RTX 5060 Ti 16GBコスパ重視16 GB VRAM (+6)
A
Adds memory headroom for longer context windows and future model growth.56 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$449 MSRP
Can Phi 3.5 Mini 4B run on RTX 3080 10GB?
YES — With Offload
B68Good
Estimated from fit model
Phi 3.5 Mini 4B needs ~10.5 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~56 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload
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) — 10.5 GB, 56.0 tok/s, Runs with offload (needs ~0.1 GB host RAM)
10.5 GB required10.0 GB available
105% VRAM needed
0.5 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.1 GB host RAM)
Decode
56.0 tok/s
TTFT
3457 ms
Safe context
15K
Memory
10.5 GB / 10.0 GB
Memory breakdown
Weights2.4 GB
KV Cache5.9 GB
Runtime1.2 GB
Headroom1.0 GB
See how fast it feels
See how fast it feelsPhi 3.5 Mini 4B on RTX 3080 10GB
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: 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.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 56.0 tok/s | 1886 ms | 15K |
| Coding | B | Runs with offload | 56.0 tok/s | 3457 ms | 15K |
| Agentic Coding | F | Too heavy | 56.0 tok/s | 5029 ms | 15K |
| Reasoning | B | Runs with offload | 56.0 tok/s | 4086 ms | 15K |
| RAG | F | Too heavy | 56.0 tok/s | 6286 ms | 15K |
Quantization options
How Phi 3.5 Mini 4B (4B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | B65 |
Q3_K_S | 3 | 2.0 GB | Low | B65 |
NVFP4 | 4 | 2.2 GB | Medium | B66 |
Q4_K_M | 4 | 2.4 GB | Medium | B66 |
Q5_K_M | 5 | 2.9 GB | High | B67 |
Q6_K | 6 | 3.3 GB | High | B67 |
Q8_0Best for your GPU | 8 | 4.3 GB | Very High | B69 |
F16 | 16 | 8.2 GB | Maximum | F0 |
Get started
Copy-paste commands to run Phi 3.5 Mini 4B on your machine.
Run
ollama run phi3.5アップグレードオプション
Phi 3.5 Mini 4Bを快適に動かすハードウェア
RTX 5060 Ti 16GBコスパ重視RTX 4060 Ti 16GBコスパ最良16 GB VRAM (+6)
A
Adds memory headroom for longer context windows and future model growth.56 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$499 MSRP
RTX 2000 Ada 16GBNVIDIAアップグレード16 GB VRAM (+6)
A
Adds memory headroom for longer context windows and future model growth.56 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$625 MSRP
Frequently asked questions
Can RTX 3080 10GB run Phi 3.5 Mini 4B?
Yes, RTX 3080 10GB can run Phi 3.5 Mini 4B with a B grade (Runs with offload). Expected decode speed: 56.0 tok/s.
How much VRAM does Phi 3.5 Mini 4B need?
Phi 3.5 Mini 4B (4B parameters) requires approximately 10.5 GB of memory with Q4_K_M quantization.
What is the best quantization for Phi 3.5 Mini 4B?
The recommended quantization for Phi 3.5 Mini 4B is Q4_K_M, which balances quality and memory efficiency.
What speed will Phi 3.5 Mini 4B run at on RTX 3080 10GB?
On RTX 3080 10GB, Phi 3.5 Mini 4B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q4_K_M quantization.
Can RTX 3080 10GB run Phi 3.5 Mini 4B for coding?
For coding workloads, Phi 3.5 Mini 4B on RTX 3080 10GB receives a B grade with 56.0 tok/s and 15K context.
What context window can Phi 3.5 Mini 4B use on RTX 3080 10GB?
On RTX 3080 10GB, Phi 3.5 Mini 4B can safely use up to 15K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
What should I upgrade first if Phi 3.5 Mini 4B feels slow on RTX 3080 10GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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