Can InternLM 20B run on RTX 5090 32GB?

BARELY — Tight on Memory

C50Usable

○Estimated from fit model

InternLM 20B needs ~36.8 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q5_K_M quantization, expect ~49 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload

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) — 36.8 GB, 44.5 tok/s, Very compromised (needs ~1.9 GB host RAM)

36.8 GB required32.0 GB available

115% VRAM needed

4.8 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.9 GB host RAM)

Decode

44.5 tok/s

TTFT

4346 ms

Safe context

Memory

36.8 GB / 32.0 GB

Offload

10%

Memory breakdown

Weights14.4 GB

KV Cache18.3 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsInternLM 20B on RTX 5090 32GB
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: 44.5 tok/s decode · 4.3s TTFT (warm) · 111 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Tight fit	85.0 tok/s	1242 ms	8K
Coding	C	Very compromised	48.9 tok/s	3961 ms	8K
Agentic Coding	F	Too heavy	21.4 tok/s	13130 ms	8K
Reasoning	C	Very compromised	48.9 tok/s	4682 ms	8K
RAG	F	Too heavy	21.4 tok/s	16413 ms	8K

Quantization options

How InternLM 20B (20B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.8 GB	Low	C53
Q3_K_S	3	9.8 GB	Low	C54
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
RTX A6000 48GBBudget pick

48 GB VRAM (+16)

Removes host-memory offload, which is usually the single biggest latency and throughput win.41.3 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

~$4,650 MSRP

👁 NVIDIA
RTX PRO 5000 Blackwell 48GBBest value

48 GB VRAM (+16)

Removes host-memory offload, which is usually the single biggest latency and throughput win.80 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 80%.

~$4,999 MSRP

👁 NVIDIA
Quadro RTX 8000 48GBNVIDIA upgrade

48 GB VRAM (+16)

Removes host-memory offload, which is usually the single biggest latency and throughput win.32.8 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

~$5,800 MSRP

Frequently asked questions

See all results for RTX 5090 32GB See all hardware for InternLM 20B

URL: https://willitrunai.com/can-run/internlm-20b-on-rtx-5090-32gb

⇱

Can InternLM 20B run on RTX 5090 32GB?

BARELY — Tight on Memory

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Get started

Hardware that runs InternLM 20B well

Frequently asked questions