MacBook Pro M3 Max 128GB经济之选
128 GB Unified (+48)
B
This setup is broadly balanced for this model.56 tok/s 解码
~$2,499 MSRP
Can Phi 3.5 Mini 4B run on NVIDIA A100 80GB?
YES — Runs Great
B61Good
Estimated from fit model
Phi 3.5 Mini 4B needs ~17.5 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~56 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 17.5 GB, 56.0 tok/s, Runs well
17.5 GB required80.0 GB available
22% VRAM used
Fit status
Runs well
Decode
56.0 tok/s
TTFT
3457 ms
Safe context
128K
Memory
17.5 GB / 80.0 GB
Memory breakdown
Weights2.4 GB
KV Cache5.9 GB
Runtime1.2 GB
Headroom8.0 GB
See how fast it feels
See how fast it feelsPhi 3.5 Mini 4B on NVIDIA A100 80GB
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.
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 | 56.0 tok/s | 1886 ms | 128K |
| Coding | B | Runs well | 56.0 tok/s | 3457 ms | 128K |
| Agentic Coding | B | Runs well | 56.0 tok/s | 5029 ms | 128K |
| Reasoning | B | Runs well | 56.0 tok/s | 4086 ms | 128K |
| RAG | B | Runs well | 56.0 tok/s | 6286 ms | 128K |
Inference speed
Phi 3.5 Mini 4B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Phi 3.5 Mini 4B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~76 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 | 76.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 64.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 64.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 64.0 | Tight |
| RTX 3090 24GB | 24 GB | Q4_K_M | 56.0 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 56.0 | Tight |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 56.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 56.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 56.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 56.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 56.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 56.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 56.0 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 56.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 56.0 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 35.9 | Too big |
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.
Quantization options
How Phi 3.5 Mini 4B (4B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | B55 |
Q3_K_S | 3 | 2.0 GB | Low | B55 |
NVFP4 | 4 | 2.2 GB | Medium | B55 |
Q4_K_M | 4 | 2.4 GB | Medium | B55 |
Q5_K_M | 5 | 2.9 GB | High | B55 |
Q6_K | 6 | 3.3 GB | High | B55 |
Q8_0 | 8 | 4.3 GB | Very High | B55 |
F16Best for your GPU | 16 | 8.2 GB | Maximum | B56 |
Get started
Copy-paste commands to run Phi 3.5 Mini 4B on your machine.
Run
ollama run phi3.5升级选项
能流畅运行 Phi 3.5 Mini 4B 的硬件
Frequently asked questions
Can NVIDIA A100 80GB run Phi 3.5 Mini 4B?
Yes, NVIDIA A100 80GB can run Phi 3.5 Mini 4B with a B grade (Runs well). 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 17.5 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 NVIDIA A100 80GB?
On NVIDIA A100 80GB, 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 NVIDIA A100 80GB run Phi 3.5 Mini 4B for coding?
For coding workloads, Phi 3.5 Mini 4B on NVIDIA A100 80GB receives a B grade with 56.0 tok/s and 128K context.
What context window can Phi 3.5 Mini 4B use on NVIDIA A100 80GB?
On NVIDIA A100 80GB, Phi 3.5 Mini 4B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/phi-3.5-mini-4b-on-a100-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: