Can Gemma 2 27B run on RX 9070 XT 16GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Gemma 2 27B needs ~30.2 GB but RX 9070 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

4.1 tok/s

TTFT

46751 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 2 27B on RX 9070 XT 16GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 4.1 tok/s decode · 46.8s TTFT (warm) · 10 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	6.3 tok/s	16761 ms	4K
Coding	F	Too heavy	4.1 tok/s	46751 ms	4K
Agentic Coding	F	Too heavy	3.0 tok/s	94863 ms	4K
Reasoning	F	Too heavy	4.1 tok/s	55251 ms	4K
RAG	F	Too heavy	3.0 tok/s	118579 ms	4K

Quantization options

How Gemma 2 27B (27B params) fits at each quantization level on RX 9070 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	10.5 GB	Low	A70
Q3_K_S	3	13.2 GB	Low	F0

Upgrade options

Hardware that runs Gemma 2 27B well

Radeon AI PRO R9700 32GBBudget pick

32 GB VRAM (+16)

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 A100 40GBBiggest leap

40 GB VRAM (+24)1555 GB/s (+915)

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

Can RX 9070 XT 16GB run Gemma 2 27B?

No, Gemma 2 27B requires more memory than RX 9070 XT 16GB provides.

How much VRAM does Gemma 2 27B need?

Gemma 2 27B (27B parameters) requires approximately 30.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 2 27B?

The recommended quantization for Gemma 2 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 2 27B run at on RX 9070 XT 16GB?

On RX 9070 XT 16GB, Gemma 2 27B achieves approximately 4.1 tokens per second decode speed with a time-to-first-token of 46751ms using Q4_K_M quantization.

Can RX 9070 XT 16GB run Gemma 2 27B for coding?

For coding workloads, Gemma 2 27B on RX 9070 XT 16GB receives a F grade with 4.1 tok/s and 4K context.

What context window can Gemma 2 27B use on RX 9070 XT 16GB?

On RX 9070 XT 16GB, Gemma 2 27B can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 2 27B feels slow on RX 9070 XT 16GB?

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 9070 XT 16GB See all hardware for Gemma 2 27B

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