MacBook Pro M4 Max 36GBコスパ重視
36 GB Unified (+12)
B
This setup is broadly balanced for this model.53.2 tok/sデコード
〜$2,499 MSRP
Can Phi 3 Mini 3.8B run on RTX 4090 24GB?
YES — Runs Great
B68Good
Estimated from fit model
Phi 3 Mini 3.8B needs ~11.5 GB VRAM. RTX 4090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~61 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) — 11.5 GB, 60.8 tok/s, Runs well
11.5 GB required24.0 GB available
48% VRAM used
Fit status
Runs well
Decode
60.8 tok/s
TTFT
3184 ms
Safe context
50K
Memory
11.5 GB / 24.0 GB
Memory breakdown
Weights2.3 GB
KV Cache5.9 GB
Runtime0.9 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsPhi 3 Mini 3.8B on RTX 4090 24GB
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: 60.8 tok/s decode · 3.2s TTFT (warm) · 152 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 | 60.8 tok/s | 1737 ms | 50K |
| Coding | B | Runs well | 60.8 tok/s | 3184 ms | 50K |
| Agentic Coding | A | Runs well | 60.8 tok/s | 4632 ms | 50K |
| Reasoning | B | Runs well | 60.8 tok/s | 3763 ms | 50K |
| RAG | A | Runs well | 60.8 tok/s | 5789 ms | 50K |
Quantization options
How Phi 3 Mini 3.8B (3.799999952316284B params) fits at each quantization level on RTX 4090 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.5 GB | Low | B61 |
Q3_K_S | 3 | 1.9 GB | Low | B61 |
NVFP4 | 4 | 2.1 GB | Medium | B61 |
Q4_K_M | 4 | 2.3 GB | Medium | B61 |
Q5_K_M | 5 | 2.7 GB | High | B61 |
Q6_K | 6 | 3.1 GB | High | B62 |
Q8_0 | 8 | 4.1 GB | Very High | B62 |
F16Best for your GPU | 16 | 7.8 GB | Maximum | B64 |
Get started
Copy-paste commands to run Phi 3 Mini 3.8B on your machine.
Run
ollama run phi3:miniアップグレードオプション
Phi 3 Mini 3.8Bを快適に動かすハードウェア
Frequently asked questions
Can RTX 4090 24GB run Phi 3 Mini 3.8B?
Yes, RTX 4090 24GB can run Phi 3 Mini 3.8B with a B grade (Runs well). Expected decode speed: 60.8 tok/s.
How much VRAM does Phi 3 Mini 3.8B need?
Phi 3 Mini 3.8B (3.799999952316284B parameters) requires approximately 11.5 GB of memory with Q4_K_M quantization.
What is the best quantization for Phi 3 Mini 3.8B?
The recommended quantization for Phi 3 Mini 3.8B is Q4_K_M, which balances quality and memory efficiency.
What speed will Phi 3 Mini 3.8B run at on RTX 4090 24GB?
On RTX 4090 24GB, Phi 3 Mini 3.8B achieves approximately 60.8 tokens per second decode speed with a time-to-first-token of 3184ms using Q4_K_M quantization.
Can RTX 4090 24GB run Phi 3 Mini 3.8B for coding?
For coding workloads, Phi 3 Mini 3.8B on RTX 4090 24GB receives a B grade with 60.8 tok/s and 50K context.
What context window can Phi 3 Mini 3.8B use on RTX 4090 24GB?
On RTX 4090 24GB, Phi 3 Mini 3.8B can safely use up to 50K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Embed this result▼
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<iframe src="https://willitrunai.com/embed/phi-3-mini-3.8b-on-rtx-4090-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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