MacBook Pro M4 Max 48GBOpção econômica
48 GB Unified (+16)546 GB/s (+34)
C
This setup is broadly balanced for this model.41.4 tok/s decodificação
~$2,499 MSRP
Can gemma 3 12b it run on Radeon Pro W6800 32GB?
YES — Runs Great
C48Usable
Estimated from fit model
gemma 3 12b it needs ~12.8 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~39 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 12.8 GB, 39.2 tok/s, Runs well
12.8 GB required32.0 GB available
40% VRAM used
Fit status
Runs well
Decode
39.2 tok/s
TTFT
4943 ms
Safe context
234K
Memory
12.8 GB / 32.0 GB
Memory breakdown
Weights7.3 GB
KV Cache1.4 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsgemma 3 12b it on Radeon Pro W6800 32GB
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: 39.2 tok/s decode · 4.9s TTFT (warm) · 98 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 | 39.2 tok/s | 2696 ms | 234K |
| Coding | C | Runs well | 39.2 tok/s | 4943 ms | 234K |
| Agentic Coding | C | Runs well | 39.2 tok/s | 7189 ms | 234K |
| Reasoning | C | Runs well | 39.2 tok/s | 5841 ms | 234K |
| RAG | C | Runs well | 39.2 tok/s | 8987 ms | 234K |
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 Radeon Pro W6800 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | C44 |
Q3_K_S | 3 | 5.9 GB | Low | C44 |
NVFP4 | 4 | 6.7 GB | Medium | C44 |
Q4_K_M | 4 | 7.3 GB | Medium | C45 |
Q5_K_M | 5 | 8.6 GB | High | C45 |
Q6_K | 6 | 9.8 GB | High | C46 |
Q8_0 | 8 | 12.8 GB | Very High | C47 |
F16Best for your GPU | 16 | 24.6 GB | Maximum | C49 |
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 startOpções de upgrade
Hardware que roda bem gemma 3 12b it
Frequently asked questions
Can Radeon Pro W6800 32GB run gemma 3 12b it?
Yes, Radeon Pro W6800 32GB can run gemma 3 12b it with a C grade (Runs well). Expected decode speed: 39.2 tok/s.
How much VRAM does gemma 3 12b it need?
gemma 3 12b it (12B parameters) requires approximately 12.8 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 Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, gemma 3 12b it achieves approximately 39.2 tokens per second decode speed with a time-to-first-token of 4943ms using Q4_K_M quantization.
Can Radeon Pro W6800 32GB run gemma 3 12b it for coding?
For coding workloads, gemma 3 12b it on Radeon Pro W6800 32GB receives a C grade with 39.2 tok/s and 234K context.
What context window can gemma 3 12b it use on Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, gemma 3 12b it can safely use up to 234K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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