Can InternLM 20B run on RX 7900 XTX 24GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

InternLM 20B needs ~36.0 GB but RX 7900 XTX 24GB only has 24.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: HighStack: 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

15.6 tok/s

TTFT

12386 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 RX 7900 XTX 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: 15.6 tok/s decode · 12.4s TTFT (warm) · 39 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)	29.0 tok/s	3644 ms	6K
Coding	F	Too heavy	15.6 tok/s	12386 ms	6K
Agentic Coding	F	Too heavy	7.3 tok/s	38344 ms	6K
Reasoning	F	Too heavy	15.6 tok/s	14638 ms	6K
RAG	F	Too heavy	7.3 tok/s	47930 ms	6K

Quantization options

How InternLM 20B (20B params) fits at each quantization level on RX 7900 XTX 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

Upgrade options

Hardware that runs InternLM 20B well

Radeon AI PRO R9700 32GBBest value

32 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.15.4 tok/s decode

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

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

~$1,899 MSRP

Radeon Pro W6800 32GBAMD upgrade

32 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.11.3 tok/s decode

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

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

~$2,249 MSRP

Radeon Pro W7900 48GBBudget pick

48 GB VRAM (+24)

Makes the model fit on the accelerator instead of staying completely out of reach.36.1 tok/s decode

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.

~$3,999 MSRP

Frequently asked questions

Can RX 7900 XTX 24GB run InternLM 20B?

No, InternLM 20B requires more memory than RX 7900 XTX 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 RX 7900 XTX 24GB?

On RX 7900 XTX 24GB, InternLM 20B achieves approximately 15.6 tokens per second decode speed with a time-to-first-token of 12386ms using Q5_K_M quantization.

Can RX 7900 XTX 24GB run InternLM 20B for coding?

For coding workloads, InternLM 20B on RX 7900 XTX 24GB receives a F grade with 15.6 tok/s and 6K context.

What context window can InternLM 20B use on RX 7900 XTX 24GB?

On RX 7900 XTX 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 RX 7900 XTX 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 RX 7900 XTX 24GB See all hardware for InternLM 20B

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