Mac Studio M3 Ultra 256GBOpção econômica
256 GB Unified (+128)
B
Adds memory headroom for longer context windows and future model growth.56 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
Can Gemma 3 4B run on Gaudi 3 128GB?
YES — Runs Great
B65Good
Estimated from fit model
Gemma 3 4B needs ~18.2 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~56 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) — 18.2 GB, 56.0 tok/s, Runs well
18.2 GB required128.0 GB available
14% VRAM used
Fit status
Runs well
Decode
56.0 tok/s
TTFT
3457 ms
Safe context
128K
Memory
18.2 GB / 128.0 GB
Memory breakdown
Weights2.4 GB
KV Cache2.1 GB
Runtime0.9 GB
Headroom12.8 GB
See how fast it feels
See how fast it feelsGemma 3 4B on Gaudi 3 128GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 56.0 tok/s decode · 3.5s TTFT (warm) · 140 tok/s prefill
What limits this setup
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Best improvement path
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 56.0 tok/s | 1886 ms | 128K |
| Coding | B | Runs well | 56.0 tok/s | 3457 ms | 128K |
| Agentic Coding | B | Runs well | 56.0 tok/s | 5029 ms | 128K |
| Reasoning | B | Runs well | 56.0 tok/s | 4086 ms | 128K |
| RAG | B | Runs well | 56.0 tok/s | 6286 ms | 128K |
Quantization options
How Gemma 3 4B (4B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | B59 |
Q3_K_S | 3 | 2.0 GB | Low | B59 |
NVFP4 | 4 | 2.2 GB | Medium | B59 |
Q4_K_M | 4 | 2.4 GB | Medium | B59 |
Q5_K_M | 5 | 2.9 GB | High | B59 |
Q6_K | 6 | 3.3 GB | High | B59 |
Q8_0 | 8 | 4.3 GB | Very High | B59 |
F16Best for your GPU | 16 | 8.2 GB | Maximum | B60 |
Get started
Copy-paste commands to run Gemma 3 4B on your machine.
Run
ollama run gemma3:4bOpções de upgrade
Hardware que roda bem Gemma 3 4B
Frequently asked questions
Can Gaudi 3 128GB run Gemma 3 4B?
Yes, Gaudi 3 128GB can run Gemma 3 4B with a B grade (Runs well). Expected decode speed: 56.0 tok/s.
How much VRAM does Gemma 3 4B need?
Gemma 3 4B (4B parameters) requires approximately 18.2 GB of memory with Q4_K_M quantization.
What is the best quantization for Gemma 3 4B?
The recommended quantization for Gemma 3 4B is Q4_K_M, which balances quality and memory efficiency.
What speed will Gemma 3 4B run at on Gaudi 3 128GB?
On Gaudi 3 128GB, Gemma 3 4B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q4_K_M quantization.
Can Gaudi 3 128GB run Gemma 3 4B for coding?
For coding workloads, Gemma 3 4B on Gaudi 3 128GB receives a B grade with 56.0 tok/s and 128K context.
What context window can Gemma 3 4B use on Gaudi 3 128GB?
On Gaudi 3 128GB, Gemma 3 4B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
What should I upgrade first if Gemma 3 4B feels slow on Gaudi 3 128GB?
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Would CUDA be a better path than Gaudi 3 128GB for Gemma 3 4B?
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.
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