willitrun·ai

Can Gemma 3 4B run on NVIDIA A100 80GB?

YES — Runs Great

B65Good
Estimated from fit model

Gemma 3 4B needs ~13.7 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~56 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
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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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) 13.7 GB, 56.0 tok/s, Runs well
13.7 GB required80.0 GB available
17% VRAM used

Fit status

Runs well

Decode

56.0 tok/s

TTFT

3457 ms

Safe context

128K

Memory

13.7 GB / 80.0 GB

Memory breakdown

Weights2.4 GB
KV Cache2.1 GB
Runtime1.2 GB
Headroom8.0 GB

See how fast it feels

See how fast it feelsGemma 3 4B on NVIDIA A100 80GB
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

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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well56.0 tok/s1886 ms128K
CodingBRuns well56.0 tok/s3457 ms128K
Agentic CodingBRuns well56.0 tok/s5029 ms128K
ReasoningBRuns well56.0 tok/s4086 ms128K
RAGBRuns well56.0 tok/s6286 ms128K

Inference speed

Gemma 3 4B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 3 4B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~76 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 / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M76.0Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M64.0Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M64.0Fits
NVIDIARTX 4060 8GB
8 GBQ4_K_M57.2Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M56.0Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M56.0Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M56.0Fits
RX 7900 XTX 24GB
24 GBQ4_K_M56.0Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M56.0Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M56.0Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M56.0Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M56.0Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M56.0Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M56.0Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M56.0Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M56.0Fits

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 4B (4B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
1.6 GB
LowB61
Q3_K_S
3
2.0 GB
LowB61
NVFP4
4
2.2 GB
MediumB61
Q4_K_M
4
2.4 GB
MediumB61
Q5_K_M
5
2.9 GB
HighB61
Q6_K
6
3.3 GB
HighB61
Q8_0
8
4.3 GB
Very HighB61
F16Best for your GPU
16
8.2 GB
MaximumB61

Get started

Copy-paste commands to run Gemma 3 4B on your machine.

Run

ollama run gemma3:4b

Opciones de mejora

Hardware que ejecuta bien Gemma 3 4B

Frequently asked questions

Can NVIDIA A100 80GB run Gemma 3 4B?

Yes, NVIDIA A100 80GB 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 13.7 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 NVIDIA A100 80GB?

On NVIDIA A100 80GB, 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 NVIDIA A100 80GB run Gemma 3 4B for coding?

For coding workloads, Gemma 3 4B on NVIDIA A100 80GB receives a B grade with 56.0 tok/s and 128K context.

What context window can Gemma 3 4B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, 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.

See all results for NVIDIA A100 80GBSee all hardware for Gemma 3 4B
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