Phi-4 Mini Reasoning 4B needs ~6.2 GB VRAM. RTX A2000 12GB has 12.0 GB. With Q4_K_M quantization, expect ~53 tok/s.
Operating mode
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
Fit status
Runs well
Decode
53.2 tok/s
TTFT
3639 ms
Safe context
80K
Memory
6.2 GB / 12.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs well | 53.2 tok/s | 1985 ms | 80K |
| Coding | S | Runs well | 53.2 tok/s | 3639 ms | 80K |
| Agentic Coding | S | Runs well | 53.2 tok/s | 5293 ms | 80K |
| Reasoning | S | Runs well | 53.2 tok/s | 4301 ms | 80K |
| RAG | S | Runs well | 53.2 tok/s | 6617 ms | 80K |
Inference speed
Estimated decode speed (tokens/sec) for Phi-4 Mini Reasoning 4B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~72 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 / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 72.2 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 60.8 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 60.8 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 60.8 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 53.2 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 53.2 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 53.2 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 53.2 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 53.2 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 53.2 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 53.2 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 53.2 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 53.2 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 53.2 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 53.2 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 53.2 | Fits |
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.
How Phi-4 Mini Reasoning 4B (3.799999952316284B params) fits at each quantization level on RTX A2000 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.5 GB | Low | A85 |
Q3_K_S | 3 | 1.9 GB | Low | S85 |
NVFP4 | 4 | 2.1 GB | Medium | S85 |
Q4_K_M | 4 | 2.3 GB | Medium | S86 |
Q5_K_M | 5 | 2.7 GB | High | S86 |
Q6_K | 6 | 3.1 GB | High | S87 |
Q8_0 | 8 | 4.1 GB | Very High | S88 |
F16Best for your GPU | 16 | 7.8 GB | Maximum | S89 |
Copy-paste commands to run Phi-4 Mini Reasoning 4B on your machine.
Run
ollama run phi4-miniYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 44 tok/s | |
| Qwen 3 14B | 14B | A | 16.9 tok/s | |
| Qwen 3.5 4B | 4B | S | 56 tok/s | |
| Qwen 3 8B | 8B | S | 49.5 tok/s |
Yes, RTX A2000 12GB can run Phi-4 Mini Reasoning 4B with a S grade (Runs well). Expected decode speed: 53.2 tok/s.
Phi-4 Mini Reasoning 4B (3.799999952316284B parameters) requires approximately 6.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Phi-4 Mini Reasoning 4B is Q4_K_M, which balances quality and memory efficiency.
On RTX A2000 12GB, 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.
For coding workloads, Phi-4 Mini Reasoning 4B on RTX A2000 12GB receives a S grade with 53.2 tok/s and 80K context.
On RTX A2000 12GB, Phi-4 Mini Reasoning 4B can safely use up to 80K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/phi-4-mini-reasoning-on-a2000-12gb" 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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