VOOZH about

URL: https://willitrunai.com/can-run/kimi-linear-48b-a3b-on-rx-7900-xt-20gb

⇱ Kimi Linear 48B A3B on RX 7900 XT 20GB? No — Alternatives

Can Kimi Linear 48B A3B run on RX 7900 XT 20GB?

NO — Won't Fit

F0Won't run
○Estimated from fit model

Kimi Linear 48B A3B needs ~34.6 GB but RX 7900 XT 20GB only has 20.0 GB. Try a smaller quantization or lighter model.

Runtime: vLLMCapacity: No fitBandwidth: HighStack: OptimizedBottleneck: Memory capacity
Share:

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

Q4_K_M (Medium quality) — 34.6 GB, exceeds 20.0 GB available
34.6 GB required20.0 GB available
173% VRAM needed

14.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.1 tok/s

TTFT

62425 ms

Safe context

4K

Memory

34.6 GB / 20.0 GB

Offload

40%

Memory breakdown

Weights29.3 GB
KV Cache0.9 GB
Runtime2.4 GB
Headroom2.0 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsKimi Linear 48B A3B on RX 7900 XT 20GB
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: 3.1 tok/s decode · 62.4s TTFT (warm) · 8 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 34.6 GB, but this setup only exposes 20.0 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
ChatFToo heavy3.2 tok/s33097 ms4K
CodingFToo heavy3.1 tok/s62425 ms4K
Agentic CodingFToo heavy2.9 tok/s95995 ms4K
ReasoningFToo heavy3.1 tok/s73774 ms4K
RAGFToo heavy2.9 tok/s119993 ms4K

Quantization options

How Kimi Linear 48B A3B (48B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
18.7 GB
LowF0
Q3_K_S
3
23.5 GB
LowF0
NVFP4
4
26.9 GB
MediumF0
Q4_K_M
4
29.3 GB
MediumF0
Q5_K_M
5
34.6 GB
HighF0
Q6_K
6
39.4 GB
HighF0
Q8_0
8
51.4 GB
Very HighF0
F16
16
98.4 GB
MaximumF0

Upgrade options

Hardware that runs Kimi Linear 48B A3B well

Radeon Pro W7900 48GBBudget pick
48 GB VRAM (+28)864 GB/s (+64)
A
Makes the model fit on the accelerator instead of staying completely out of reach.13.9 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

Radeon PRO W7900 DS 48GBBest value
48 GB VRAM (+28)864 GB/s (+64)
A
Makes the model fit on the accelerator instead of staying completely out of reach.13.9 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

AMD Instinct MI350X 288GBAMD upgrade
288 GB VRAM (+268)8000 GB/s (+7200)
A
Makes the model fit on the accelerator instead of staying completely out of reach.159.6 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.

~$8,000 MSRP

Frequently asked questions

See all results for RX 7900 XT 20GBSee all hardware for Kimi Linear 48B A3B