Can gemma 3 12b it run on AMD Instinct MI350X 288GB?

YES — Runs Great

C45Usable

Estimated from fit model

gemma 3 12b it needs ~38.4 GB VRAM. AMD Instinct MI350X 288GB has 288.0 GB. With Q4_K_M quantization, expect ~168 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced

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) — 38.4 GB, 168.0 tok/s, Runs well

38.4 GB required288.0 GB available

13% VRAM used

Fit status

Runs well

Decode

168.0 tok/s

TTFT

1152 ms

Safe context

2.9M

Memory

38.4 GB / 288.0 GB

Memory breakdown

Weights7.3 GB

KV Cache1.4 GB

Runtime0.9 GB

Headroom28.8 GB

See how fast it feels

See how fast it feelsgemma 3 12b it on AMD Instinct MI350X 288GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 168.0 tok/s decode · 1.2s TTFT (warm) · 420 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	168.0 tok/s	629 ms	2.9M
Coding	C	Runs well	168.0 tok/s	1152 ms	2.9M
Agentic Coding	C	Runs well	168.0 tok/s	1676 ms	2.9M
Reasoning	C	Runs well	168.0 tok/s	1362 ms	2.9M
RAG	C	Runs well	168.0 tok/s	2095 ms	2.9M

Inference speed

gemma 3 12b it inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for gemma 3 12b it at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~164 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 / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	164.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	104.7	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	94.4	Fits
RTX 3090 24GB	24 GB	Q4_K_M	89.5	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	87.6	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	76.1	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	63.4	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	60.1	Fits
RTX 4070 12GB	12 GB	Q4_K_M	54.2	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	41.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	41.4	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	32.8	Fits
RTX 3060 12GB	12 GB	Q4_K_M	32.5	Tight
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	30.1	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	25.3	Fits
RTX 4060 8GB	8 GB	Q4_K_M	11.0	Too 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 it (12B params) fits at each quantization level on AMD Instinct MI350X 288GB (288.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	D36
Q3_K_S	3	5.9 GB	Low	D36
NVFP4	4	6.7 GB	Medium	D36
Q4_K_M	4	7.3 GB	Medium	D36
Q5_K_M	5	8.6 GB	High	D36
Q6_K	6	9.8 GB	High	D36
Q8_0	8	12.8 GB	Very High	D36
F16Best for your GPU	16	24.6 GB	Maximum	D37

Get started

Copy-paste commands to run gemma 3 12b it on your machine.

Run

lms load hf-maziyarpanahi--gemma-3-12b-it-gguf && lms server start

Frequently asked questions

Can AMD Instinct MI350X 288GB run gemma 3 12b it?

Yes, AMD Instinct MI350X 288GB can run gemma 3 12b it with a C grade (Runs well). Expected decode speed: 168.0 tok/s.

How much VRAM does gemma 3 12b it need?

gemma 3 12b it (12B parameters) requires approximately 38.4 GB of memory with Q4_K_M quantization.

What is the best quantization for gemma 3 12b it?

The recommended quantization for gemma 3 12b it is Q4_K_M, which balances quality and memory efficiency.

What speed will gemma 3 12b it run at on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, gemma 3 12b it achieves approximately 168.0 tokens per second decode speed with a time-to-first-token of 1152ms using Q4_K_M quantization.

Can AMD Instinct MI350X 288GB run gemma 3 12b it for coding?

For coding workloads, gemma 3 12b it on AMD Instinct MI350X 288GB receives a C grade with 168.0 tok/s and 2.9M context.

What context window can gemma 3 12b it use on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, gemma 3 12b it can safely use up to 2.9M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for AMD Instinct MI350X 288GB See all hardware for gemma 3 12b it

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