Will It Run AI

Can InternLM 20B run on NVIDIA A10 24GB?

NO — Won't Fit

F0Won't run
Estimated from fit model

InternLM 20B needs ~36.0 GB but NVIDIA A10 24GB only has 24.0 GB. Try a smaller quantization or lighter model.

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

10.6 tok/s

TTFT

18294 ms

Safe context

6K

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 NVIDIA A10 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: 10.6 tok/s decode · 18.3s TTFT (warm) · 27 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

WorkloadGradeFitDecodeTTFTContext
ChatCVery compromised (needs ~1.5 GB host RAM)19.6 tok/s5381 ms6K
CodingFToo heavy10.6 tok/s18294 ms6K
Agentic CodingFToo heavy5.0 tok/s56631 ms6K
ReasoningFToo heavy10.6 tok/s21620 ms6K
RAGFToo heavy5.0 tok/s70789 ms6K

Quantization options

How InternLM 20B (20B params) fits at each quantization level on NVIDIA A10 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
7.8 GB
LowB55
Q3_K_S
3
9.8 GB
LowB57
NVFP4
4
11.2 GB
MediumB58
Q4_K_M
4
12.2 GB
MediumB58
Q5_K_M
5
14.4 GB
HighB58
Q6_KBest for your GPU
6
16.4 GB
HighB58
Q8_0
8
21.4 GB
Very HighF0
F16
16
41.0 GB
MaximumF0

升级选项

能流畅运行 InternLM 20B 的硬件

NVIDIARTX 5090 32GB最佳性价比
32 GB VRAM (+8)1792 GB/s (+1192)
C
Makes the model fit on the accelerator instead of staying completely out of reach.44.5 tok/s 解码

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

Raises estimated decode speed by about 320%.

~$1,999 MSRP

NVIDIARTX PRO 4500 Blackwell 32GBNVIDIA 升级
32 GB VRAM (+8)896 GB/s (+296)
C
Makes the model fit on the accelerator instead of staying completely out of reach.30.6 tok/s 解码

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

Raises estimated decode speed by about 189%.

~$2,499 MSRP

NVIDIARTX A6000 48GB经济之选
48 GB VRAM (+24)768 GB/s (+168)
B
Makes the model fit on the accelerator instead of staying completely out of reach.41.3 tok/s 解码

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 A10 24GB run InternLM 20B?

No, InternLM 20B requires more memory than NVIDIA A10 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 NVIDIA A10 24GB?

On NVIDIA A10 24GB, InternLM 20B achieves approximately 10.6 tokens per second decode speed with a time-to-first-token of 18294ms using Q5_K_M quantization.

Can NVIDIA A10 24GB run InternLM 20B for coding?

For coding workloads, InternLM 20B on NVIDIA A10 24GB receives a F grade with 10.6 tok/s and 6K context.

What context window can InternLM 20B use on NVIDIA A10 24GB?

On NVIDIA A10 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 NVIDIA A10 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 NVIDIA A10 24GBSee all hardware for InternLM 20B
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