MacBook Pro M4 Max 48GBGünstige Wahl
48 GB Unified (+16)546 GB/s (+34)
C
Raises estimated decode speed by about 92%.33.4 tok/s Dekodierung
Raises estimated decode speed by about 92%.
ca. $2,499 MSRP
Can gemma 3 27b it run on Radeon Pro W6800 32GB?
YES — Runs Great
C52Usable
Estimated from fit model
gemma 3 27b it needs ~23.7 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~17 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) — 23.7 GB, 17.4 tok/s, Runs well
23.7 GB required32.0 GB available
74% VRAM used
Fit status
Runs well
Decode
17.4 tok/s
TTFT
11121 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 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: 17.4 tok/s decode · 11.1s TTFT (warm) · 44 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 | 17.4 tok/s | 6066 ms | 58K |
| Coding | C | Runs well | 17.4 tok/s | 11121 ms | 58K |
| Agentic Coding | C | Tight fit | 17.4 tok/s | 16176 ms | 58K |
| Reasoning | C | Runs well | 17.4 tok/s | 13143 ms | 58K |
| RAG | C | Tight fit | 17.4 tok/s | 20220 ms | 58K |
Inference speed
gemma 3 27b it inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for gemma 3 27b it at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~73 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 | 72.9 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 46.5 | Offloads |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 42.0 | Offloads |
| RTX 3090 24GB | 24 GB | Q4_K_M | 39.8 | Offloads |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 33.8 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 33.4 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 33.4 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 28.2 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 26.7 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 21.1 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 14.6 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 13.7 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 13.4 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 4.8 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 3.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | 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 27b it (27B params) fits at each quantization level on Radeon Pro W6800 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 | 15.1 GB | Medium | C49 |
Q4_K_M | 4 | 16.5 GB | Medium | C50 |
Q5_K_M | 5 | 19.4 GB | High | C49 |
Q6_KBest for your GPU | 6 | 22.1 GB | High | C49 |
Q8_0 | 8 | 28.9 GB | Very High | F0 |
F16 | 16 | 55.4 GB | Maximum | F0 |
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-Optionen
Hardware, die gemma 3 27b it gut ausführt
Frequently asked questions
Can Radeon Pro W6800 32GB run gemma 3 27b it?
Yes, Radeon Pro W6800 32GB can run gemma 3 27b it with a C grade (Runs well). Expected decode speed: 17.4 tok/s.
How much VRAM does gemma 3 27b it need?
gemma 3 27b it (27B parameters) requires approximately 23.7 GB of memory with Q4_K_M quantization.
What is the best quantization for gemma 3 27b it?
The recommended quantization for gemma 3 27b it is Q4_K_M, which balances quality and memory efficiency.
What speed will gemma 3 27b it run at on Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, gemma 3 27b it achieves approximately 17.4 tokens per second decode speed with a time-to-first-token of 11121ms using Q4_K_M quantization.
Can Radeon Pro W6800 32GB run gemma 3 27b it for coding?
For coding workloads, gemma 3 27b it on Radeon Pro W6800 32GB receives a C grade with 17.4 tok/s and 58K context.
What context window can gemma 3 27b it use on Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, gemma 3 27b it can safely use up to 58K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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