Can Gemma 3 27B run on RX 6950 XT 16GB?

NO — Won't Fit

F0Won't run

○Estimated from fit model

Gemma 3 27B needs ~30.2 GB but RX 6950 XT 16GB only has 16.0 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

Q4_K_M (Medium quality) — 30.2 GB, exceeds 16.0 GB available

30.2 GB required16.0 GB available

189% VRAM needed

14.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.2 tok/s

TTFT

60907 ms

Safe context

Memory

30.2 GB / 16.0 GB

Offload

50%

Memory breakdown

Weights16.5 GB

KV Cache11.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 3 27B on RX 6950 XT 16GB
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: 3.2 tok/s decode · 60.9s 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 30.2 GB, but this setup only exposes 16.0 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	F	Too heavy	4.9 tok/s	21546 ms	4K
Coding	F	Too heavy	3.2 tok/s	60907 ms	4K
Agentic Coding	F	Too heavy	2.4 tok/s	116307 ms	4K
Reasoning	F	Too heavy	3.2 tok/s	71981 ms	4K
RAG	F	Too heavy	2.4 tok/s	145384 ms	4K

Quantization options

How Gemma 3 27B (27B params) fits at each quantization level on RX 6950 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	10.5 GB	Low	A83
Q3_K_S	3	13.2 GB	Low	F0
NVFP4	4	15.1 GB	Medium	F0
Q4_K_M	4	16.5 GB	Medium	F0
Q5_K_M	5	19.4 GB	High	F0
Q6_K	6	22.1 GB	High	F0
Q8_0	8	28.9 GB	Very High	F0
F16	16	55.4 GB	Maximum	F0

Upgrade options

Hardware that runs Gemma 3 27B well

Radeon AI PRO R9700 32GBBudget pick

32 GB VRAM (+16)640 GB/s (+64)

Makes the model fit on the accelerator instead of staying completely out of reach.18.2 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.

~$1,899 MSRP

Radeon Pro W6800 32GBBest value

32 GB VRAM (+16)

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.

~$2,249 MSRP

Radeon Pro W7800 32GBAMD upgrade

32 GB VRAM (+16)

Makes the model fit on the accelerator instead of staying completely out of reach.16.4 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.

~$2,499 MSRP

👁 NVIDIA
NVIDIA A100 40GBBiggest leap

40 GB VRAM (+24)1555 GB/s (+979)

Makes the model fit on the accelerator instead of staying completely out of reach.51.7 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.

~$10,000 MSRP

Frequently asked questions

See all results for RX 6950 XT 16GB See all hardware for Gemma 3 27B

URL: https://willitrunai.com/can-run/gemma-3-27b-on-rx-6950-xt-16gb

⇱ Gemma 3 27B on RX 6950 XT 16GB? No — Alternatives

Can Gemma 3 27B run on RX 6950 XT 16GB?

NO — Won't Fit

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

Hardware that runs Gemma 3 27B well

Frequently asked questions