Will It Run AI

Can Phi-4 Mini Reasoning 4B run on RTX 2070 Super 8GB?

YES — Runs Great

S92Excellent
Estimated from fit model

Phi-4 Mini Reasoning 4B needs ~5.8 GB VRAM. RTX 2070 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~53 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 5.8 GB, 53.2 tok/s, Runs well
5.8 GB required8.0 GB available
73% VRAM used

Fit status

Runs well

Decode

53.2 tok/s

TTFT

3639 ms

Safe context

40K

Memory

5.8 GB / 8.0 GB

Memory breakdown

Weights2.3 GB
KV Cache1.5 GB
Runtime1.2 GB
Headroom0.8 GB

See how fast it feels

See how fast it feelsPhi-4 Mini Reasoning 4B on RTX 2070 Super 8GB
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: 53.2 tok/s decode · 3.6s TTFT (warm) · 133 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatSRuns well53.2 tok/s1985 ms40K
CodingSRuns well53.2 tok/s3639 ms40K
Agentic CodingSTight fit53.2 tok/s5293 ms40K
ReasoningSRuns well53.2 tok/s4301 ms40K
RAGSTight fit53.2 tok/s6617 ms40K

Quantization options

How Phi-4 Mini Reasoning 4B (3.799999952316284B params) fits at each quantization level on RTX 2070 Super 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
1.5 GB
LowS88
Q3_K_S
3
1.9 GB
LowS89
NVFP4
4
2.1 GB
MediumS89
Q4_K_M
4
2.3 GB
MediumS89
Q5_K_M
5
2.7 GB
HighS90
Q6_K
6
3.1 GB
HighS91
Q8_0Best for your GPU
8
4.1 GB
Very HighS90
F16
16
7.8 GB
MaximumF0

Get started

Copy-paste commands to run Phi-4 Mini Reasoning 4B on your machine.

Run

ollama run phi4-mini

Your hardware

More models your RTX 2070 Super 8GB can run

ModelParamsGradeDecodeCapabilities
AlibabaQwen 3.5 4B4BS56 tok/s
AlibabaQwen 3 8B8BA33.3 tok/s

Frequently asked questions

Can RTX 2070 Super 8GB run Phi-4 Mini Reasoning 4B?

Yes, RTX 2070 Super 8GB can run Phi-4 Mini Reasoning 4B with a S grade (Runs well). Expected decode speed: 53.2 tok/s.

How much VRAM does Phi-4 Mini Reasoning 4B need?

Phi-4 Mini Reasoning 4B (3.799999952316284B parameters) requires approximately 5.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Phi-4 Mini Reasoning 4B?

The recommended quantization for Phi-4 Mini Reasoning 4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Phi-4 Mini Reasoning 4B run at on RTX 2070 Super 8GB?

On RTX 2070 Super 8GB, Phi-4 Mini Reasoning 4B achieves approximately 53.2 tokens per second decode speed with a time-to-first-token of 3639ms using Q4_K_M quantization.

Can RTX 2070 Super 8GB run Phi-4 Mini Reasoning 4B for coding?

For coding workloads, Phi-4 Mini Reasoning 4B on RTX 2070 Super 8GB receives a S grade with 53.2 tok/s and 40K context.

What context window can Phi-4 Mini Reasoning 4B use on RTX 2070 Super 8GB?

On RTX 2070 Super 8GB, Phi-4 Mini Reasoning 4B can safely use up to 40K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 2070 Super 8GBSee all hardware for Phi-4 Mini Reasoning 4B
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