M4 Max 128GB	128 GB	Q5_K_M	30.7	Fits
MacBook Pro M4 Max 64GB	64 GB	Q5_K_M	30.7	Tight
MacBook Pro M3 Max 64GB	64 GB	Q5_K_M	17.0	Tight
MacBook Pro M4 Pro 48GB	48 GB	Q5_K_M	16.1	Heavy offload
RX 7900 XTX 24GB	24 GB	Q5_K_M	15.6	Too big
MacBook Pro M1 Max 64GB	64 GB	Q5_K_M	15.6	Tight
RTX 4090 24GB	24 GB	Q5_K_M	14.0	Too big
RTX 3090 24GB	24 GB	Q5_K_M	12.9	Too big
RTX 4080 Super 16GB	16 GB	Q5_K_M	5.3	Too big
RTX 4070 12GB	12 GB	Q5_K_M	3.3	Too big
RTX 3060 12GB	12 GB	Q5_K_M	2.2	Too big
RTX 4060 8GB	8 GB	Q5_K_M	2.0	Too big

Estimates for single-stream decoding at Q5_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How InternLM 20B (20B params) fits at each quantization level on RTX A5000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	B55
Q3_K_S	3	9.8 GB	Low	B57
NVFP4	4	11.2 GB	Medium	B58
Q4_K_M	4	12.2 GB	Medium	B58
Q5_K_M	5	14.4 GB	High	B58
Q6_KBest for your GPU	6	16.4 GB	High	B58
Q8_0	8	21.4 GB	Very High	F0
F16	16	41.0 GB	Maximum	F0

Upgrade-Optionen

Hardware, die InternLM 20B gut ausführt

RTX 5090 32GBBestes Preis-Leistungs-Verhältnis

32 GB VRAM (+8)1792 GB/s (+1024)

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

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

Raises estimated decode speed by about 265%.

ca. $1,999 MSRP

RTX PRO 4500 Blackwell 32GBNVIDIA-Upgrade

32 GB VRAM (+8)896 GB/s (+128)

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

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

Raises estimated decode speed by about 151%.

ca. $2,499 MSRP

RTX A6000 48GBGünstige Wahl

48 GB VRAM (+24)

Makes the model fit on the accelerator instead of staying completely out of reach.41.3 tok/s Dekodierung

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.

ca. $4,650 MSRP

Frequently asked questions

Can RTX A5000 24GB run InternLM 20B?

No, InternLM 20B requires more memory than RTX A5000 24GB provides.

How much VRAM does InternLM 20B need?

InternLM 20B (20B parameters) requires approximately 36.0 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 RTX A5000 24GB?

On RTX A5000 24GB, InternLM 20B achieves approximately 12.2 tokens per second decode speed with a time-to-first-token of 15925ms using Q5_K_M quantization.

Can RTX A5000 24GB run InternLM 20B for coding?

For coding workloads, InternLM 20B on RTX A5000 24GB receives a F grade with 12.2 tok/s and 6K context.

What context window can InternLM 20B use on RTX A5000 24GB?

On RTX A5000 24GB, InternLM 20B can safely use up to 6K 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 A5000 24GB?

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 A5000 24GB See all hardware for InternLM 20B

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