StepFun·Step 3·Step3p7ForConditionalGeneration

Step 3.7 Flash — Hardware Requirements & GPU Compatibility

Vision

Step 3.7 Flash is a 201.4B-parameter open language model from StepFun in the Step 3 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 132.90 GB of VRAM — see which GPUs and Macs can run it below.

140.1K downloads 419 likes 100.4K quant downloads262K context

Specifications

Publisher
StepFun
Family
Step 3
Parameters
201.4B
Architecture
Step3p7ForConditionalGeneration
Context Length
262,144 tokens
Vocabulary Size
128,896
Release Date
2026-05-23
License
Apache 2.0

Get Started

How Much VRAM Does Step 3.7 Flash Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_K3.4094.1 GB
Q3_K_S3.5096.9 GB
Q3_K_M3.90108.0 GB
Q4_04.00110.8 GB
Q4_K_M4.80132.9 GB
Q5_K_M5.70157.8 GB
Q6_K6.60182.7 GB
Q8_08.00221.5 GB

Which GPUs Can Run Step 3.7 Flash?

Q4_K_M · 132.9 GB

Step 3.7 Flash (Q4_K_M) requires 132.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 173+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.

Which Devices Can Run Step 3.7 Flash?

Q4_K_M · 132.9 GB

6 devices with unified memory can run Step 3.7 Flash, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 256GB).

Where to Download Step 3.7 Flash

Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.

Related Models

Frequently Asked Questions

How much VRAM does Step 3.7 Flash need?

Step 3.7 Flash requires 132.9 GB of VRAM at Q4_K_M, or 443 GB at BF16.

VRAM = Weights + KV Cache + Overhead

Weights = 201.4B × 4.8 bits ÷ 8 = 120.8 GB

KV Cache + Overhead 12.1 GB (at 2K context + ~0.3 GB framework)

VRAM usage by quantization

132.9 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 5090 run Step 3.7 Flash?

No — Step 3.7 Flash requires at least 60.9 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.

What's the best quantization for Step 3.7 Flash?

For Step 3.7 Flash, Q4_K_M (132.9 GB) offers the best balance of quality and VRAM usage. Q4_K_L (135.7 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 60.9 GB.

VRAM requirement by quantization

IQ2_XXS
60.9 GB
IQ3_S
94.1 GB
Q3_K_L
113.5 GB
Q4_K_M
132.9 GB
Q4_K_L
135.7 GB
BF16
443.0 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Step 3.7 Flash on a Mac?

Step 3.7 Flash requires at least 60.9 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.

Can I run Step 3.7 Flash locally?

Yes — Step 3.7 Flash can run locally on consumer hardware. At Q4_K_M quantization it needs 132.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Step 3.7 Flash?

At Q4_K_M, Step 3.7 Flash can reach ~33 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

tok/s = (bandwidth GB/s ÷ model GB) × efficiency

Example: NVIDIA B2008000 ÷ 132.9 × 0.65 = ~39 tok/s

Estimated speed at Q4_K_M (132.9 GB)

~39 tok/s
~39 tok/s
~33 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of Step 3.7 Flash?

At Q4_K_M, the download is about 120.82 GB. The full-precision BF16 version is 402.73 GB. The smallest option (IQ2_XXS) is 55.38 GB.

Which GPUs can run Step 3.7 Flash?

No single consumer GPU has enough VRAM to run Step 3.7 Flash at Q4_K_M (132.9 GB). Multi-GPU or professional hardware is required.

Which devices can run Step 3.7 Flash?

6 devices with unified memory can run Step 3.7 Flash at Q4_K_M (132.9 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.