Can Gemma 3 12B run on Radeon Pro W6800 32GB?

YES — Runs Great

A80Great
Estimated from fit model

Gemma 3 12B needs ~16.6 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~41 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
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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) 16.6 GB, 41.1 tok/s, Runs well
16.6 GB required32.0 GB available
52% VRAM used

Fit status

Runs well

Decode

41.1 tok/s

TTFT

4707 ms

Safe context

66K

Memory

16.6 GB / 32.0 GB

Memory breakdown

Weights7.3 GB
KV Cache4.9 GB
Runtime1.2 GB
Headroom3.2 GB

See how fast it feels

See how fast it feelsGemma 3 12B on Radeon Pro W6800 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: 41.1 tok/s decode · 4.7s TTFT (warm) · 103 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatARuns well41.1 tok/s2568 ms66K
CodingARuns well41.1 tok/s4707 ms66K
Agentic CodingARuns well41.1 tok/s6847 ms66K
ReasoningARuns well41.1 tok/s5563 ms66K
RAGARuns well41.1 tok/s8559 ms66K

Inference speed

Gemma 3 12B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 3 12B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~168 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M168.0Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M109.9Fits
RX 7900 XTX 24GB
24 GBQ4_K_M99.1Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M94.0Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M87.6Tight
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M60.5Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M50.4Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M47.8Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M43.4Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M34.4Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M32.9Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M31.6Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M26.5Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M21.5Heavy offload
NVIDIARTX 3060 12GB
12 GBQ4_K_M13.1Heavy offload
NVIDIARTX 4060 8GB
8 GBQ4_K_M6.4Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Gemma 3 12B (12B params) fits at each quantization level on Radeon Pro W6800 32GB (32.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
4.7 GB
LowA73
Q3_K_S
3
5.9 GB
LowA74
NVFP4
4
6.7 GB
MediumA74
Q4_K_M
4
7.3 GB
MediumA74
Q5_K_M
5
8.6 GB
HighA75
Q6_K
6
9.8 GB
HighA75
Q8_0
8
12.8 GB
Very HighA77
F16Best for your GPU
16
24.6 GB
MaximumA78

Get started

Copy-paste commands to run Gemma 3 12B on your machine.

Run

ollama run gemma3:12b

Your hardware

More models your Radeon Pro W6800 32GB can run

ModelParamsGradeDecodeCapabilities
AlibabaQwen3-Coder 30B A3B Instruct30.5BS43.4 tok/s
AlibabaQwen 3.5 27B27BS18.8 tok/s
AlibabaQwen 3.6 27B27BS18.9 tok/s
AlibabaQwen 3.6 35B A3B35BS36.4 tok/s
AlibabaQwen3-VL 30B A3B Instruct30BS44.8 tok/s

Frequently asked questions

Can Radeon Pro W6800 32GB run Gemma 3 12B?

Yes, Radeon Pro W6800 32GB can run Gemma 3 12B with a A grade (Runs well). Expected decode speed: 41.1 tok/s.

How much VRAM does Gemma 3 12B need?

Gemma 3 12B (12B parameters) requires approximately 16.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 3 12B?

The recommended quantization for Gemma 3 12B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 3 12B run at on Radeon Pro W6800 32GB?

On Radeon Pro W6800 32GB, Gemma 3 12B achieves approximately 41.1 tokens per second decode speed with a time-to-first-token of 4707ms using Q4_K_M quantization.

Can Radeon Pro W6800 32GB run Gemma 3 12B for coding?

For coding workloads, Gemma 3 12B on Radeon Pro W6800 32GB receives a A grade with 41.1 tok/s and 66K context.

What context window can Gemma 3 12B use on Radeon Pro W6800 32GB?

On Radeon Pro W6800 32GB, Gemma 3 12B can safely use up to 66K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Radeon Pro W6800 32GBSee all hardware for Gemma 3 12B
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