MacBook Pro M4 Max 48GBBudget pick
48 GB Unified (+16)
C
Raises estimated decode speed by about 46%.33.4 tok/s decode
Raises estimated decode speed by about 46%.
~$2,499 MSRP
Can gemma 3 27b it run on Radeon AI PRO R9700 32GB?
YES — Runs Great
C52Usable
○Estimated from fit model
gemma 3 27b it needs ~23.7 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~23 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
Share:
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) — 23.7 GB, 22.9 tok/s, Runs well
23.7 GB required32.0 GB available
74% VRAM used
Fit status
Runs well
Decode
22.9 tok/s
TTFT
8444 ms
Safe context
58K
Memory
23.7 GB / 32.0 GB
Memory breakdown
Weights16.5 GB
KV Cache3.2 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsgemma 3 27b it on Radeon AI PRO R9700 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: 22.9 tok/s decode · 8.4s TTFT (warm) · 57 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 | 22.9 tok/s | 4606 ms | 58K |
| Coding | C | Runs well | 22.9 tok/s | 8444 ms | 58K |
| Agentic Coding | C | Tight fit | 22.9 tok/s | 12283 ms | 58K |
| Reasoning | C | Runs well | 22.9 tok/s | 9980 ms | 58K |
| RAG | C | Tight fit | 22.9 tok/s | 15353 ms | 58K |
Quantization options
How gemma 3 27b it (27B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | C46 |
Q3_K_S | 3 | 13.2 GB | Low | C48 |
NVFP4 | 4 |
Get started
Copy-paste commands to run gemma 3 27b it on your machine.
Run
lms load hf-maziyarpanahi--gemma-3-27b-it-gguf && lms server startUpgrade options
