MacBook Pro M3 Max 128GBBudget pick
128 GB Unified (+48)
C
This setup is broadly balanced for this model.28 tok/s decode
~$2,499 MSRP
Can gemma 2b run on NVIDIA H100 PCIe 80GB?
YES — Runs Great
C41Usable
○Estimated from fit model
gemma 2b needs ~10.7 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q4_K_M quantization, expect ~28 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 10.7 GB, 28.0 tok/s, Runs well
10.7 GB required80.0 GB available
13% VRAM used
Fit status
Runs well
Decode
28.0 tok/s
TTFT
6914 ms
Safe context
4.7M
Memory
10.7 GB / 80.0 GB
Memory breakdown
Weights1.2 GB
KV Cache0.2 GB
Runtime1.2 GB
Headroom8.0 GB
See how fast it feels
See how fast it feelsgemma 2b on NVIDIA H100 PCIe 80GB
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: 28.0 tok/s decode · 6.9s TTFT (warm) · 70 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 | 28.0 tok/s | 3771 ms | 4.7M |
| Coding | C | Runs well | 28.0 tok/s | 6914 ms | 4.7M |
| Agentic Coding | C | Runs well | 28.0 tok/s | 10057 ms | 4.7M |
| Reasoning | C | Runs well | 28.0 tok/s | 8171 ms | 4.7M |
| RAG | C | Runs well | 28.0 tok/s | 12571 ms | 4.7M |
Quantization options
How gemma 2b (2B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.8 GB | Low | D40 |
Q3_K_S | 3 | 1.0 GB | Low | D40 |
NVFP4 | 4 |
Get started
Copy-paste commands to run gemma 2b on your machine.
Run
lms load hf-google--gemma-2b && lms server startUpgrade options
Hardware that runs gemma 2b well
Mac Studio M2 Ultra 128GBBest value
128 GB Unified (+48)
C
This setup is broadly balanced for this model.28 tok/s decode
~$3,999 MSRP
128 GB Unified (+48)
C
Adds memory headroom for longer context windows and future model growth.28 tok/s decode
Adds memory headroom for longer context windows and future model growth.