MacBook Air M4 24GBコスパ重視
24 GB Unified (+8)
B
This setup is broadly balanced for this model.42 tok/sデコード
〜$1,099 MSRP
Can Llama 3.2 3B run on RTX 4090 Laptop 16GB?
YES — Runs Great
B60Good
Estimated from fit model
Llama 3.2 3B needs ~6.0 GB VRAM. RTX 4090 Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~48 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) — 6.0 GB, 48.0 tok/s, Runs well
6.0 GB required16.0 GB available
38% VRAM used
Fit status
Runs well
Decode
48.0 tok/s
TTFT
4033 ms
Safe context
109K
Memory
6.0 GB / 16.0 GB
Memory breakdown
Weights1.8 GB
KV Cache1.7 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsLlama 3.2 3B on RTX 4090 Laptop 16GB
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: 48.0 tok/s decode · 4.0s TTFT (warm) · 120 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 | B | Runs well | 48.0 tok/s | 2200 ms | 109K |
| Coding | B | Runs well | 48.0 tok/s | 4033 ms | 109K |
| Agentic Coding | B | Runs well | 48.0 tok/s | 5867 ms | 109K |
| Reasoning | B | Runs well | 48.0 tok/s | 4767 ms | 109K |
| RAG | B | Runs well | 48.0 tok/s | 7333 ms | 109K |
Quantization options
How Llama 3.2 3B (3B params) fits at each quantization level on RTX 4090 Laptop 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | B58 |
Q3_K_S | 3 | 1.5 GB | Low | B58 |
NVFP4 | 4 | 1.7 GB | Medium | B58 |
Q4_K_M | 4 | 1.8 GB | Medium | B59 |
Q5_K_M | 5 | 2.2 GB | High | B59 |
Q6_K | 6 | 2.5 GB | High | B59 |
Q8_0 | 8 | 3.2 GB | Very High | B60 |
F16Best for your GPU | 16 | 6.1 GB | Maximum | B62 |
Get started
Copy-paste commands to run Llama 3.2 3B on your machine.
Run
ollama run llama3.2アップグレードオプション
Llama 3.2 3Bを快適に動かすハードウェア
Frequently asked questions
Can RTX 4090 Laptop 16GB run Llama 3.2 3B?
Yes, RTX 4090 Laptop 16GB can run Llama 3.2 3B with a B grade (Runs well). Expected decode speed: 48.0 tok/s.
How much VRAM does Llama 3.2 3B need?
Llama 3.2 3B (3B parameters) requires approximately 6.0 GB of memory with Q4_K_M quantization.
What is the best quantization for Llama 3.2 3B?
The recommended quantization for Llama 3.2 3B is Q4_K_M, which balances quality and memory efficiency.
What speed will Llama 3.2 3B run at on RTX 4090 Laptop 16GB?
On RTX 4090 Laptop 16GB, Llama 3.2 3B achieves approximately 48.0 tokens per second decode speed with a time-to-first-token of 4033ms using Q4_K_M quantization.
Can RTX 4090 Laptop 16GB run Llama 3.2 3B for coding?
For coding workloads, Llama 3.2 3B on RTX 4090 Laptop 16GB receives a B grade with 48.0 tok/s and 109K context.
What context window can Llama 3.2 3B use on RTX 4090 Laptop 16GB?
On RTX 4090 Laptop 16GB, Llama 3.2 3B can safely use up to 109K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
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