Mac Studio M3 Ultra 256GBコスパ重視
256 GB Unified (+128)
C
Adds memory headroom for longer context windows and future model growth.76.1 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$6,999 MSRP
Can gemma 3 12b it run on AMD Instinct MI300A 128GB?
YES — Runs Great
C46Usable
Estimated from fit model
gemma 3 12b it needs ~22.4 GB VRAM. AMD Instinct MI300A 128GB has 128.0 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.
Select quantization to explore
Q4_K_M (Medium quality) — 22.4 GB, 168.0 tok/s, Runs well
22.4 GB required128.0 GB available
18% VRAM used
Fit status
Runs well
Decode
168.0 tok/s
TTFT
1152 ms
Safe context
1.2M
Memory
22.4 GB / 128.0 GB
Memory breakdown
Weights7.3 GB
KV Cache1.4 GB
Runtime0.9 GB
Headroom12.8 GB
See how fast it feels
See how fast it feelsgemma 3 12b it on AMD Instinct MI300A 128GB
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 | 1.2M |
| Coding | C | Runs well | 168.0 tok/s | 1152 ms | 1.2M |
| Agentic Coding | C | Runs well | 168.0 tok/s | 1676 ms | 1.2M |
| Reasoning | C | Runs well | 168.0 tok/s | 1362 ms | 1.2M |
| RAG | C | Runs well | 168.0 tok/s | 2095 ms | 1.2M |
Quantization options
How gemma 3 12b it (12B params) fits at each quantization level on AMD Instinct MI300A 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | D38 |
Q3_K_S | 3 | 5.9 GB | Low | D38 |
NVFP4 | 4 | 6.7 GB | Medium | D38 |
Q4_K_M | 4 | 7.3 GB | Medium | D38 |
Q5_K_M | 5 | 8.6 GB | High | D38 |
Q6_K | 6 | 9.8 GB | High | D38 |
Q8_0 | 8 | 12.8 GB | Very High | D39 |
F16Best for your GPU | 16 | 24.6 GB | Maximum | D40 |
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アップグレードオプション
gemma 3 12b itを快適に動かすハードウェア
Frequently asked questions
Can AMD Instinct MI300A 128GB run gemma 3 12b it?
Yes, AMD Instinct MI300A 128GB 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 22.4 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 MI300A 128GB?
On AMD Instinct MI300A 128GB, 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 MI300A 128GB run gemma 3 12b it for coding?
For coding workloads, gemma 3 12b it on AMD Instinct MI300A 128GB receives a C grade with 168.0 tok/s and 1.2M context.
What context window can gemma 3 12b it use on AMD Instinct MI300A 128GB?
On AMD Instinct MI300A 128GB, gemma 3 12b it can safely use up to 1.2M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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